Computational Scientist (Biology)
for headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections where there is real specific content. We must not repeat job title. No metadata like Job ID. Remove boilerplate. The provided text includes sections: "About Axiom and the role:" (we can treat as About the role). Then "What are we looking for:" (maybe Qualifications or Requirements). Then "What you will do:" (Responsibilities). Then "We’re especially interested in:" (Skills). Then "What we look for:" (maybe more Qualifications/Requirements). We need to combine overlapping content.
We need to preserve all substantive info, not invent. No filler sections like Pay/Benefits/Schedule if not present. The posting does not mention pay, benefits, schedule. So we should not include those headings.
We need to use for each section heading. Use - for any list of duties/requirements/benefits. Use
for prose.
We need to decide sections: About the role, Responsibilities, Requirements (or Qualifications), Skills. Possibly combine Requirements and Qualifications into one section? The instruction: use headings e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can include multiple if needed. But we should prefer few substantive sections over many thin ones. So we could have About the role, Responsibilities, Requirements/Skills combined? But we need to follow instruction: use those headings when appropriate. Could have About the role, Responsibilities, Requirements, Skills. That's four sections.
We need to extract content.
About the role: The first paragraph about Axiom building closed-loop scientific AI system, etc. That's prose.
Responsibilities: "What you will do:" bullet list items.
Requirements: maybe "What are we looking for:" and "What we look for:" combined? Those are about desired traits, experience, etc. Could be Requirements.
Skills: "We’re especially interested in:" list of specific technical skills.
We need to ensure we don't repeat content. Let's parse.
Original text:
About Axiom and the role:Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.
What are we looking for:We want to hire people who inspire us and level up the entire team. They should be high energy, high agency, and have great taste for what matters. They should have a relentless “observe, orient, decide, act” loop, and be constantly identifying what needs to happen and getting it done. They need to be technically excellent and obsessive masters of their craft, as well as having a great curiosity which will keep them at the frontier of tech and help them interface between AI, engineering, product, biology, chemistry, and business. They could work in big tech, but it won’t satisfy them. They want to go on an adventure which will be brutally challenging, and to share in the rewards and satisfaction at its end.
What you will do:Drive the exploration and analysis of the world's largest multimodal toxicity datasetsTransform noisy, high-dimensional biological data into clear, actionable insights by identifying critical signalsConduct detailed analyses on machine learning models to understand what the model has learned and its failure modesDevelop new methods at the intersection of computation and biology to understand biological mechanisms.Investigate a diverse range of biological systems—including liver, heart, kidney, and immune tissues—to model the relationship between chemical structures and biological responses.Collaborate with ML researchers to enhance model accuracyDevelop new high content imaging assays capable of generating datasets for predictive modeling of human tissue toxicityCreate quality control processes for massive high content imaging datasets.Help leading drug discovery teams build a deep understanding of drugs’ toxicity profiles
We’re especially interested in:Python, Pandas, Numpy, Jupyter notebooksSolid mathematical and statistical background (curve fitting, dimensionality reduction, statistics, machine learning, imaging algorithms)Cellprofiler, Cellpose, imaging data analysis, quality controlHigh content imaging, cell painting, assay developmentHigh throughput screening, automation
What we look for:Strong coding ability for a biologist - you may straddle biology and computer science/machine learning, or you may have taught yourself to code to help with your analysisA talent for identifying signals in biological data and for separating signal from noiseA drive to push beyond conventional methods to develop new and better computational algorithms and approaches for biologyPassionate about high-content imaging and about building, understanding, and evaluating biological datasetsDriven by a relentless pursuit of scientific excellence— impeccable data quality, meticulous analyses, and well-grounded conclusionsAbility to bridge wet lab protocols, computational analysis, and machine learning algorithmsObsessive technical curiosity for all things in the wetlab, drylab, and drug discovery.Strong research and publication track recordStrong ability to communicate and inspire with science
We need to turn these into HTML.
We'll create:
About the role
... (the paragraph)
Responsibilities
- Drive the exploration and analysis of the world's largest multimodal toxicity datasets
- Transform noisy, high-dimensional biological data into clear, actionable insights by identifying critical signals
- Conduct detailed analyses on machine learning models to understand what the model has learned and its failure modes
- Develop new methods at the intersection of computation and biology to understand biological mechanisms
- Investigate a diverse range of biological systems—including liver, heart, kidney, and immune tissues—to model the relationship between chemical structures and biological responses
- Collaborate with ML researchers to enhance model accuracy
- Develop new high content imaging assays capable of generating datasets for predictive modeling of human tissue toxicity
- Create quality control processes for massive high content imaging datasets
- Help leading drug discovery teams build a deep understanding of drugs’ toxicity profiles
Requirements
We want to hire people who inspire us and level up the entire team. They should be high energy, high agency, and have great taste for what matters. They should have a relentless “observe, orient, decide, act” loop, and be constantly identifying what needs to happen and getting it done. They need to be technically excellent and obsessive masters of their craft, as well as having a great curiosity which will keep them at the frontier of tech and help them interface between AI, engineering, product, biology, chemistry, and business. They could work in big tech, but it won’t satisfy them. They want to go on an adventure which will be brutally challenging, and to share in the rewards and satisfaction at its end.
Maybe also include the "What we look for:" content as part of Requirements or separate Qualifications. Since we have both "What are we looking for:" and "What we look for:" we could combine them into Requirements section. But we already used that for the first block. The second block (What we look for:) includes more specific traits: strong coding ability, talent for identifying signals, drive to push beyond conventional methods, passionate about high-content imaging, driven by pursuit of scientific excellence, ability to bridge wet lab protocols, obsessive technical curiosity, strong research and publication track record, strong ability to communicate and inspire with science.
We could either add another Qualifications
with that list as or . Since it's a list of qualities, we could make a
. The instruction: use - for any list of duties/requirements/benefits. So we can treat that as a list of requirements.
Thus we could have:
Requirements
- Strong coding ability for a biologist - you may straddle biology and computer science/machine learning, or you may have taught yourself to code to help with your analysis
- A talent for identifying signals in biological data and for separating signal from noise
- A drive to push beyond conventional methods to develop new and better computational algorithms and approaches for biology
- Passionate about high-content imaging and about building, understanding, and evaluating biological datasets
- Driven by a relentless pursuit of scientific excellence— impeccable data quality, meticulous analyses, and well-grounded conclusions
- Ability to bridge wet lab protocols, computational analysis, and machine learning algorithms
- Obsessive technical curiosity for all things in the wetlab, drylab, and drug discovery
- Strong research and publication track record
- Strong ability to communicate and inspire with science
But we already used Requirements for the earlier paragraph. Maybe we should rename first block as "Qualifications" and second as "Requirements"? However the instruction says we can use those headings. Let's think: "What are we looking for:" is more about personal attributes (high energy, high agency, etc.) - could be Qualifications. "What we look for:" is more about specific skills/experience - could be Requirements. But we also have a separate Skills section for technical skills.
Thus we could have:
About the role
...
Responsibilities
...
Qualifications
We want to hire people who inspire us and level up the entire team. They should be high energy, high agency, and have great taste for what matters. They should have a relentless “observe, orient, decide, act” loop, and be constantly identifying what needs to happen and getting it done. They need to be technically excellent and obsessive masters of their craft, as well as having a great curiosity which will keep them at the frontier of tech and help them interface between AI, engineering, product, biology, chemistry, and business. They could work in big tech, but it won’t satisfy them. They want to go on an adventure which will be brutally challenging, and to share in the rewards and satisfaction at its end.
Requirements
... (the "What we look for:" list)
Skills
... (the "We’re especially interested in:" list)
That seems reasonable and follows instruction to prefer few substantive sections over many thin ones, but we have 5 sections. Could we combine Qualifications and Requirements into one? But they are distinct content; still okay.
We must not include Pay/Benefits/Schedule.
Now we need to ensure we preserve all substantive information. Let's verify we didn't miss any.
From About Axiom and the role: we captured the whole paragraph.
From What are we looking for: we captured as Qualifications paragraph.
From What you will do: we captured as Responsibilities list.
From We’re especially interested in: we captured as Skills list.
From What we look for: we captured as Requirements list.
Check for any missing details: The "What are we looking for:" also includes "They should be high energy, high agency, and have great taste for what matters." etc. We included that.
The "What we look for:" includes "Strong coding ability for a biologist - you may straddle biology and computer science/machine learning, or
- for any list of duties/requirements/benefits. Use
for prose. We need to decide sections: About the role, Responsibilities, Requirements (or Qualifications), Skills. Possibly combine Requirements and Qualifications into one section? The instruction: use headings e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can include multiple if needed. But we should prefer few substantive sections over many thin ones. So we could have About the role, Responsibilities, Requirements/Skills combined? But we need to follow instruction: use those headings when appropriate. Could have About the role, Responsibilities, Requirements, Skills. That's four sections. We need to extract content. About the role: The first paragraph about Axiom building closed-loop scientific AI system, etc. That's prose. Responsibilities: "What you will do:" bullet list items. Requirements: maybe "What are we looking for:" and "What we look for:" combined? Those are about desired traits, experience, etc. Could be Requirements. Skills: "We’re especially interested in:" list of specific technical skills. We need to ensure we don't repeat content. Let's parse. Original text: About Axiom and the role:Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans. What are we looking for:We want to hire people who inspire us and level up the entire team. They should be high energy, high agency, and have great taste for what matters. They should have a relentless “observe, orient, decide, act” loop, and be constantly identifying what needs to happen and getting it done. They need to be technically excellent and obsessive masters of their craft, as well as having a great curiosity which will keep them at the frontier of tech and help them interface between AI, engineering, product, biology, chemistry, and business. They could work in big tech, but it won’t satisfy them. They want to go on an adventure which will be brutally challenging, and to share in the rewards and satisfaction at its end. What you will do:Drive the exploration and analysis of the world's largest multimodal toxicity datasetsTransform noisy, high-dimensional biological data into clear, actionable insights by identifying critical signalsConduct detailed analyses on machine learning models to understand what the model has learned and its failure modesDevelop new methods at the intersection of computation and biology to understand biological mechanisms.Investigate a diverse range of biological systems—including liver, heart, kidney, and immune tissues—to model the relationship between chemical structures and biological responses.Collaborate with ML researchers to enhance model accuracyDevelop new high content imaging assays capable of generating datasets for predictive modeling of human tissue toxicityCreate quality control processes for massive high content imaging datasets.Help leading drug discovery teams build a deep understanding of drugs’ toxicity profiles We’re especially interested in:Python, Pandas, Numpy, Jupyter notebooksSolid mathematical and statistical background (curve fitting, dimensionality reduction, statistics, machine learning, imaging algorithms)Cellprofiler, Cellpose, imaging data analysis, quality controlHigh content imaging, cell painting, assay developmentHigh throughput screening, automation What we look for:Strong coding ability for a biologist - you may straddle biology and computer science/machine learning, or you may have taught yourself to code to help with your analysisA talent for identifying signals in biological data and for separating signal from noiseA drive to push beyond conventional methods to develop new and better computational algorithms and approaches for biologyPassionate about high-content imaging and about building, understanding, and evaluating biological datasetsDriven by a relentless pursuit of scientific excellence— impeccable data quality, meticulous analyses, and well-grounded conclusionsAbility to bridge wet lab protocols, computational analysis, and machine learning algorithmsObsessive technical curiosity for all things in the wetlab, drylab, and drug discovery.Strong research and publication track recordStrong ability to communicate and inspire with science We need to turn these into HTML. We'll create:
About the role
... (the paragraph)
Responsibilities
- Drive the exploration and analysis of the world's largest multimodal toxicity datasets
- Transform noisy, high-dimensional biological data into clear, actionable insights by identifying critical signals
- Conduct detailed analyses on machine learning models to understand what the model has learned and its failure modes
- Develop new methods at the intersection of computation and biology to understand biological mechanisms
- Investigate a diverse range of biological systems—including liver, heart, kidney, and immune tissues—to model the relationship between chemical structures and biological responses
- Collaborate with ML researchers to enhance model accuracy
- Develop new high content imaging assays capable of generating datasets for predictive modeling of human tissue toxicity
- Create quality control processes for massive high content imaging datasets
- Help leading drug discovery teams build a deep understanding of drugs’ toxicity profiles
Requirements
We want to hire people who inspire us and level up the entire team. They should be high energy, high agency, and have great taste for what matters. They should have a relentless “observe, orient, decide, act” loop, and be constantly identifying what needs to happen and getting it done. They need to be technically excellent and obsessive masters of their craft, as well as having a great curiosity which will keep them at the frontier of tech and help them interface between AI, engineering, product, biology, chemistry, and business. They could work in big tech, but it won’t satisfy them. They want to go on an adventure which will be brutally challenging, and to share in the rewards and satisfaction at its end.
Maybe also include the "What we look for:" content as part of Requirements or separate Qualifications. Since we have both "What are we looking for:" and "What we look for:" we could combine them into Requirements section. But we already used that for the first block. The second block (What we look for:) includes more specific traits: strong coding ability, talent for identifying signals, drive to push beyond conventional methods, passionate about high-content imaging, driven by pursuit of scientific excellence, ability to bridge wet lab protocols, obsessive technical curiosity, strong research and publication track record, strong ability to communicate and inspire with science. We could either add anotherQualifications
with that list as- or
- for any list of duties/requirements/benefits. So we can treat that as a list of requirements.
Thus we could have:
Requirements
- Strong coding ability for a biologist - you may straddle biology and computer science/machine learning, or you may have taught yourself to code to help with your analysis
- A talent for identifying signals in biological data and for separating signal from noise
- A drive to push beyond conventional methods to develop new and better computational algorithms and approaches for biology
- Passionate about high-content imaging and about building, understanding, and evaluating biological datasets
- Driven by a relentless pursuit of scientific excellence— impeccable data quality, meticulous analyses, and well-grounded conclusions
- Ability to bridge wet lab protocols, computational analysis, and machine learning algorithms
- Obsessive technical curiosity for all things in the wetlab, drylab, and drug discovery
- Strong research and publication track record
- Strong ability to communicate and inspire with science
About the role
...
Responsibilities
- ...
Qualifications
We want to hire people who inspire us and level up the entire team. They should be high energy, high agency, and have great taste for what matters. They should have a relentless “observe, orient, decide, act” loop, and be constantly identifying what needs to happen and getting it done. They need to be technically excellent and obsessive masters of their craft, as well as having a great curiosity which will keep them at the frontier of tech and help them interface between AI, engineering, product, biology, chemistry, and business. They could work in big tech, but it won’t satisfy them. They want to go on an adventure which will be brutally challenging, and to share in the rewards and satisfaction at its end.
Requirements
- ... (the "What we look for:" list)
Skills
- ... (the "We’re especially interested in:" list)
. Since it's a list of qualities, we could make a
- . The instruction: use