Research Engineer

Orin Labs; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 280,000; Full time

Job summary

Orin Labs in San Francisco is hiring researchers to build agents, train models, and design benchmarks that work in deployment. You will own research end-to-end, bridging long-horizon tasks, memory, planning, and reliable operation with real test data, translating insights into production systems.

The role emphasizes shipping impactful work over credentials, with PhD or post-doc candidates encouraged and a focus on continual learning, test-time adaptation, and architectures that retain context

Qualifications

• PhD or post-doc in ML/AI with demonstrated research impact.
• Experience shipping research to production systems.
• Strong ability to connect research to real deployment data.

Responsibilities

• Own long-horizon learning benchmarks and evaluation suites.
• Build agents, train models, and design environments and evals.
• Set research direction and translate findings into production.

Skills

Continual learning

Test-time training

Long-context architectures

Long-horizon tasks

Education

PhD or Post‑doc research

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.