Responsibilities
- 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.
Basic qualifications
- 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
Preferred qualifications
- 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)
- The base pay for this position is $155,000 to $175,000. 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.
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Ai Machine Learning Generative Ai