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Outset has invented AI-powered user interviews, enabling global brands like HubSpot, Microsoft, Uber, and Nestle to conduct qualitative research at unprecedented speed and scale. The company is disrupting the $140B+ research industry with a category called AI-moderated research. Backed by top Silicon Valley investors including Radical Ventures, M12 (Microsoft's venture arm), 8VC, Y Combinator, and Adverb Ventures, Outset raised a $30M Series B in December 2025 and achieved 8x business growth in the past year.
Outset's core technology builds digital twins—AI systems that represent specific real people and respond to research questions as 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 various levels of analysis.
As Research Engineer reporting to the Head Scientist, you will build and maintain the systems that enable the team's scientists to run studies correctly, reproducibly, and at scale. Scientists design studies and define measurements; you ensure every experiment executes exactly as designed, results can be reproduced from stored inputs, and research code stays synchronized with the shipped product.
Key responsibilities include:
- Own the evaluation infrastructure for testing competing modeling approaches, enabling quick setup, concurrent execution, automatic monitoring, and rapid iteration.
- Implement measurement pipelines exactly as specified by scientists, with deterministic and fully traceable outputs.
- Maintain versioned evaluation datasets with held-out sets never used for training.
- Build and operate data pipelines for human-participant data, external datasets, and model training data, including record linkage across sources.
- Keep research code synchronized with production so validated improvements ship promptly.
- Detect and prevent threats to evaluation validity.
- Track compute and API spend across experiments and their production-stage cost implications.
- Manage repositories, code review, documentation, and reproducibility of the team's coding workflow.
- Identify implementation choices that can be made more correct or efficient, and propose improvements.
QUALIFICATIONS (Required):
- Several years of software engineering experience, including 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, confirming pipelines matched researcher-specified criteria and designs.
- Strong Python, including 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 running many jobs concurrently.
- Working understanding of machine learning, including transformer-based language model behavior, model API usage, and fine-tuning.
- Habit of writing deterministic, reproducible, cost-aware, and well-documented code.
PREFERRED QUALIFICATIONS:
- 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 inference frameworks such as vLLM or SGLang, including batched inference and prefix caching.
- 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.
- Prior work on generative agents, digital twins, or simulation of human respondents.
