Senior Computational Biologist
Gordian Biotechnology · South San Francisco, CA · 2 days ago
On-siteResearchFull-time
We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Must preserve all substantive info. Remove boilerplate. Use for headings. Use
for headings. Use - for any list of duties/requirements/benefits. Use
for prose.
We need to parse the job posting.
Sections present:
- About the Role: description of mission, tasks.
- About You: qualifications, requirements.
- The Details: benefits, salary, equity, health, etc.
We need to map to headings: About the role (already present). Responsibilities: we can extract duties from About the Role. Requirements/Qualifications/Skills: from About You. Benefits: from The Details. Pay: maybe mention competitive salary and equity. Schedule: maybe not explicit; there is unlimited vacation, paid onsite lunch 3 days a week, onsite gym. No explicit schedule like full-time, hours. So we should not create Schedule section unless stated. Pay: we have "competitive salary and equity". That's specific enough? It says competitive salary and equity. That is a statement about pay. So we can include Pay section with that sentence.
Benefits: comprehensive health/dental/vision/life insurance, 401k match, paid onsite lunch 3 days a week, onsite gym, unlimited vacation, access to world-class mentors. Also office location South San Francisco (maybe location not benefit). We'll include location maybe as part of Benefits? Could be separate but not required. We'll include location in Benefits or maybe as a separate paragraph.
We must not include boilerplate like EEO etc. Not present.
We need to avoid placeholder text like "commensurate with experience". Not present.
We need to output only HTML fragment.
Let's craft:
About the role
... prose summarizing the role description ...
But we need to preserve all substantive information; we cannot summarize away detail. Must keep details. However we can keep prose as is, maybe break into paragraphs. Use for prose. For lists, use
- .
We need to decide what goes into Responsibilities vs About the role. The instruction: use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have both About the role and Responsibilities sections. The About the role heading likely contains overview; Responsibilities heading contains list of duties.
We need to extract duties from About the Role paragraph: "Your mission as a Computational Biologist at Gordian is to leverage our in vivo screens to decode how cells respond to genetic perturbation at the transcriptomic level, and translate those responses into predictions of physiologically-relevant, therapeutically-actionable outcomes in disease. Based on your work, Gordian will continue to push the frontier of extracting physiological predictions from cell states. You’ll specifically focus on cardio-renal-metabolic indications and associated tissues (heart, kidney, adipose, liver, etc.), partnering closely with our disease-area experts and experimental teams to translate screen results into clear, testable biological hypotheses. Working collaboratively with other computational members, you will guide key analytical decisions across the screen lifecycle — from experimental design and power calculations, to QC thresholds and dataset integration strategies, to the statistical frameworks used for hit calling and prioritization for validation. This includes developing and applying robust methods for modeling heterogeneous biological contexts (e.g., cell-type-specific perturbation responses, animal, batch or treatment context variability), identifying and correcting for confounders (e.g., cell cycle, ambient RNA, doublets, batch effects etc.), and selecting or designing appropriate positive and negative controls to validate effect sizes and method performance. You will communicate findings with rigorous attention to interpretability and generalizability — distinguishing robust, reproducible signal from context-specific artifacts — and ensure that QC metrics, model outputs, and troubleshooting insights flow back to the single-cell and experimental teams to iteratively improve assay design and data generation. You will also help define how we deploy agentic LLM systems to build modular, semi-automated frameworks for in-house QC, analysis, and interpretation — integrating cutting-edge computational methods (e.g., perturbation-response models, trajectory inference, representation learning) with clinically relevant genomic resources to address the specific biological question at hand. Working in collaboration with our disease experts and in vivo team, you will help connect perturbation-driven molecular changes to in vivo physiology, identifying high-confidence features that capture desirable phenotypes, and build a prioritized set of candidate targets for future screens on the basis of those predictions. Over time, you’ll help establish and iterate on selection criteria for validation that improves screening efficiency and translatability across programs. In your first month, you’ll become fluent in our in-house pipelines and workflows and independently propose analysis tasks supporting our Obesity and Heart Failure programs, starting with resource gathering and structured data exploration. By three months, you’ll make significant contributions to feature development and/or validation, including evaluating alternative analytical approaches with appropriate use of controls, statistical testing, and an emphasis on interpretability tied to mechanism-of-action validation. At six months, you’ll help define strong positive and negative controls (indicators) for these screens, partner independently with disease experts on forward screen planning, and use existing validation comparisons to assess predictive power, proposing concrete improvements to analysis methodologies along the way."
That is a lot of detail; we can keep as prose or break into list. The instruction: Use - for any list of duties/requirements/benefits. So we could turn the duties into a list. But we must preserve all substantive information; we can convert sentences into list items. That's acceptable.
Similarly, About You section contains qualifications, requirements, skills. We can split into Requirements, Qualifications, Skills headings as appropriate.
We need to decide what goes where. The posting has "About You:" with bullet-like sentences. We can treat that as Requirements and Qualifications and Skills.
Let's parse About You:
"You have a track record of high-agency success, consistently creating momentum rather than waiting for direction. Energized by an environment where exceptional teammates push each other to think clearly and work at a higher standard, you want a key role in an early-stage startup. You thrive amid the fast pace and uncertainty of screening new targets for intractable diseases of aging, bringing relentless resourcefulness to every challenge. You hold a PhD in Bioinformatics, Computational Biology, or a quantitative field, paired with deep domain expertise in disease biology, effortlessly bridging pathophysiology and computational model architecture. You have 2+ years of post-graduate experience (industry or postdoc) analyzing single-cell transcriptomic data across diverse biological contexts. Your productivity is proven by at least one peer-reviewed publication or preprint with a major co-author contribution to a computational method or adapted analysis framework applied to a disease-relevant system.Driven to build or adapt novel analysis methods rather than rely on off-the-shelf pipelines, you proactively integrate foundation models, perturbation prediction, and trajectory modeling to map cell-state transitions. You treat benchmarking as a first-class part of method development. You instinctively reach for positive and negative controls to validate new approaches, designing or sourcing ground-truth datasets when none exist. You have strong statistical foundations regarding controls, confounders, and interpretability in single-cell data. Proficient in Python and R (e.g., Scanpy, Seurat), you move fluidly between ecosystems and possess the NGS workflow familiarity (FASTQ, BAM) needed to reason about data quality, focusing on what the data reveals biologically. You look to scale your impact by standardizing recurring analyses into repeatable workflows and using agentic LLM systems to semi-automate them, freeing time for novel method development. An excellent interdisciplinary collaborator, you are self-motivated, comfortable with ambiguity, and energized by close partnership with experimental teams to ground your work in real biological questions, particularly around in vivo perturbation studies. You communicate results through honest, clear visualizations and concise summaries that resonate with both computational and experimental audiences. Valuable additional skills include experience with pooled perturbation and screening data (e.g., CRISPR or barcode-driven screens) and single-cell perturbation analysis methods. Prior work in cardio-renal-metabolic biology and relevant tissues (heart, kidney, adipose, liver) is highly desirable, alongside experience integrating public genomics resources for large-scale workflows (HPC or cloud), or working with preclinical validation models like human explants or organoids."
We can categorize:
- Requirements: PhD, 2+ years experience, publication, proficiency in Python/R, NGS workflow familiarity, strong statistical foundations, etc.
- Qualifications: maybe similar; but we can split: Requirements (hard requirements) and Qualifications (desirable). Skills: technical skills (Python, R, Scanpy, Seurat, LLM, etc.) and additional skills.
But the instruction says to prefer a few substantive sections over many thin ones. So we could combine Requirements and Qualifications into one section? But they ask to use headings for each if content exists. We could have Requirements, Qualifications, Skills sections each with content.
Let's decide:
- Requirements: PhD in Bioinformatics/Computational Biology or quantitative field; 2+ years post-graduate experience analyzing single-cell transcriptomic data; at least one peer-reviewed publication or preprint with major co-author contribution to a computational method/adapted analysis framework applied to disease-relevant system; strong statistical foundations regarding controls, confounders, interpretability in single-cell data; proficient in Python and R (Scanpy, Seurat); NGS workflow familiarity (FASTQ, BAM); ability to treat benchmarking as first-class part of method development; instinctively reach for positive/negative controls; etc.
- Qualifications: track record of high-agency success, thrives in fast pace, wants key role in early-stage startup, excellent interdisciplinary collaborator, self-motivated, comfortable with ambiguity, energized by close partnership with experimental teams, communicates results via clear visualizations and concise summaries.
- Skills: experience with pooled perturbation and screening data (CRISPR/barcode), single-cell perturbation analysis methods, prior work in cardio-renal-metabolic biology and relevant tissues desirable, experience integrating public genomics resources for large-scale workflows (HPC/cloud), working with preclinical validation models like human explants or organoids, experience with agentic LLM systems to semi-automate workflows, foundation models, perturbation prediction, trajectory modeling.
We also have "You look to scale your impact by standardizing recurring analyses into repeatable workflows and using agentic LLM systems to semi-automate them, freeing time for novel method development." That could be skill.
Now Benefits: "Gordian offers competitive salary and equity, comprehensive health/dental/vision/life insurance, a 401k match, paid onsite lunch 3 days a week, an onsite gym, unlimited vacation, and access to world-class mentors. Our office is located in South San Francisco."
Pay: competitive salary and equity.
Schedule: Not mentioned; we should not create Schedule section.
Now About the role section: we can give overview paragraph maybe summarizing mission. Then Responsibilities as list.
Let's craft HTML:
About the role
Gordian is at an exciting inflection point, having recently generated in vivo perturbation data spanning over 500 targets across different in vivo contexts (Obesity, Heart Failure), and are actively expanding this to Chronic Kidney Disease (CKD). This growing resource is being utilized in collaboration with external partners looking to leverage our platform to help build the most comprehensive knowledge-graph of disease-relevant, translatable therapeutics spanning these disease areas. Your mission as a Computational Biologist at Gordian is to leverage our in vivo screens to decode how cells respond to genetic perturbation at the transcriptomic level, and translate those responses into predictions of physiologically-relevant, therapeutically-actionable outcomes in disease. Based on your work, Gordian will continue to push the frontier of extracting physiological predictions from cell states. You’ll specifically focus on cardio-renal-metabolic indications and associated tissues (heart, kidney, adipose, liver, etc.), partnering closely with our disease-area experts and experimental teams to translate screen results into clear,
for prose. We need to parse the job posting. Sections present: - About the Role: description of mission, tasks. - About You: qualifications, requirements. - The Details: benefits, salary, equity, health, etc. We need to map to headings: About the role (already present). Responsibilities: we can extract duties from About the Role. Requirements/Qualifications/Skills: from About You. Benefits: from The Details. Pay: maybe mention competitive salary and equity. Schedule: maybe not explicit; there is unlimited vacation, paid onsite lunch 3 days a week, onsite gym. No explicit schedule like full-time, hours. So we should not create Schedule section unless stated. Pay: we have "competitive salary and equity". That's specific enough? It says competitive salary and equity. That is a statement about pay. So we can include Pay section with that sentence. Benefits: comprehensive health/dental/vision/life insurance, 401k match, paid onsite lunch 3 days a week, onsite gym, unlimited vacation, access to world-class mentors. Also office location South San Francisco (maybe location not benefit). We'll include location maybe as part of Benefits? Could be separate but not required. We'll include location in Benefits or maybe as a separate paragraph. We must not include boilerplate like EEO etc. Not present. We need to avoid placeholder text like "commensurate with experience". Not present. We need to output only HTML fragment. Let's craft:
About the role
... prose summarizing the role description ...
But we need to preserve all substantive information; we cannot summarize away detail. Must keep details. However we can keep prose as is, maybe break into paragraphs. Usefor prose. For lists, use
- .
We need to decide what goes into Responsibilities vs About the role. The instruction: use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have both About the role and Responsibilities sections. The About the role heading likely contains overview; Responsibilities heading contains list of duties. We need to extract duties from About the Role paragraph: "Your mission as a Computational Biologist at Gordian is to leverage our in vivo screens to decode how cells respond to genetic perturbation at the transcriptomic level, and translate those responses into predictions of physiologically-relevant, therapeutically-actionable outcomes in disease. Based on your work, Gordian will continue to push the frontier of extracting physiological predictions from cell states. You’ll specifically focus on cardio-renal-metabolic indications and associated tissues (heart, kidney, adipose, liver, etc.), partnering closely with our disease-area experts and experimental teams to translate screen results into clear, testable biological hypotheses. Working collaboratively with other computational members, you will guide key analytical decisions across the screen lifecycle — from experimental design and power calculations, to QC thresholds and dataset integration strategies, to the statistical frameworks used for hit calling and prioritization for validation. This includes developing and applying robust methods for modeling heterogeneous biological contexts (e.g., cell-type-specific perturbation responses, animal, batch or treatment context variability), identifying and correcting for confounders (e.g., cell cycle, ambient RNA, doublets, batch effects etc.), and selecting or designing appropriate positive and negative controls to validate effect sizes and method performance. You will communicate findings with rigorous attention to interpretability and generalizability — distinguishing robust, reproducible signal from context-specific artifacts — and ensure that QC metrics, model outputs, and troubleshooting insights flow back to the single-cell and experimental teams to iteratively improve assay design and data generation. You will also help define how we deploy agentic LLM systems to build modular, semi-automated frameworks for in-house QC, analysis, and interpretation — integrating cutting-edge computational methods (e.g., perturbation-response models, trajectory inference, representation learning) with clinically relevant genomic resources to address the specific biological question at hand. Working in collaboration with our disease experts and in vivo team, you will help connect perturbation-driven molecular changes to in vivo physiology, identifying high-confidence features that capture desirable phenotypes, and build a prioritized set of candidate targets for future screens on the basis of those predictions. Over time, you’ll help establish and iterate on selection criteria for validation that improves screening efficiency and translatability across programs. In your first month, you’ll become fluent in our in-house pipelines and workflows and independently propose analysis tasks supporting our Obesity and Heart Failure programs, starting with resource gathering and structured data exploration. By three months, you’ll make significant contributions to feature development and/or validation, including evaluating alternative analytical approaches with appropriate use of controls, statistical testing, and an emphasis on interpretability tied to mechanism-of-action validation. At six months, you’ll help define strong positive and negative controls (indicators) for these screens, partner independently with disease experts on forward screen planning, and use existing validation comparisons to assess predictive power, proposing concrete improvements to analysis methodologies along the way." That is a lot of detail; we can keep as prose or break into list. The instruction: Use
- for any list of duties/requirements/benefits. So we could turn the duties into a list. But we must preserve all substantive information; we can convert sentences into list items. That's acceptable.
Similarly, About You section contains qualifications, requirements, skills. We can split into Requirements, Qualifications, Skills headings as appropriate.
We need to decide what goes where. The posting has "About You:" with bullet-like sentences. We can treat that as Requirements and Qualifications and Skills.
Let's parse About You:
"You have a track record of high-agency success, consistently creating momentum rather than waiting for direction. Energized by an environment where exceptional teammates push each other to think clearly and work at a higher standard, you want a key role in an early-stage startup. You thrive amid the fast pace and uncertainty of screening new targets for intractable diseases of aging, bringing relentless resourcefulness to every challenge. You hold a PhD in Bioinformatics, Computational Biology, or a quantitative field, paired with deep domain expertise in disease biology, effortlessly bridging pathophysiology and computational model architecture. You have 2+ years of post-graduate experience (industry or postdoc) analyzing single-cell transcriptomic data across diverse biological contexts. Your productivity is proven by at least one peer-reviewed publication or preprint with a major co-author contribution to a computational method or adapted analysis framework applied to a disease-relevant system.Driven to build or adapt novel analysis methods rather than rely on off-the-shelf pipelines, you proactively integrate foundation models, perturbation prediction, and trajectory modeling to map cell-state transitions. You treat benchmarking as a first-class part of method development. You instinctively reach for positive and negative controls to validate new approaches, designing or sourcing ground-truth datasets when none exist. You have strong statistical foundations regarding controls, confounders, and interpretability in single-cell data. Proficient in Python and R (e.g., Scanpy, Seurat), you move fluidly between ecosystems and possess the NGS workflow familiarity (FASTQ, BAM) needed to reason about data quality, focusing on what the data reveals biologically. You look to scale your impact by standardizing recurring analyses into repeatable workflows and using agentic LLM systems to semi-automate them, freeing time for novel method development. An excellent interdisciplinary collaborator, you are self-motivated, comfortable with ambiguity, and energized by close partnership with experimental teams to ground your work in real biological questions, particularly around in vivo perturbation studies. You communicate results through honest, clear visualizations and concise summaries that resonate with both computational and experimental audiences. Valuable additional skills include experience with pooled perturbation and screening data (e.g., CRISPR or barcode-driven screens) and single-cell perturbation analysis methods. Prior work in cardio-renal-metabolic biology and relevant tissues (heart, kidney, adipose, liver) is highly desirable, alongside experience integrating public genomics resources for large-scale workflows (HPC or cloud), or working with preclinical validation models like human explants or organoids."
We can categorize:
- Requirements: PhD, 2+ years experience, publication, proficiency in Python/R, NGS workflow familiarity, strong statistical foundations, etc.
- Qualifications: maybe similar; but we can split: Requirements (hard requirements) and Qualifications (desirable). Skills: technical skills (Python, R, Scanpy, Seurat, LLM, etc.) and additional skills.
But the instruction says to prefer a few substantive sections over many thin ones. So we could combine Requirements and Qualifications into one section? But they ask to use headings for each if content exists. We could have Requirements, Qualifications, Skills sections each with content.
Let's decide:
- Requirements: PhD in Bioinformatics/Computational Biology or quantitative field; 2+ years post-graduate experience analyzing single-cell transcriptomic data; at least one peer-reviewed publication or preprint with major co-author contribution to a computational method/adapted analysis framework applied to disease-relevant system; strong statistical foundations regarding controls, confounders, interpretability in single-cell data; proficient in Python and R (Scanpy, Seurat); NGS workflow familiarity (FASTQ, BAM); ability to treat benchmarking as first-class part of method development; instinctively reach for positive/negative controls; etc.
- Qualifications: track record of high-agency success, thrives in fast pace, wants key role in early-stage startup, excellent interdisciplinary collaborator, self-motivated, comfortable with ambiguity, energized by close partnership with experimental teams, communicates results via clear visualizations and concise summaries.
- Skills: experience with pooled perturbation and screening data (CRISPR/barcode), single-cell perturbation analysis methods, prior work in cardio-renal-metabolic biology and relevant tissues desirable, experience integrating public genomics resources for large-scale workflows (HPC/cloud), working with preclinical validation models like human explants or organoids, experience with agentic LLM systems to semi-automate workflows, foundation models, perturbation prediction, trajectory modeling.
We also have "You look to scale your impact by standardizing recurring analyses into repeatable workflows and using agentic LLM systems to semi-automate them, freeing time for novel method development." That could be skill.
Now Benefits: "Gordian offers competitive salary and equity, comprehensive health/dental/vision/life insurance, a 401k match, paid onsite lunch 3 days a week, an onsite gym, unlimited vacation, and access to world-class mentors. Our office is located in South San Francisco."
Pay: competitive salary and equity.
Schedule: Not mentioned; we should not create Schedule section.
Now About the role section: we can give overview paragraph maybe summarizing mission. Then Responsibilities as list.
Let's craft HTML:
About the role
Gordian is at an exciting inflection point, having recently generated in vivo perturbation data spanning over 500 targets across different in vivo contexts (Obesity, Heart Failure), and are actively expanding this to Chronic Kidney Disease (CKD). This growing resource is being utilized in collaboration with external partners looking to leverage our platform to help build the most comprehensive knowledge-graph of disease-relevant, translatable therapeutics spanning these disease areas. Your mission as a Computational Biologist at Gordian is to leverage our in vivo screens to decode how cells respond to genetic perturbation at the transcriptomic level, and translate those responses into predictions of physiologically-relevant, therapeutically-actionable outcomes in disease. Based on your work, Gordian will continue to push the frontier of extracting physiological predictions from cell states. You’ll specifically focus on cardio-renal-metabolic indications and associated tissues (heart, kidney, adipose, liver, etc.), partnering closely with our disease-area experts and experimental teams to translate screen results into clear,
- for any list of duties/requirements/benefits. So we could turn the duties into a list. But we must preserve all substantive information; we can convert sentences into list items. That's acceptable.
Similarly, About You section contains qualifications, requirements, skills. We can split into Requirements, Qualifications, Skills headings as appropriate.
We need to decide what goes where. The posting has "About You:" with bullet-like sentences. We can treat that as Requirements and Qualifications and Skills.
Let's parse About You:
"You have a track record of high-agency success, consistently creating momentum rather than waiting for direction. Energized by an environment where exceptional teammates push each other to think clearly and work at a higher standard, you want a key role in an early-stage startup. You thrive amid the fast pace and uncertainty of screening new targets for intractable diseases of aging, bringing relentless resourcefulness to every challenge. You hold a PhD in Bioinformatics, Computational Biology, or a quantitative field, paired with deep domain expertise in disease biology, effortlessly bridging pathophysiology and computational model architecture. You have 2+ years of post-graduate experience (industry or postdoc) analyzing single-cell transcriptomic data across diverse biological contexts. Your productivity is proven by at least one peer-reviewed publication or preprint with a major co-author contribution to a computational method or adapted analysis framework applied to a disease-relevant system.Driven to build or adapt novel analysis methods rather than rely on off-the-shelf pipelines, you proactively integrate foundation models, perturbation prediction, and trajectory modeling to map cell-state transitions. You treat benchmarking as a first-class part of method development. You instinctively reach for positive and negative controls to validate new approaches, designing or sourcing ground-truth datasets when none exist. You have strong statistical foundations regarding controls, confounders, and interpretability in single-cell data. Proficient in Python and R (e.g., Scanpy, Seurat), you move fluidly between ecosystems and possess the NGS workflow familiarity (FASTQ, BAM) needed to reason about data quality, focusing on what the data reveals biologically. You look to scale your impact by standardizing recurring analyses into repeatable workflows and using agentic LLM systems to semi-automate them, freeing time for novel method development. An excellent interdisciplinary collaborator, you are self-motivated, comfortable with ambiguity, and energized by close partnership with experimental teams to ground your work in real biological questions, particularly around in vivo perturbation studies. You communicate results through honest, clear visualizations and concise summaries that resonate with both computational and experimental audiences. Valuable additional skills include experience with pooled perturbation and screening data (e.g., CRISPR or barcode-driven screens) and single-cell perturbation analysis methods. Prior work in cardio-renal-metabolic biology and relevant tissues (heart, kidney, adipose, liver) is highly desirable, alongside experience integrating public genomics resources for large-scale workflows (HPC or cloud), or working with preclinical validation models like human explants or organoids."
We can categorize:
- Requirements: PhD, 2+ years experience, publication, proficiency in Python/R, NGS workflow familiarity, strong statistical foundations, etc.
- Qualifications: maybe similar; but we can split: Requirements (hard requirements) and Qualifications (desirable). Skills: technical skills (Python, R, Scanpy, Seurat, LLM, etc.) and additional skills.
But the instruction says to prefer a few substantive sections over many thin ones. So we could combine Requirements and Qualifications into one section? But they ask to use headings for each if content exists. We could have Requirements, Qualifications, Skills sections each with content.
Let's decide:
- Requirements: PhD in Bioinformatics/Computational Biology or quantitative field; 2+ years post-graduate experience analyzing single-cell transcriptomic data; at least one peer-reviewed publication or preprint with major co-author contribution to a computational method/adapted analysis framework applied to disease-relevant system; strong statistical foundations regarding controls, confounders, interpretability in single-cell data; proficient in Python and R (Scanpy, Seurat); NGS workflow familiarity (FASTQ, BAM); ability to treat benchmarking as first-class part of method development; instinctively reach for positive/negative controls; etc.
- Qualifications: track record of high-agency success, thrives in fast pace, wants key role in early-stage startup, excellent interdisciplinary collaborator, self-motivated, comfortable with ambiguity, energized by close partnership with experimental teams, communicates results via clear visualizations and concise summaries.
- Skills: experience with pooled perturbation and screening data (CRISPR/barcode), single-cell perturbation analysis methods, prior work in cardio-renal-metabolic biology and relevant tissues desirable, experience integrating public genomics resources for large-scale workflows (HPC/cloud), working with preclinical validation models like human explants or organoids, experience with agentic LLM systems to semi-automate workflows, foundation models, perturbation prediction, trajectory modeling.
We also have "You look to scale your impact by standardizing recurring analyses into repeatable workflows and using agentic LLM systems to semi-automate them, freeing time for novel method development." That could be skill.
Now Benefits: "Gordian offers competitive salary and equity, comprehensive health/dental/vision/life insurance, a 401k match, paid onsite lunch 3 days a week, an onsite gym, unlimited vacation, and access to world-class mentors. Our office is located in South San Francisco."
Pay: competitive salary and equity.
Schedule: Not mentioned; we should not create Schedule section.
Now About the role section: we can give overview paragraph maybe summarizing mission. Then Responsibilities as list.
Let's craft HTML: