Applied Machine Learning Engineer
Hybrid (San Francisco, CA)Full-timePosted 2mo ago
About the role
Ohm is an enterprise AI platform for Fortune 100 engineering teams. The role focuses on end-to-end machine learning and data science problems in the physical AI space, partnering with founders, head of engineering, and customer teams to deploy production ML solutions.
Responsibilities
- Own machine learning problems end to end - from problem framing and experimentation through deployment and ongoing support in live customer environments.
- Work closely with engineering, product, and client-facing teams to turn machine learning methods into production-ready solutions that can be trusted in real customer environments.
- Build and support models running in production, where their output drives real engineering decisions.
- Communicate technical decisions clearly to technical and non-technical audiences, and align stakeholders around them.
Qualifications
- Applied ML experience. 3+ years in data science, machine learning, or applied AI, with evidence of delivering high-impact production ML systems.
- STEM foundation. A background in engineering, physics, chemistry, or another STEM discipline connected to complex physical systems.
- Real-world data fluency. Experience working with sparse, noisy, time-series, or high-dimensional datasets from engineering labs or other applied environments.
- Cross-functional communication. Comfortable working with engineering, product, and customer-facing teams, and able to explain technical decisions clearly to different audiences.
- AI-native. You use AI coding tools as your primary workflow, and have thought carefully about where AI can accelerate applied ML work.
Skills
Required:Applied machine learningData scienceEngineering/physical sciencesCommunication
Nice to have:AI-native tooling
Benefits
- Benefits are not explicitly listed in the provided description.
Last updated Oct 7, 2026. Interested? Apply on the company site.
About Ohm
AI agents for battery teams
Ohm deploys AI agents across battery teams around the world
data-engineeringai
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