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

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.

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Fulltime Ai Machine Learning Generative Ai