About Us:

Ambience is developing the most capable AI systems for healthcare and medicine. As healthcare costs soar to 17.3% of US GDP and a projected shortage of 100,000 physicians within the next decade, the need for AI is critical. Our frontline healthcare workers are overwhelmed, with only 27% of the average clinician's day spent on direct patient care.

Our vision is to equip every healthcare worker with an advanced AI co-pilot. We believe that people, augmented by AI, will enable us to deliver higher quality healthcare at a lower cost, improving the experience for everyone involved.

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

Join us in the endeavor of accelerating the path to safe & useful clinical super intelligence by becoming part of our community of problem solvers, technologists, clinicians, and innovators.

The Role

As a Staff Machine Learning Engineer, you will own of significant swaths of our AI tech stack, driving decisions that prioritize the most impactful investments and ensuring the highest standards of experimental rigor. You will take drive direct collaboration with our clinical team to prototype and deploy cutting-edge machine learning systems, leading the way toward building an AI doctor.

Within your first six months, you will be responsible for laying the groundwork for long-term success; establishing AI strategy, developing tooling, and training the next generation of Ambience’s foundation models tailored to medicine. As a technical leader, you will oversee every phase of critical AI initiatives—from system architecture to writing and deploying production-ready code. Our engineering roles are hybrid, with team members working from our SF office three times a week.

What You’ll Do

• Co-design Our AI Strategy: Work with our clinical team to set our technical direction, identify the most promising large language model (LLM) techniques to explore, and determine the most impactful areas to invest in across annotation, evaluation, training, monitoring, and feedback loop pipelines.

• Advance Our Medical Foundation Models: Prototype new model architectures, training regimes, and post-processing techniques to train the next generation of Ambience's foundational models tuned specifically for medicine, pushing the boundaries of what's possible in healthcare AI.

• Develop Rigorous Evaluation Pipelines: Collaborate with Clinical and Engineering teams to design and implement state-of-the-art offline and online model evaluation infrastructure. Identify the right metrics to track and leverage end-user feedback to build a continuous learning loop.

• Contribute to Building the Team: Leverage your deep network and prior interviewing experience to help build a world-class research team, fostering a culture of innovation and excellence.

Who You Are

• Experienced Professional: You have 5+ years in a technical role as a software engineer or machine learning engineer/researcher, including 2+ years specifically in machine learning roles.

• Mastery of ML Fundamentals: You can clearly articulate how neural networks and transformers work, understand best practices for experiment design, know the pros and cons of different evaluation metrics, and can distill insights from complex research papers for non-technical audiences.

• Strong Software Engineering Skills: Fluent in Python and deep learning frameworks like PyTorch, TensorFlow, or JAX. Capable of setting up end-to-end training pipelines, debugging model issues, visualizing metrics in Jupyter notebooks, refactoring code for distributed training, and deploying models to production.

• Effective Cross-Functional Collaborator: Able to build rapport with non-technical stakeholders (e.g., clinical, product) and gather requirements needed to develop ML systems.

• Exceptional Communicator: As a thought leader who sets strategic direction and crafts numerous PRDs and system designs, you excel in both written and verbal communication.

• Team Builder: You're excited to lead and mentor an ambitious, mission-aligned team that delivers breakthrough product experiences.

• Adaptable and Resilient: You thrive in environments with high levels of complexity, uncertainty, and ambiguity.

• In-Person Collaboration: Willingness to work in-person from our SF office three times a week.

Nice-to-Haves

• Experience with LLMs: Familiarity with the latest techniques for training, evaluating, or deploying LLMs in applied settings or at the foundation model layer. You have good intuitions about when to use techniques like prompting, chaining, retrieval-augmented generation (RAG), behavioral cloning, RLHF, etc.

• Hiring Experience: Experience as an interviewer or hiring manager for engineering or machine learning roles.

• Mission-Driven: Passion for healthcare or other mission-oriented sectors like education, climate tech, government tech, or non-profits.

Compensation

$250,000 - $300,000, with the addition of significant equity.

Are you outside of the range? We encourage you to still apply; we take an individualized approach to ensure that compensation accounts for all of the life factors that matter for each candidate.

Being at Ambience:

• An opportunity to work with cutting edge AI technology, on a product that dramatically improves the quality of life for healthcare providers and the quality of care they can provide to their patients

• Dedicated budget for personal development, including access to world class mentors, advisors, and an in-house executive coach

• Work alongside a world-class, diverse team that is deeply mission aligned

• Ownership over your success and the ability to significantly impact the growth of our company

• Competitive salary and equity compensation with benefits including health, dental, and vision coverage, paid maternity/paternity leave, quarterly retreats, unlimited PTO, and a 401(k) plan

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