Research Engineer – Physical AI

Company

Adoc Talent Management is supporting the recruitment of a Research Engineer – Physical AI for a San Francisco-based company, part of Y Combinator’s 2026 cohort, whose work focuses on collecting human and robotic data to train robots.

The company specializes in collecting first-person, real-world data at a scale and speed that research laboratories cannot readily achieve independently. To ensure traceability and quality, each contributor’s identity is verified through KYC procedures, and every contribution undergoes several quality-control checks before reaching clients.

Looking ahead, the company aims to play a leading role in deploying robots at scale and making the data needed to advance Physical AI more accessible.

To support this ambition, the company is establishing Hub Labs, a research laboratory in Toulouse, France. With access to a large and varied body of data collected directly from contributors, the laboratory will conduct research to support the future deployment of the company’s robot fleet.

The team develops the infrastructure needed to turn research ideas into results quickly and at scale, transforming those results into data that can be used by teams training future AI models.

Position

As Research Engineer – Physical AI, you will work at the interface between scientific research and engineering. Your role will be to turn research ideas into working implementations, reproduce published methods, run experiments and evaluate results. Working closely with the laboratory's researchers, you will help establish whether an approach works in practice, understand its limitations and generate reliable evidence to guide further investigation.

A central part of your work will involve implementing methods described in scientific papers. You will read and interpret publications, translate their methods into code, reproduce results on relevant datasets and investigate discrepancies between expected and observed outcomes. This will require more than implementing algorithms, you will need to understand the experimental setup, identify potential sources of error and assess what the results actually demonstrate.

You will also design and run experiments in collaboration with researchers. This includes training and evaluating models, investigating technical issues that affect experimental reliability, and improving the speed and robustness of experimental workflows. Depending on the work undertaken, you may operate multi-GPU and multi-node training jobs, work with large models and contribute to the laboratory's training stack, including experiment tracking, evaluation tools and computing-resource management.

Research data and infrastructure will be another important part of the role. You will work with data originating from real-world, multi-camera recordings and help prepare clean, versioned datasets for experimentation. Where relevant, you may also work with simulation-generated data. The objective is to provide researchers with reliable data, tools and experimental workflows that enable them to test ideas efficiently and build on previous results.

The position also connects research with physical systems. Depending on the projects, this may involve working with robotic platforms, teleoperation facilities, robotic cells, simulation environments, sensors or calibration systems. Relevant technical specializations may include distributed training and inference optimization, robotics software, simulation, computer vision, video-based learning or large-scale 3D data. You are not expected to be an expert in every area; the important point is to bring strong engineering skills and depth in at least one relevant field.

Beyond individual experiments, you will contribute to making the laboratory's research reproducible and useful. This may include maintaining reproducible code, datasets and models, documenting experimental methods and results, contributing to scientific publications and making research outputs available through open-source channels where appropriate. You may also help translate successful research into practical capabilities for the wider company, for example by integrating a model into an existing data workflow.

This is a research engineering role in which scientific implementation and experimental rigor are central. It is not primarily a conventional production machine learning position focused on maintaining established pipelines or deploying existing models. You will work closely with researchers to make ideas testable, investigate difficult technical problems and establish what can be learned from experimental results.

Profile

You have a strong technical background in computer science, machine learning, robotics or a related engineering or scientific discipline. You combine solid programming skills with the ability to understand scientific methods and translate them into practica