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About the Role
We are looking for an engineer to transform novel modeling and agent concepts into functional systems. You will develop evaluations, data and inference workflows, backend services, internal tools, and product experiments, collaborating closely with founders and researchers. Your primary goal is to accelerate the transition from hypothesis to robust software and integrate observed failures back into future experiments. As the technology evolves, you will contribute to developing forecasting, scenario exploration, and decision-support capabilities from model outputs.
Responsibilities
- 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.
Requirements
- 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.
Location
- New York City
- Boston/Cambridge
Work Type
- Hybrid
Salary/Compensations
- $225,000–$280,000 USD
Benefits
- Meaningful early-company equity
About the Company
- Parisi Labs is building foundational world models for physical industry, developing models that learn how complex physical systems behave and reuse that understanding across forecasts, scenarios, and operational decisions.
- Energy is our first proving ground. We combine historical and live data with operational context, bringing together machine learning research, data infrastructure, and software engineering to turn advances in modeling into useful technology for energy operators.
- We are a small technical team working directly with the founders on our core models, systems, and products.