Research Engineer

About the role

Outset invented a better way to do research: AI-powered user interviews.

Global brands like Hubspot, Microsoft, Uber and Nestle use Outset to achieve deeper, qualitative insights about their users at unprecedented speed and scale.

The research industry is massive (>$140B and growing) and ripe for disruption. Incumbents are outdated, slow, and burdened by high-cost services. That's our opportunity.

Outset is backed by top Silicon Valley investors and raised a $30M Series B in December 2025—less than six months after our Series A—led by Radical Ventures with participation from M12 (Microsoft’s venture arm), 8VC, Y Combinator, and Adverb Ventures.

The raise follows a breakout year, with our business growing 8x as enterprise customers across industries adopt our category, AI‑moderated research, as the new standard for understanding people.

We’re a tight-knit team serving some of the world’s largest enterprises.

Outset builds digital twins: AI systems that represent specific real people and respond to research questions the way those people would. The Digital Twins Team runs an ongoing research program to measure and improve how faithfully these systems reproduce human responses and behavior, across a range of cross-sectional levels of analysis.

The Research Engineer reports to the Head Scientist and builds and maintains the systems that let the team's scientists run their studies correctly, reproducibly, and at scale. The scientists design the studies and define the measurements; you make sure every experiment executes exactly as designed, that results can be reproduced from stored inputs, and that research code stays in step with the shipped product. The ideal candidate has supported scientists in an academic computational social science lab or a comparable research setting and has hands-on experience engineering systems built on large language models.

Responsibilities

- Own the evaluation infrastructure that tests competing modeling approaches, so that new tests can be set up quickly, run concurrently and automatically, and be monitored.

- Implement measurement pipelines exactly as specified by the scientists, with deterministic and traceable outputs.

- Maintain versioned evaluation datasets with held-out sets that are never used for training.

- Build and maintain data pipelines for human-participant data, external datasets, and model training data, including linkage of records belonging to the same participant across sources.

- Keep research code in step with the production product so that validated improvements ship promptly.

- Detect and prevent threats to the validity of evaluations.

- Track compute and API spend across experiments and their implications for production-stage costs

- Manage repositories, code review, documentation, and the reproducibility of the team's coding workflow.

- Identify implementation choices that can be made more correct or more efficient, and propose changes.