About the roleWe are looking for an engineer who can turn new modeling and agent ideas into working systems. You will build evaluations, data and inference workflows, backend services, internal tools, and product-facing experiments while working directly with the founders and researchers.
Your job is to shorten the path from hypothesis to reliable software and bring failures observed in real products and real data back into the next experiment. As the technology develops, you will help turn model outputs into useful forecasting, scenario-exploration, and decision-support capabilities.
This is not a narrow ML infrastructure role, a pure research role, or a management job. You will own reusable technical capabilities, partnering with the data engineer on shared data foundations and the product engineer on the applications that expose those capabilities to users.
What you'll own- Turn research and agent ideas into working prototypes, evaluations, internal tools, and customer-ready capabilities.
- Build reusable systems for forecasting, covariate search, model-input preparation, inference, evaluation, and decision support.
- Work across model code, APIs, data contracts, workflows, backend services, and lightweight product experiments.
- Investigate failures in models, agents, and their surrounding systems, and turn findings into better evaluations and more dependable behavior.
- Improve developer experience, observability, reliability, security, and deployment speed where they unlock more research and product progress.
- Work with the CTO, Chief Scientist, and CEO on technical direction and the capabilities needed for customer deployments.
- Help set the engineering standards for the team, including testing, technical documentation, and sound decisions about what to build or reuse.
What we're looking for- A track record of taking ambiguous technical problems from an initial idea through implementation and reliable production use.
- Strong software engineering skills across backend services, APIs, data workflows, testing, and debugging.
- Hands-on experience taking ML models or agent-based systems into production, including evaluation, integration, failure analysis, and ongoing operation.
- The ability to work in research and model code while also building maintainable services and developer tooling.
- Technical judgment about when to experiment, when to invest in reusable infrastructure, and when a simpler solution is sufficient.
- Clear communication and the ability to collaborate directly with researchers, product engineers, and founders.
First 90 days- 30 days: Understand the modeling stack, data infrastructure, and how our technology is used in Ask The Grid; identify one high-impact system to own.
- 60 days: Ship a working capability that converts research, data, or customer learning into repeatable software.
- 90 days: Own a durable technical capability and materially reduce repeated founder engineering work through reliable systems, tooling, or workflows.
Why joinYou will help determine how the company's research becomes usable technology. The work spans experiments and production systems, with direct access to the people developing the models and the people using the resulting products. Your technical decisions will shape the engineering team we build around them.
Location and compensationLocation: New York City or Boston/Cambridge. This is a hybrid role - we expect in-person collaboration 3 days per week in person.
Salary range: $225,000-$280,000 USD.
Equity: meaningful early-company equity.
