Applied Machine Learning Engineer

Ohm • Full-time • San Francisco, California, United States • 1d ago

About Ohm

Ohm is an enterprise AI platform that helps Fortune 100 engineering teams transform how they develop, test, and validate physical products.

Cursor and Claude Code reimagined how software products are developed. Our mission is to address the next frontier: artificial intelligence that reimagines how physical products are developed.

Every complex physical product - a battery cell, an electric vehicle, a wearables device - is developed through a process that generates enormous quantities of data, almost none of which is used to its potential. This is an opportunity that AI was built to solve.

We are recruiting for exceptional scientists, engineers and operators to join our mission to reimagine how products are engineered in the next decade.

Ohm is backed by Y Combinator and other world-class investors as well as serial founders and builders.

About the role

As an Applied Machine Learning Engineer, you will take end-to-end ownership of complex machine learning and data science problems across the physical AI space. This is a high-impact role for someone with a strong technical foundation and a generalist mindset who can move from problem framing and experimentation through deployment and ongoing support.

You will 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. The role blends research depth with practical delivery, and requires someone who can align stakeholders and communicate technical decisions clearly.

Who you are

3+ years of experience in data science, machine learning, or applied AI, with evidence of delivering high-impact production ML systems
Background in engineering, physics, chemistry, or another STEM discipline connected to complex physical systems
Experience with anomaly detection, optimization, reliability-focused modelling, or related approaches in industrial settings
Experience working with sparse, noisy, or imperfect real-world data from engineering labs or other applied environments
Experience deploying and supporting ML systems in production, including cloud-based environments
Experience working with time-series, high-dimensional, or imperfect real-world datasets
Comfortable working cross-functionally with engineering, product, and customer-facing teams, and able to explain technical decisions clearly to different audiences
Experience building or experimenting with agents or autonomous systems

Wondering if you’re a good fit? We believe in investing in our teammates, and value candidates who can bring their own diversified experiences to our teams, even if you aren't a 100% skill or experience match. If some of what we've outlined about describes you, we’d love to hear from you.