MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Machine Learning Engineer collaborating with researchers on neural network quality and applying standard techniques to technical problems. Maintaining awareness of current trends and managing concurrent experiments.
Responsibilities
- Collaborate with researchers to perform research operations using existing infrastructure
- Use judgment in complex scenarios and help apply standard techniques to a wide variety of technical problems
- Characterize neural network quality, failure modes, and edge cases based on research data
- Maintain awareness of current trends in relevant areas of research and technology
- Coordinate with researchers and accurately convey status of experiments
- Manage a large number of concurrent experiments and make accurate time estimates with respect to deadlines
- Review experimental results and suggest theoretical or process improvements for future iterations
- Write technical reports indicating qualitative and quantitative results to external parties
Requirements
- A sense of practical optimism: not all experiments are successful, but the ones that are more than make up for it!
- Proficiency in Python
- Proven ability to thrive in fast-paced environment
- Ability to communicate complex technical concepts to colleagues and a variety of audience
- Introspection, thoughtfulness, and detail-orientation
- Master’s or Ph.D. in a related field and/or 5+ years of experience in a directly related field (a plus)
- Experience reading research papers and implementing the techniques therein (a plus)
- Computer vision and deep learning experience (a plus)
Benefits
- Competitive health insurance options
- 401K plan management
- Free lunch and fully-stocked kitchen in our South Bay office
- Additional perks: monthly wellness stipend, office set up allowance, company retreats, and more to come as we scale
- The opportunity to work on one of the most interesting, impactful problems of the decade
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