Senior Machine Learning (ML) Engineer
Truveta · Greater Seattle Area · 3 wk ago
Hybrid$175k–$200k/yrFull-time
We need to output HTML fragment with sections: headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title as heading. Must not include Job ID etc. Remove boilerplate, navigation, EEO, etc. Must preserve all substantive info.
We need to parse the posting and extract sections.
The posting includes:
- Intro about Truveta (mission, vision). Could be "About the role" or maybe "About Truveta". The instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map content accordingly.
We have sections:
- "Who We Need" describing role: maybe "About the role" or "Responsibilities". Actually "Who We Need" describes what they need: Senior ML Engineer tasks: design agentic AI frameworks, etc. That's responsibilities.
- "This Opportunity" maybe more about role purpose.
- "We Are Seeking Senior ML Engineers Who ..." enumerates bullet points: design and reason with agentic AI frameworks, etc. That's responsibilities/requirements.
- "Key Qualifications" list: 5+ years experience, proficiency with agentic AI frameworks, hands-on experience fine-tuning LLMs, experience with vector DBs, strong software engineering fundamentals, B.S./M.S.
- "Why Truveta?" and "We Offer ..." includes benefits: interesting work, benefits package, comprehensive benefits with medical/dental/vision, 401K, professional development, work/life autonomy via flexible hours and PTO, generous parental leave, regular team activities, base pay $175k-$200k.
- Also mention "Certain roles eligible for additional compensation such as incentive pay and stock options." That's pay info.
- Also mention location: based out of headquarters in Greater Seattle area.
- Also mention "If you are based in California, we encourage you to read this important information for California residents linked here." That's maybe not needed.
- Also mention "Please note that all applicants must be authorized to work in the United States for any employer as we are unable to sponsor work visas or permits (e.g. F-1 OPT, H1-B) at this time." That's maybe boilerplate but could be considered minor metadata; we can drop or put in short p.
We need to remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The "We appreciate your interest..." is application instruction? Probably drop.
We need to preserve all substantive information.
Thus sections:
- About the role? Could combine "Who We Need" and "This Opportunity" into a description. Could make a section "About the role" with paragraphs summarizing role purpose.
- Responsibilities: bullet list of duties (design agentic AI frameworks, fine-tune LLMs, work with embeddings/vector stores, optimize model efficiency, collaborate, mentor, evaluation, etc.)
- Requirements / Qualifications: maybe combine "Key Qualifications" as Requirements. Could also separate Skills? But we have both Requirements and Qualifications headings allowed. We could put "Requirements" for experience and technical skills, and "Qualifications" for education. But we can also just have one section "Requirements" covering both. The instruction: prefer a few substantive sections over many thin ones. So maybe we have sections: About the role, Responsibilities, Requirements, Benefits, Pay. Schedule not mentioned, so omit.
We need to ensure we don't create sections with filler like "not specified". So only include sections with real content.
Let's extract content.
About the role: Could be a paragraph describing the role's mission: "Join Truveta’s Intelligence Platform and Applications team to engineer the next wave of healthcare AI — developing adaptive agentic systems and fine-tuned foundation models that continuously learn, reason, and accelerate real-world impact." plus "This Opportunity" paragraph about purpose. Combine.
Responsibilities: bullet list from "We Are Seeking Senior ML Engineers Who ..." plus maybe from "Who We Need". Let's list:
- Design and reason with agentic AI frameworks — experienced in building multi-agent workflows using frameworks such as LangGraph, AutoGen, or CrewAI, integrating reasoning, planning, and memory to create intelligent, goal-driven systems.
- Bring a deep understanding of LLM fundamentals — knowledgeable in transformer architectures, attention mechanisms, and tokenization principles. Understand how embeddings, context windows, and model scaling laws influence quality, cost, and performance.
- Work fluently with embeddings and vector stores — experienced in building or integrating retrieval-augmented generation (RAG) pipelines, managing vector databases (e.g., FAISS, Pinecone, Chroma, Azure AI Search, or similar), and leveraging semantic search or context injection to enhance reasoning.
- Excel at model fine-tuning — with hands-on expertise fine-tuning large language models (LLMs) or multimodal models using supervised, reinforcement, or instruction-tuning techniques (e.g., LoRA, PEFT, TRL). Skilled at optimizing model efficiency, interpretability, and inference performance.
- Think and build like engineers — grounded in strong software design principles, modular architecture, and code quality. Translate experimental ideas into robust, maintainable systems that integrate seamlessly within large-scale AI platforms.
- Collaborate across boundaries — partnering with platform, application, and product engineers to transform concepts into scalable, reliable AI solutions. Communicate clearly, share knowledge openly, and thrive in cross-functional teams.
- Demonstrate senior-level ownership — capable of setting technical direction, mentoring peers, and making pragmatic design trade-offs that balance innovation, performance, and reliability.
- Understand evaluation deeply — fluent in ML validation and measurement (Precision, Recall, Specificity, NPV, etc.), and experienced designing reward functions, evaluators, or grader models for reinforcement fine-tuning and continuous improvement.
- Adapt and learn continuously — staying ahead of evolving AI architectures, agentic frameworks, and emerging paradigms in reasoning and retrieval. Have curiosity, humility, and a bias for iteration.
- Act with purpose — applying thoughtful engineering and ethical AI principles to create systems that advance healthcare intelligence responsibly and at scale.
Also from "Who We Need": maybe include "blend ML craftsmanship with platform engineering excellence to create scalable, self-improving AI agents and models that learn from vast, diverse healthcare data and deliver meaningful insights through automation and reasoning." and "help shape the AI capabilities that make healthcare more intelligent, efficient, and equitable — driving real-world impact across research, care delivery, and patient outcomes." Could be responsibilities as well.
We'll incorporate those.
Requirements: from "Key Qualifications":
- 5+ years of experience building and deploying scalable, production-ready ML systems in a collaborative engineering environment.
- Proficiency with agentic AI frameworks (e.g., LangGraph, AutoGen, CrewAI) and understanding of interoperability protocols such as MCP and A2A.
- Hands-on experience fine-tuning and optimizing large language models (LLMs) or multimodal models, using techniques such as LoRA, PEFT, and TRL.
- Experience working with vector databases and embeddings, integrating FAISS, Pinecone, Chroma, or Azure AI Search into retrieval-augmented generation (RAG) or semantic-search pipelines.
- Strong software engineering fundamentals, with proficiency in Python and experience designing scalable systems in modern cloud environments (Azure, AWS, or GCP).
- B.S. or M.S. in Computer Science, Artificial Intelligence, or a related technical field.
Also maybe include location: "Based out of our headquarters in the Greater Seattle area." Could be a short p under About the role or separate metadata. We'll include as a short p maybe under About the role.
Benefits: from "We Offer":
- Interesting and meaningful work for every career stage
- Great benefits package
- Comprehensive benefits with strong medical, dental and vision insurance plans
- 401K plan
- Professional development & training opportunities for continuous learning
- Work/life autonomy via flexible work hours and flexible paid time off
- Generous parental leave
- Regular team activities (virtual and in-person)
Also maybe mention "We are well-funded." but that's not a benefit.
Pay: from "The base pay for this position is $175,000 to $200,000. The pay range reflects the minimum and maximum target. Pay is based on several factors including location and may vary depending on job-related knowledge, skills, and experience. Certain roles are eligible for additional compensation such as incentive pay and stock options."
We'll include that.
Schedule: not mentioned, so omit.
We must not include boilerplate like EEO, visa sponsorship, etc. We can drop those.
Now produce HTML fragment with
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title as heading. Must not include Job ID etc. Remove boilerplate, navigation, EEO, etc. Must preserve all substantive info.
We need to parse the posting and extract sections.
The posting includes:
- Intro about Truveta (mission, vision). Could be "About the role" or maybe "About Truveta". The instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map content accordingly.
We have sections:
- "Who We Need" describing role: maybe "About the role" or "Responsibilities". Actually "Who We Need" describes what they need: Senior ML Engineer tasks: design agentic AI frameworks, etc. That's responsibilities.
- "This Opportunity" maybe more about role purpose.
- "We Are Seeking Senior ML Engineers Who ..." enumerates bullet points: design and reason with agentic AI frameworks, etc. That's responsibilities/requirements.
- "Key Qualifications" list: 5+ years experience, proficiency with agentic AI frameworks, hands-on experience fine-tuning LLMs, experience with vector DBs, strong software engineering fundamentals, B.S./M.S.
- "Why Truveta?" and "We Offer ..." includes benefits: interesting work, benefits package, comprehensive benefits with medical/dental/vision, 401K, professional development, work/life autonomy via flexible hours and PTO, generous parental leave, regular team activities, base pay $175k-$200k.
- Also mention "Certain roles eligible for additional compensation such as incentive pay and stock options." That's pay info.
- Also mention location: based out of headquarters in Greater Seattle area.
- Also mention "If you are based in California, we encourage you to read this important information for California residents linked here." That's maybe not needed.
- Also mention "Please note that all applicants must be authorized to work in the United States for any employer as we are unable to sponsor work visas or permits (e.g. F-1 OPT, H1-B) at this time." That's maybe boilerplate but could be considered minor metadata; we can drop or put in short p.
We need to remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The "We appreciate your interest..." is application instruction? Probably drop.
We need to preserve all substantive information.
Thus sections:
- About the role? Could combine "Who We Need" and "This Opportunity" into a description. Could make a section "About the role" with paragraphs summarizing role purpose.
- Responsibilities: bullet list of duties (design agentic AI frameworks, fine-tune LLMs, work with embeddings/vector stores, optimize model efficiency, collaborate, mentor, evaluation, etc.)
- Requirements / Qualifications: maybe combine "Key Qualifications" as Requirements. Could also separate Skills? But we have both Requirements and Qualifications headings allowed. We could put "Requirements" for experience and technical skills, and "Qualifications" for education. But we can also just have one section "Requirements" covering both. The instruction: prefer a few substantive sections over many thin ones. So maybe we have sections: About the role, Responsibilities, Requirements, Benefits, Pay. Schedule not mentioned, so omit.
We need to ensure we don't create sections with filler like "not specified". So only include sections with real content.
Let's extract content.
About the role: Could be a paragraph describing the role's mission: "Join Truveta’s Intelligence Platform and Applications team to engineer the next wave of healthcare AI — developing adaptive agentic systems and fine-tuned foundation models that continuously learn, reason, and accelerate real-world impact." plus "This Opportunity" paragraph about purpose. Combine.
Responsibilities: bullet list from "We Are Seeking Senior ML Engineers Who ..." plus maybe from "Who We Need". Let's list:
- Design and reason with agentic AI frameworks — experienced in building multi-agent workflows using frameworks such as LangGraph, AutoGen, or CrewAI, integrating reasoning, planning, and memory to create intelligent, goal-driven systems.
- Bring a deep understanding of LLM fundamentals — knowledgeable in transformer architectures, attention mechanisms, and tokenization principles. Understand how embeddings, context windows, and model scaling laws influence quality, cost, and performance.
- Work fluently with embeddings and vector stores — experienced in building or integrating retrieval-augmented generation (RAG) pipelines, managing vector databases (e.g., FAISS, Pinecone, Chroma, Azure AI Search, or similar), and leveraging semantic search or context injection to enhance reasoning.
- Excel at model fine-tuning — with hands-on expertise fine-tuning large language models (LLMs) or multimodal models using supervised, reinforcement, or instruction-tuning techniques (e.g., LoRA, PEFT, TRL). Skilled at optimizing model efficiency, interpretability, and inference performance.
- Think and build like engineers — grounded in strong software design principles, modular architecture, and code quality. Translate experimental ideas into robust, maintainable systems that integrate seamlessly within large-scale AI platforms.
- Collaborate across boundaries — partnering with platform, application, and product engineers to transform concepts into scalable, reliable AI solutions. Communicate clearly, share knowledge openly, and thrive in cross-functional teams.
- Demonstrate senior-level ownership — capable of setting technical direction, mentoring peers, and making pragmatic design trade-offs that balance innovation, performance, and reliability.
- Understand evaluation deeply — fluent in ML validation and measurement (Precision, Recall, Specificity, NPV, etc.), and experienced designing reward functions, evaluators, or grader models for reinforcement fine-tuning and continuous improvement.
- Adapt and learn continuously — staying ahead of evolving AI architectures, agentic frameworks, and emerging paradigms in reasoning and retrieval. Have curiosity, humility, and a bias for iteration.
- Act with purpose — applying thoughtful engineering and ethical AI principles to create systems that advance healthcare intelligence responsibly and at scale.
Also from "Who We Need": maybe include "blend ML craftsmanship with platform engineering excellence to create scalable, self-improving AI agents and models that learn from vast, diverse healthcare data and deliver meaningful insights through automation and reasoning." and "help shape the AI capabilities that make healthcare more intelligent, efficient, and equitable — driving real-world impact across research, care delivery, and patient outcomes." Could be responsibilities as well.
We'll incorporate those.
Requirements: from "Key Qualifications":
- 5+ years of experience building and deploying scalable, production-ready ML systems in a collaborative engineering environment.
- Proficiency with agentic AI frameworks (e.g., LangGraph, AutoGen, CrewAI) and understanding of interoperability protocols such as MCP and A2A.
- Hands-on experience fine-tuning and optimizing large language models (LLMs) or multimodal models, using techniques such as LoRA, PEFT, and TRL.
- Experience working with vector databases and embeddings, integrating FAISS, Pinecone, Chroma, or Azure AI Search into retrieval-augmented generation (RAG) or semantic-search pipelines.
- Strong software engineering fundamentals, with proficiency in Python and experience designing scalable systems in modern cloud environments (Azure, AWS, or GCP).
- B.S. or M.S. in Computer Science, Artificial Intelligence, or a related technical field.
Also maybe include location: "Based out of our headquarters in the Greater Seattle area." Could be a short p under About the role or separate metadata. We'll include as a short p maybe under About the role.
Benefits: from "We Offer":
- Interesting and meaningful work for every career stage
- Great benefits package
- Comprehensive benefits with strong medical, dental and vision insurance plans
- 401K plan
- Professional development & training opportunities for continuous learning
- Work/life autonomy via flexible work hours and flexible paid time off
- Generous parental leave
- Regular team activities (virtual and in-person)
Also maybe mention "We are well-funded." but that's not a benefit.
Pay: from "The base pay for this position is $175,000 to $200,000. The pay range reflects the minimum and maximum target. Pay is based on several factors including location and may vary depending on job-related knowledge, skills, and experience. Certain roles are eligible for additional compensation such as incentive pay and stock options."
We'll include that.
Schedule: not mentioned, so omit.
We must not include boilerplate like EEO, visa sponsorship, etc. We can drop those.
Now produce HTML fragment with headings and - for lists,
for prose.
We need to avoid repeating job title as heading. So no
Senior Machine Learning Engineer
.
We'll output something like:
About the role
Join Truveta’s Intelligence Platform and Applications team to engineer the next wave of healthcare AI — developing adaptive agentic systems and fine-tuned foundation models that continuously learn, reason, and accelerate real-world impact. Guided by Truveta’s mission of Saving Lives with Data, you’ll help transform how health data becomes intelligence, powering breakthroughs that enable clinicians and researchers to make data-driven, proactive, and impactful decisions. This opportunity lets you use your expertise in applied AI, model optimization, and agentic intelligence to create systems that think, adapt, and improve R&D, business strategy, operations and care delivery across the healthcare ecosystem. The position is based out of Truveta’s headquarters in the Greater Seattle area.
Responsibilities
- Design and reason with agentic AI frameworks — experienced in building multi-agent workflows using frameworks such as LangGraph, AutoGen, or CrewAI, integrating reasoning, planning, and memory to create intelligent, goal-driven systems.
- Bring a deep understanding of LLM fundamentals — knowledgeable in transformer architectures, attention mechanisms, and tokenization principles; understand how embeddings, context windows, and model scaling laws influence quality, cost, and performance.
- Work fluently with embeddings and vector stores — experienced in building or integrating retrieval-augmented generation (RAG) pipelines, managing vector databases (e.g., FAISS, Pinecone, Chroma, Azure AI Search, or similar), and leveraging semantic search or context injection to enhance reasoning.
- Excel at model fine-tuning — with hands-on expertise fine-tuning large language models (LLMs) or multimodal models using supervised, reinforcement, or instruction-tuning techniques (e.g., LoRA, PEFT, TRL); skilled at optimizing model efficiency, interpretability, and inference performance.
- Think and build like engineers — grounded in strong software design principles, modular architecture, and code quality; translate experimental ideas into robust, maintainable systems that integrate seamlessly within large-scale AI platforms.
- Collaborate across boundaries — partnering with platform, application, and product engineers to transform concepts into scalable, reliable AI solutions; communicate clearly, share knowledge openly, and thrive in cross-functional teams.
- Demonstrate senior-level ownership — capable of setting technical direction, mentoring peers, and making pragmatic design trade-offs that balance innovation, performance, and reliability.
- Understand evaluation deeply — fluent in ML validation and measurement (Precision, Recall, Specificity, NPV, etc.); experienced designing reward functions, evaluators, or grader models for reinforcement fine-tuning and continuous improvement.
- Adapt and learn continuously — staying ahead of evolving AI architectures, agentic frameworks, and emerging paradigms in reasoning and retrieval; possess curiosity, humility, and a bias for iteration.
- Act with purpose — applying thoughtful engineering and ethical AI principles to create systems that advance healthcare intelligence responsibly and at scale.
- Blend ML craftsmanship with platform engineering excellence to create scalable, self-improving AI agents and models that learn from vast, diverse healthcare data and deliver meaningful insights through automation and
- for lists,
for prose. We need to avoid repeating job title as heading. So no
Senior Machine Learning Engineer
. We'll output something like:About the role
Join Truveta’s Intelligence Platform and Applications team to engineer the next wave of healthcare AI — developing adaptive agentic systems and fine-tuned foundation models that continuously learn, reason, and accelerate real-world impact. Guided by Truveta’s mission of Saving Lives with Data, you’ll help transform how health data becomes intelligence, powering breakthroughs that enable clinicians and researchers to make data-driven, proactive, and impactful decisions. This opportunity lets you use your expertise in applied AI, model optimization, and agentic intelligence to create systems that think, adapt, and improve R&D, business strategy, operations and care delivery across the healthcare ecosystem. The position is based out of Truveta’s headquarters in the Greater Seattle area.
Responsibilities
- Design and reason with agentic AI frameworks — experienced in building multi-agent workflows using frameworks such as LangGraph, AutoGen, or CrewAI, integrating reasoning, planning, and memory to create intelligent, goal-driven systems.
- Bring a deep understanding of LLM fundamentals — knowledgeable in transformer architectures, attention mechanisms, and tokenization principles; understand how embeddings, context windows, and model scaling laws influence quality, cost, and performance.
- Work fluently with embeddings and vector stores — experienced in building or integrating retrieval-augmented generation (RAG) pipelines, managing vector databases (e.g., FAISS, Pinecone, Chroma, Azure AI Search, or similar), and leveraging semantic search or context injection to enhance reasoning.
- Excel at model fine-tuning — with hands-on expertise fine-tuning large language models (LLMs) or multimodal models using supervised, reinforcement, or instruction-tuning techniques (e.g., LoRA, PEFT, TRL); skilled at optimizing model efficiency, interpretability, and inference performance.
- Think and build like engineers — grounded in strong software design principles, modular architecture, and code quality; translate experimental ideas into robust, maintainable systems that integrate seamlessly within large-scale AI platforms.
- Collaborate across boundaries — partnering with platform, application, and product engineers to transform concepts into scalable, reliable AI solutions; communicate clearly, share knowledge openly, and thrive in cross-functional teams.
- Demonstrate senior-level ownership — capable of setting technical direction, mentoring peers, and making pragmatic design trade-offs that balance innovation, performance, and reliability.
- Understand evaluation deeply — fluent in ML validation and measurement (Precision, Recall, Specificity, NPV, etc.); experienced designing reward functions, evaluators, or grader models for reinforcement fine-tuning and continuous improvement.
- Adapt and learn continuously — staying ahead of evolving AI architectures, agentic frameworks, and emerging paradigms in reasoning and retrieval; possess curiosity, humility, and a bias for iteration.
- Act with purpose — applying thoughtful engineering and ethical AI principles to create systems that advance healthcare intelligence responsibly and at scale.
- Blend ML craftsmanship with platform engineering excellence to create scalable, self-improving AI agents and models that learn from vast, diverse healthcare data and deliver meaningful insights through automation and