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Staff Machine Learning Engineer

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

#li-remote

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Average salary estimate

$275000 / YEARLY (est.)
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$250000K
$300000K

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What You Should Know About Staff Machine Learning Engineer, Ambience Healthcare

At Ambience, we're on a mission to transform healthcare with advanced AI technologies, and we're looking for a Staff Machine Learning Engineer to join our innovative team in San Francisco. In this pivotal role, you will take charge of significant portions of our AI tech stack, collaborating directly with clinical teams to design, prototype, and deploy cutting-edge machine learning systems aimed at building the AI doctor of the future. Your expertise will help shape the AI strategy, leveraging your 5+ years of experience in machine learning and software engineering. If you're proficient in Python and deep learning frameworks like PyTorch or TensorFlow, this could be the perfect opportunity for you. You'll create rigorous evaluation pipelines, mentor upcoming talents, and drive initiatives that will lay the groundwork for our long-term vision. At Ambience, you won’t just code; you will help change the landscape of healthcare by empowering clinicians with AI tools that enhance their capabilities and improve patient care. Your work will directly impact the efficiency of healthcare systems and ultimately, the lives of countless patients. This hybrid role provides you with the flexibility to work from our San Francisco office three times a week, surrounded by a team of passionate and mission-driven professionals. If you’re ready to take your career to new heights and contribute to something truly meaningful, we invite you to explore this opportunity with Ambience, where innovation meets purpose.

Frequently Asked Questions (FAQs) for Staff Machine Learning Engineer Role at Ambience Healthcare
What responsibilities does a Staff Machine Learning Engineer at Ambience have?

The Staff Machine Learning Engineer at Ambience will own substantial parts of the AI tech stack, collaborating with clinical experts to deploy machine learning systems that advance healthcare. Responsibilities include co-designing AI strategy, prototyping new medical foundation models, developing evaluation pipelines, and building a high-performing team.

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What qualifications are required for the Staff Machine Learning Engineer position at Ambience?

Candidates for the Staff Machine Learning Engineer role at Ambience should have over 5 years of experience in technical roles, including at least 2 years in machine learning. Mastery of ML fundamentals, strong software engineering capabilities in Python and frameworks like PyTorch or TensorFlow, and exceptional communication and team-building skills are essential.

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How does Ambience foster collaboration between machine learning engineers and clinical teams?

Ambience encourages direct collaboration between Staff Machine Learning Engineers and clinical teams to enhance the development of AI tools. This role emphasizes joint efforts to set technical direction, gather requirements, and integrate user feedback, thereby ensuring the AI systems created are truly beneficial to healthcare providers.

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What is the work environment like at Ambience for the Staff Machine Learning Engineer?

The Staff Machine Learning Engineer role at Ambience is hybrid, with team members working from the San Francisco office three times a week. This setup promotes in-person collaboration, enabling dynamic interactions with a diverse and mission-driven team focused on improving healthcare through AI.

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What opportunities for personal and professional development does Ambience provide for its Staff Machine Learning Engineers?

Ambience offers a dedicated budget for personal development, access to mentors and advisors, and an in-house executive coach. As a Staff Machine Learning Engineer, you'll have ownership of your success and opportunities to shape the company’s growth while continuously enhancing your skills.

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Common Interview Questions for Staff Machine Learning Engineer
Can you explain your experience with prototyping machine learning models?

In answering this, focus on specific projects where you have designed and prototyped models. Highlight any innovative approaches you took, the challenges faced, and how your work impacted the project's success, ensuring to discuss tools and frameworks used.

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How do you approach designing an AI strategy in a clinical setting?

Discuss your methodology for collaborating with clinical teams, assessing their needs, and integrating those insights into the AI strategy. Emphasize the importance of aligning technical goals with patient care improvements and how you've implemented those in past roles.

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What techniques do you use to evaluate the performance of machine learning models?

Share your familiarity with various evaluation metrics. Discuss a structured process for evaluating model performance, including how you use offline and online evaluations, feedback loops, and metrics to refine models iteratively.

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Describe your experience mentoring or building a tech team.

Focus on your leadership style and experience. Give examples of how you have effectively mentored junior engineers, fostered team growth, and contributed to building a collaborative and innovative culture within your past teams.

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What do you believe are the key considerations when working with large language models?

Articulate your understanding of LLMs, including their benefits and challenges. Discuss specific considerations you’ve encountered, such as model architecture choices, training, evaluation methods, and application in healthcare-related tasks.

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Can you discuss a time when you faced significant uncertainty or ambiguity in a project?

Share a specific example demonstrating your adaptability and resilience. Discuss how you navigated the situation, engaged stakeholders, and made informed decisions that led to a successful outcome.

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How do you ensure cross-functional collaboration with non-technical teams?

Explain your approach to communication, emphasizing your ability to build rapport with non-technical stakeholders. Provide examples of successful collaborations where you gathered requirements and delivered solutions that met those needs.

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What are some best practices for experiment design in machine learning?

Discuss the importance of defining clear hypotheses, choosing appropriate metrics, and ensuring reproducibility. Provide examples from your experience that illustrate how you implemented best practices in your machine learning experiments.

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What frameworks or tools do you prefer for building and deploying machine learning models?

Mention your favorite tools, such as PyTorch, TensorFlow, or JAX, and explain why you prefer them. Discuss any specific projects where these frameworks helped you achieve your goals effectively.

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How do you keep current with advancements in machine learning and AI?

Share your strategies for staying informed, such as following leading AI researchers, attending conferences, and reading relevant publications. Emphasize your commitment to continuous learning and how you apply new knowledge to your work.

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Ambience Healthcare’s mission is to supercharge healthcare providers with AI superpowers.

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EMPLOYMENT TYPE
Full-time, hybrid
DATE POSTED
December 5, 2024

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