Machine Learning Engineer, Ambience Healthcare

• 5+ years in a production ML or research engineering role
• Direct experience with LLMs (applied or research)
• Strong proficiency in Python and a deep learning framework (PyTorch, TensorFlow, or JAX)
• Willingness to work on-site in San Francisco at least 3 days/week
• Mastery of modern neural networks (e.g., transformers) and advanced evaluation techniques
• Familiar with the latest literature and able to translate research into applied experiments
• Proficient in Python and a major deep learning framework (PyTorch preferred)
• Comfortable with modern MLOps, CI/CD for ML, and containerization for deployment
• Experience building and maintaining large, high-quality datasets
• Skilled at leveraging user feedback to refine and iterate on data pipelines
• Able to work alongside clinicians, product managers, and fellow engineers
• Strong communicator who can simplify complex ML concepts for diverse audiences
• Driven by improving clinician and patient outcomes through AI
• Thrives in a fast-paced, early-stage environment; takes extreme ownership of deliverables

Desirable

• As a Machine Learning Engineer at Ambience, you will help guide our AI team’s technical direction, identifying the most impactful opportunities to build, refine, and deploy machine learning systems that enhance clinical outcomes
• This role is both strategic and hands-on, requiring close collaboration with clinicians, product managers, and other engineers to turn cutting-edge research into production-grade solutions
• Architect our AI Roadmap: Identify high-priority projects and shape our AI strategy, ensuring we balance near-term product needs with longer-term research opportunities
• Scale Model Evaluation: Collaborate with clinical and engineering teams to establish robust evaluation pipelines, track performance metrics, and integrate real-world feedback
• Explore Frontier Model Development: Experiment with the latest model architectures and optimization techniques, focusing on real-world improvements in accuracy, reliability, and efficiency
• Evolve Data Pipelines: Architect systems for capturing and curating training data, prioritizing the most challenging and valuable clinical use cases to continuously improve models
• Stay at the Cutting Edge: Distill insights from recent AI research—particularly in LLMs, NLP, and speech recognition—and champion experiments that keep Ambience on the forefront of innovation