Research Scientist
Confirmed on the employer's own hiring board on Oct 25, 2026. First seen by Alion on Oct 22, 2026. Outset scores B on the Alion truth index.
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 Incumbents are outdated, slow, and burdened by high-cost services.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 Scientist works directly with and reports to the Head Scientist. Together you will choose what to study, design the studies, define how results are measured, interpret the findings, and write them up for internal decision-making and, where appropriate, for academic publication. You will bring current methodological training in computational social science, data science, survey-methodology, human-computation interaction, and experiment design.
Responsibilities
Co-design and run studies that compare system-generated responses with responses from humans, using both qualitative and quantitative measures.
Design, justify, and hone measures of performance.
Apply and adapt state-of-the-art statistical techniques common to survey methodology and large-scale social data analysis, to benchmark and improve sample- and population-level simulation performance.
Design evaluations and experiments with pre-specified analysis plans.
Design and oversee human rating studies, including rater training, coding rubrics, and inter-rater agreement measurement.
Design data collection from third-party data sources and directly from human participants, including longitudinal and repeated-measures studies.
Contribute to the research agenda for new modeling approaches, including evaluation of fine-tuned models and of model calibration.
Specify analyses precisely enough for an engineer to implement.
Co-author papers, preprints, and presentations.
Qualifications
A completed or nearly completed PhD in computational social science, communication, HCI, sociology, political science, psychology, statistics, or a related field.
Peer-reviewed publications on which you were a primary author.
Working command of graduate-level statistical methodology common to social science, including the assumptions behind each.
Experience analyzing large-scale telemetry and behavioral datasets, with attention to the validity of the resulting inference.
Familiarity with machine learning, spanning classical, nonparametric, and LLM-based approaches, including how transformer-based language models work, what fine-tuning does, and how the major model families differ.
Fundamentals of computing: file systems, software libraries, databases, SQL, and tabular data.
Ability to work in an environment where most code is produced through agentic coding tools, and to read and check that code.
Preferred Qualifications
Experience fine-tuning open-weight language models on survey, experimental, or behavioral data.
Experience building or evaluating calibrated confidence or error-prediction models, including deriving decision thresholds from human judgments.
Experience measuring similarity of open-ended text, for example with embedding-based methods.
Experience designing or running panel-based data collection with human participants.
Experience presenting research at academic and/or industry conferences.
Prior work on generative agents, digital twins, or simulation of human respondents.
Familiarity with the startup ecosystem
Familiarity with qualitative interviews and ethnographic methods
Familiarity with topics in computational linguistics