Research Engineer

The role

The Research Engineer builds and maintains systems that allow scientists to run studies correctly, reproducibly, and at scale. This role involves owning evaluation infrastructure and implementing measurement pipelines for digital twin research.

What you will do

• Own the evaluation infrastructure to test competing modeling approaches.
• Implement measurement pipelines as specified by scientists.
• Maintain versioned evaluation datasets.
• Build and maintain data pipelines for human-participant data, external datasets, and model training data.
• Keep research code in step with the production product.
• Detect and prevent threats to the validity of evaluations.
• Track compute and API spend across experiments.
• Manage repositories, code review, documentation, and reproducibility.
• Identify more correct or efficient implementation choices.

Required 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.
• 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.

Nice to have

• 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.
• 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.

About the company

Outset builds digital twins, which are AI systems that represent specific real people and respond to research questions. The company provides AI-powered user interviews for global brands.