Staff Research Engineer

About Us:

Ambience is building the most capable AI systems for healthcare and medicine. With healthcare costs at 17.3% of US GDP and a projected shortage of 100,000 physicians within the next decade, the need for AI innovation is urgent. Frontline healthcare workers today spend just 27% of their day on direct patient care.

Our vision is to advance healthcare by empowering clinicians with safe, intelligent AI agents that improve quality, reduce costs, and enhance both patient and provider experiences.

Headquartered in San Francisco, we’ve raised $100M from leading investors including Kleiner Perkins, the OpenAI Startup Fund, Andreessen Horowitz, Optum Ventures, Human Capital, and Martin Ventures. We collaborate with top AI experts such as Jeff Dean, Richard Socher, Pieter Abbeel, and AIX Ventures.

Join us in accelerating the path to safe and useful clinical superintelligence alongside a world-class team of technologists, clinicians, and innovators.

The Role:

As a Staff Research Engineer at Ambience, 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.

Our engineering roles are hybrid — working onsite at our San Francisco office three days per week.

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.

Who You Are:

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.

Deep Expertise in LLM Fine-Tuning

• 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.

Evaluation and Data Obsessed

• 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.

Collaborative and Curious

• 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.

Effective Interdisciplinary Collaborator

• Able to work alongside clinicians, product managers, and fellow engineers
• Strong communicator who can distill complex ML concepts for diverse audiences.

Mission-Aligned

• 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

Nice-to-haves

• 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