Staff ML Research Engineer
2 weeks ago
Staff ML Research Engineer
Staff+ machine learning research engineer with experience fine-tuning and post-training LLMs. We do not require healthcare experience and we value fast growing startup experience. You will push the boundaries of generative AI by translating cutting-edge research into working prototypes and experimental platforms. You'll work closely with fellow researchers, engineers, and product leads to explore novel architectures, fine-tuning methods, evaluation paradigms, and data strategies-helping to define what's possible with frontier AI models in healthcare and medicine.
Job Description
What You'll Do:
- Prototype and Advance LLM Systems: Build and benchmark LLM-based systems and agents using open-source and proprietary models. Rapidly prototype new capabilities through fine-tuning, adapters, and reinforcement learning approaches.
- Drive Research-First Experimentation: Translate recent academic papers into reproducible experiments, focusing on fine-tuning (e.g., LoRA, QLoRA, DPO), model alignment, and hallucination mitigation techniques. Design clear experiment plans and share findings across the team.
- Build and Evolve Evaluation Pipelines: Define evaluation methodologies using human-in-the-loop feedback, synthetic benchmarks, and task-specific metrics. Implement continuous evaluation pipelines to track regressions and breakthroughs.
- Shape Data and Training Strategy: Curate datasets via synthetic generation, targeted scraping, and annotation pipelines. Establish practices for discovering failure cases and improving model robustness over time.
- Contribute to a Research-Driven Culture: Write research papers, internal memos, and blog posts. Foster a culture of experimentation, documentation, and knowledge-sharing across research and engineering teams.
Research Fluent
- Skilled at interpreting and replicating results from cutting-edge machine learning research.
- Experienced in designing experiments, running ablation studies, and ensuring reproducibility.
- 4+ years of experience in machine learning research, experimental AI, or applied AI engineering.
- Demonstrated ability to replicate, extend, or publish original research.
- Hands-on experience fine-tuning large language models and optimizing prompt and embedding strategies.
- Proficient with Python and deep learning frameworks such as PyTorch, JAX, and Hugging Face Transformers.
- Comfortable with distributed training environments and large-scale model experimentation.
- Deep understanding of dataset curation, filtering, and alignment with evaluation goals.
- Familiar with human annotation pipelines, ranking models (e.g., RM, RLAIF), and interpretability techniques.
- Experienced in building evaluation frameworks tied to real-world task performance.
- Thrives in research-driven environments with a commitment to experimentation, documentation, and cross-functional learning.
- Excited to prototype, present findings, and build at the frontier of AI advancement.
- Able to work alongside clinicians, product managers, and fellow engineers
- Strong communicator who can distill complex ML concepts for diverse audiences.
- Passion for healthcare or other mission-driven industries (e.g., education, climate tech)
- Thrives in a fast-paced, early-stage environment; takes extreme ownership of deliverables
- Open-source contributions to ML libraries, datasets, or benchmarks
- Experience working in AI research labs, frontier model companies, or early-stage AI startups
- Background in RLHF, alignment research, or AI safety
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