Forward Deployed Engineer, Battery Engineering

Location: Hybrid (San Francisco, CA) Employment Type: Full-time

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: bringing that same shift to how physical products are developed. Every complex physical product - a battery cell, an electric vehicle, a wearable device - is developed through a process that generates enormous quantities of data, almost none of which is used to its potential. This is precisely the kind of problem modern AI is suited to solve. We are recruiting 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.

The opportunity

Your role is to develop and ship the technical capabilities that will deliver the greatest measurable ROI for Ohm's customers. You bring the domain knowledge to identify what to build, the engineering capability to build it, and the commercial instinct to prioritize the changes that compound customer value fastest. You will work directly with the founder, the Head of Engineering, and the battery engineering teams at Ohm's customer accounts. You will write code, deploy models, and own the scientific validity of production systems.

What you'll do

• Build and own battery models at the core of Ohm's platform - deployed in live customer pipelines where their output drives real engineering decisions.
• Work directly with customers to understand their hardest technical challenges and then develop platform capabilities that close the gap.
• Build the domain knowledge infrastructure that makes Ohm's agentic features scientifically credible at the level of the most technically demanding customers.
• Bring frontier battery validation workflows into the platform before customers ask, translated into production capabilities with measurable customer impact.

What you'll need

• Battery engineering background. An MS, PhD or equivalent depth in battery science, electrochemistry, or a closely related field.
• Modeling fluency. Strength in at least one of physics-based modeling or data-driven methods (classical ML, Bayesian methods, Gaussian processes), with working knowledge of the other and a clear intuition for which problems call for which. You are fluent in Python.
• AI-forward. You use AI coding tools as your primary workflow. You have thought carefully about where AI can accelerate battery testing and validation, and can give examples of how you are using AI to do so.

What will set you apart

• You have built architectures that combine physics-based and data-driven models, and understand the trade-offs from experience.
• You have shipped models or analysis in live production systems.
• You have a point of view on where the field's standard approaches to battery development, testing, and validation are incomplete, and where modeling belongs in the development process.

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

Application Instructions

Candidates can apply by uploading their resume or autofilling from their resume on the application form provided on the webpage. Required fields include First Name, Last Name, Email, LinkedIn Profile URL, excitement about joining the team, and authorization to work in US or UK.

Relevant Skills Mentioned

• Battery science
• Electrochemistry
• Physics-based modeling
• Data-driven modeling methods including:
• Classical Machine Learning (ML)
• Bayesian methods
• Gaussian processes
• Python programming
• AI coding tools usage
• Model deployment in production systems
• Building hybrid architectures combining physics-based and data-driven models
• Battery testing and validation workflows
• Domain knowledge infrastructure development