Research Associate

About the Role

This is a software engineering role with a robotics mandate focused on building a closed-loop discovery engine. The role involves building systems including data pipelines, services, interfaces, evaluation harnesses, and the control and learning stack that drives a manipulation station. It also includes ownership of the hardware side of the station. The company is open to a range of experience levels, from strong master's graduates with portfolios to senior individuals from top robotics companies.

Key Responsibilities

• Build the software that makes the robot useful: data infrastructure, services, APIs, and interfaces.
• Build the teleoperation and data-capture pipeline to create clean, labeled training data.
• Own the robotic manipulation station end to end, including control stack, dexterous manipulators, depth cameras, and VR teleoperation.
• Train, evaluate, and deploy manipulation policies for real laboratory tasks, starting with liquid handling and plate manipulation, and establish success metrics.
• Set up simulation for the station to develop and stress-test policies before hardware deployment.
• Integrate the robot and its data into the broader AI agent harness.
• Automate real experimental workflows and measure the impact of automation.
• Work across the general engineering stack: tooling for scientists, internal applications, pipelines, and cloud infrastructure.

Requirements & Tech Stack

• Strong software engineering fundamentals: architecture, version control, testing, deployment, and systems that others can run.
• Experience building full-stack systems from database and backend infrastructure to user interfaces.
• Experience shipping real robot software: manipulation, motion planning, control, or learned policies, in ROS/ROS2 or an equivalent stack.
• Ability to provision, deploy, and manage cloud resources such as Docker, AWS, and bare metal.
• Fluency in leveraging AI coding tools such as Claude Code, Codex, and Grok.
• Comfort with hardware: mounting, wiring, calibration, and debugging robots.
• A portfolio of systems taken from conception to working, ideally with video.
• Judgment to decide what to build next.

Bonus Points

• Imitation learning in practice: behavior cloning, diffusion policies, ACT, or vision-language-action models.
• Teleoperation rigs, VR controllers, or other demonstration-collection systems.
• Simulation and sim-to-real transfer: MuJoCo, Isaac Lab, or similar.
• Familiarity with Postgres, TypeScript, React, and modern backend and frontend frameworks.
• Experience building infrastructure and tooling around AI processes and agents.
• Laboratory automation: liquid handlers, plate readers, SiLA or OPC UA, or scheduling software.
• Experience with multimodal data, video pipelines, or large-scale data capture.
• Curiosity about biology.

Qualifications

• A bachelor's, master's, or doctoral degree in computer science, robotics, electrical or mechanical engineering, or a related field.