Machine Learning Engineer - LLMs & Generative AI

Truveta-posted 28 days ago
$155,000 - $175,000/Yr
Full-time • Mid Level
Hybrid • Seattle, WA
251-500 employees
Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services
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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 Truveta is building a diverse and inclusive team to tackle complex health and technical challenges. We are seeking enthusiastic new graduates who are problem-solvers, collaborative teammates, and eager to make a difference in healthcare. If you're excited about purposeful work, joining a mission-driven team, and launching your career in a supportive environment, Truveta could be the perfect place to start. This Opportunity We are looking for a Machine Learning Engineer with a deep background in generative AI, large language models (LLMs), and reinforcement learning techniques to build the next generation of AI systems for trustworthy healthcare. This role offers a unique opportunity to build foundational models trained on vast clinical data and drive innovation in a highly meaningful domain.

  • Lead the development, training, and deployment of large language and multimodal foundation models tailored to clinical and biomedical domains.
  • apply and refine state-of-the-art techniques such as supervised fine-tuning (SFT), reinforcement learning-based methods (e.g., RLHF, RLVR), parameter-efficient fine-tuning (PEFT), prompt tuning, and retrieval-augmented generation (RAG).
  • Collaborate cross-functionally with researchers, clinicians, and engineers to design ML-driven solutions that improve healthcare delivery and outcomes.
  • Build scalable infrastructure for distributed training of large models (TPU/GPU clusters, multi-node orchestration).
  • Design and evaluate models for robustness, bias mitigation, factual consistency, and explainability in healthcare contexts.
  • Stay current with the latest research in generative AI and contribute back to the community through publications and open-source initiatives.
  • 6+ years of experience in software engineering or machine learning (3+ years with a PhD).
  • Experience designing and training LLMs or large-scale generative models (e.g., GPT, PaLM, LLaMA, Claude, Gemma).
  • Deep expertise in NLP, sequence modeling, and transformer architectures.
  • Proficient in Python and ML libraries such as PyTorch or TensorFlow; strong engineering skills in building scalable ML pipelines.
  • Experience with RL-based fine-tuning (e.g., Reinforcement Learning from Human Feedback) and evaluation of generative systems.
  • Proven ability to lead technical projects and collaborate across teams.
  • Bachelor's degree in Computer Science, Engineering, or a related technical field
  • PhD or equivalent experience in Machine Learning, NLP, AI, or a related field.
  • Experience in healthcare, biomedical informatics, or clinical data modeling.
  • Familiarity with multi-modal foundation models (e.g., text-image, text-structured data) and cross-modal alignment techniques.
  • Hands-on experience with vector databases, semantic search, and retrieval-based generation
  • Publications in top-tier ML conferences (NeurIPS, ICML, ACL, EMNLP, ICLR, etc.).
  • Experience building trustworthy AI systems and applying model interpretability, fairness, and safety frameworks.
  • 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 as soon as we are able)
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