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About the Role
Build the perception layer for an autonomous laboratory, enabling real-time error detection and comprehensive experiment provenance capture. This role is critical for ensuring the quality and reliability of data used for training AI foundation models in biology.
Responsibilities
- Own the lab's perception system from end to end.
- Onboard sensors and cameras to create a unified, timestamped data model mapped to the physical space.
- Develop and deploy vision models (VLMs and VLAs) for real-time error detection and alerting.
- Implement perception systems on edge compute for on-site, real-time processing.
- Ensure clean, complete, and well-structured data capture for downstream software and intelligence layers.
- Collaborate with software engineers, scientists, and the intelligence team.
- Get cameras and sensors capturing live data within the first 30 days.
- Ship real-time error detection on a live instrument within 30-60 days.
- Extend capture to instrument telemetry and material movement within 30-60 days.
- Integrate environmental sensors and harden the edge pipeline within 60-90 days.
- Feed clean, mapped data into the metadata layer for scaling towards full automation within 60-90 days.
Requirements
- Experience building perception or sensing systems in the physical world (robotics, autonomous vehicles, drones, manufacturing, or scientific instruments).
- Hands-on experience with cameras and sensors, including calibration, synchronization, capture pipelines, and deployment.
- Production computer vision experience, including vision-language models (VLMs or VLAs) or strong classical CV.
- Strong software engineering skills.
- Comfort at the software-to-hardware boundary.
- Four or more years of experience building systems of this kind.
- Experience with edge deployment and real-time inference on on-site compute (e.g., Nvidia Jetson).
- Experience with sensor fusion, SLAM, or large-scale time-series and telemetry.
- Exposure to lab automation, scientific instruments, or other regulated physical-data environments.
- Early-stage or founding-engineer experience at a venture-backed company.
- Ability to work hands-on as an individual contributor.
- Comfort with early-stage ambiguity and making decisions with partial information.
- Desire to be close to the hardware and the science.
Skills
- Perception systems
- Sensing systems
- Computer vision
- Vision-language models (VLMs/VLAs)
- Classical CV
- Camera calibration
- Sensor synchronization
- Capture pipelines
- Production deployment
- Software engineering
- Edge computing
- Real-time inference
- Sensor fusion
- SLAM
- Time-series data
- Telemetry
Location
- London, UK
Work Type
- Onsite (minimum 3 days/week)
- Full-time
Experience Level
- 4+ years of experience
Benefits
- 30 days of annual leave plus public holidays
- Pension with 10% employer contribution
- Top-tier private health cover with Bupa
About the Company
- Substrate is building the critical infrastructure layer between AI and biology.
- We are creating an AI-native automated lab that produces biological data at scale.
- We address the data problem in AI for biology by generating high-quality, large-scale data with built-in quality and provenance.
- We are a venture-backed company with four co-founders, building our first lab at 20 Triton Street in London.
- We are not a cloud lab or a CRO; we are the infrastructure that turns scientific intent into executed experiments and structured, AI-ready data.
Equal Opportunity
- Substrate is an equal opportunity employer.
- Hiring decisions are made on merit, scope-fit, and the strength of the expected working relationship.
- Applications are welcome from candidates of any background.