Senior Machine Learning Engineer

Truveta•Seattle, WA

1d•$165,000 - $190,000•Hybrid

About The Position

Senior Machine Learning Engineer Truveta is the world’s first health provider led data platform with a vision of Saving Lives with Data. Our mission is to enable researchers to find cures faster, empower every clinician to be an expert, and help families make the most informed decisions about their care. Achieving Truveta’ s ambitious vision requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our company values . This position is based out of our headquarters in the Greater Seattle area. #LI-hybrid Who We Need 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. We are seeking Senior Machine Learning Engineers who thrive at the intersection of machine learning, applied AI, and large-scale engineering. You’ll 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. You’ll help shape the AI capabilities that make healthcare more intelligent, efficient, and equitable — driving real-world impact across research, care delivery, and patient outcomes. This Opportunity Patients, doctors, and medical researchers deserve to benefit from the same large-scale technological innovations and artifical intelligence that have transformed productivity, creativity, and communication. As part of the Intelligence & Application Platform team, you’ll 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. If you’re seeking a sense of purpose and are motivated to advance how AI supports learning, decision-making, and discovery in healthcare, you’ll find this an inspiring place to grow. You’ll work in an ambitious, fast-paced and collaborative environment where every contribution helps make healthcare more connected, intelligent, and impactful.

Requirements

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.

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. You 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. You can 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. You 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. You 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.