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 Incumbents are outdated, slow, and burdened by high-cost services.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.
Qualifications
Several years of software engineering experience, including experience supporting researchers in a lab or comparable setting.
Experience translating specific scientific requirements and experiment designs into code.
Experience building and operating large-scale data pipelines on human behavior data, and confirming that the pipelines matched the criteria and design the researchers specified.
Strong Python, including the ability to read and audit code produced by agentic coding tools.
Fundamentals of computing: file systems, software libraries, databases, SQL, tabular data, and Git-based workflows.
Experience with cloud infrastructure and with running many jobs concurrently.
Working understanding of machine learning, including how transformer-based language models behave, how model APIs are used, and what fine-tuning involves.
A habit of writing deterministic, reproducible, cost-aware, and well-documented code.
Preferred
Hands-on experience engineering large language model systems: prompt pipelines, evaluation harnesses, embedding computation, agentic systems, and/or fine-tuning infrastructure.
Experience fine-tuning open-weight language models on multi-GPU hardware.
Experience serving open-weight models with an inference framework such as vLLM or SGLang, including batched inference and prefix caching for many requests that share a common prompt.
Experience with experiment tracking and cost monitoring for machine learning workloads.
Experience with survey or panel data and data collection platforms.
Experience extracting and storing model activations for downstream analysis.
Experience with experiment tracking and cost monitoring for machine learning workloads, including GPU provisioning across cloud providers.
Prior work on generative agents, digital twins, or simulation of human respondents.