Research Engineer
OutsetAI-moderated research company
San Francisco, United StatesMid
8VC
Y Combinator
Future Back Ventures by Bain & Company
Adverb Ventures
Rebel Fund
Genius Ventures
Data & AINew
About the role
TL;DR
Builds and maintains systems for AI-powered research studies.
- •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- •Incumbents are outdated, slow, and burdened by high-cost services.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.
Required skills
PythonSQLGitLLMsLangChainHugging FaceDockerKubernetesCI/CDAWSGoogle CloudAzure
Nice-to-have skills
TensorFlowPyTorchscikit-learnOpenAI API
Domain expertise
aiproductivity
Tech stack
PythonSQLGitLinuxAWSGoogle CloudAzureDockerKubernetesCI/CDTensorFlowPyTorchscikit-learnOpenAI APILangChainHugging FaceLLMs