Research Engineer

Orin Labs, Inc.; San Francisco (CA); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Orin Labs, Inc. in San Francisco is building AI agents that can plan, learn, remember, and operate at megaproject scales—power plants, factories, data centers, and the infrastructure that underpins civilization.

We seek researchers who have built agents, trained models, and created benchmarks or evals, and who can translate what they ship into deployment-ready production work. PhDs/post-docs in continual learning and long-context AI are encouraged, but practical impact matters most.

Qualifications

• Built agents, trained models, and created benchmarks, environments, or evals.
• Can connect research to long-term deployment goals.
• Experience aligning research with production systems.

Responsibilities

• Develop agents and training pipelines for long-horizon tasks.
• Design benchmarks, environments, and evals to measure progress.
• Translate research into production-ready components at scale.

Skills

Agent development

Model training

Benchmarking

Education

PhD in ML/AI

Tools

PyTorch

TensorFlow

Experiment frameworks

Job description

We are making AI that can build megaprojects: power plants, factories, data centers, and the physical infrastructure civilization runs on. Today this work requires large teams coordinating over long timescales. We are making agents that do that coordination directly.

What you'll do

The research that matters most to us comes straight out of deployment: getting agents to multi-task, learn, remember, and operate reliably over long stretches of time. You'll own that work end to end, building agents, training models, and designing the benchmarks, environments, and evals that tell us whether it's working. You'll set research direction with our founders, grounded in real deployment and test data, and translate what we learn into production.

What you'll work on

Agents forget what they've committed to and what users teach them, so we're building long-horizon learning benchmarks (starting from our Horizon work) for memory once history overflows the context window. Real work also comes in bursts. Give an agent three tasks at once and it drops most of them, so we need environments for multi-tasking, stakeholder management, requirements-gathering, and scaled caution around irreversible actions. Underneath it all sit open problems we have to crack: temporal reasoning and planning, and a verifiable, evidence-backed model of the state of the world.

What we're looking for

You've built agents, trained models, and created benchmarks, environments, or evals (ideally all of the above), and you can connect your own work to the long-term goal of getting AI to build in the real world. We're especially excited about current PhDs and post-docs in continual learning, test-time training, long-context architectures, and long-horizon tasks, but we care more about what you've shipped than your credentials.

Orin Labs is an equal opportunity employer. We don't discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, marital status, military or veteran status, or any other legally protected characteristic.