Product & Data Engineer (£50,000 – £90,000 + Equity) at BlakBear
Summary
Builds scalable GCP pipelines, APIs, and full-stack solutions to process real-time sensor data and AI-driven food-freshness predictions for supply-chain efficiency.
Job Title
Product & Data Engineer
Salary
£50,000 – £90,000 + Equity
Company Description
BlakBear is an Imperial College London spin-out building advanced gas sensors and AI that predict food freshness in real-time, replacing archaic use-by dates across the supply chain.
Job Description
You will own the back-end infrastructure and data strategy for a mission-driven startup tackling global food waste. Working directly with the founders, you will build scalable GCP pipelines, design high-performance APIs, and develop full-stack solutions. Your work directly impacts how the world monitors food quality across global supply chains.
Location
London, UK
Why this role is remarkable
- Join a high-impact mission to eliminate 1/3 of global food waste by replacing archaic printed expiry dates with real-time sensor intelligence.
- Work in a high-caliber environment as an Imperial College London spin-out that is already signing enterprise contracts across Europe and North America.
- Enjoy significant growth potential and ownership in a seed-stage team of under 20 people, directly influencing the product’s technical evolution.
What You Will Do
- Design and maintain scalable ETL pipelines and GCP cloud infrastructure to process real-time sensor data and AI predictions.
- Build and iterate on robust APIs using Python and FastAPI, managing the full product lifecycle from design to operation.
- Collaborate with the CEO and CTO to implement agentic AI tools and full-stack features that enhance the core freshness-prediction platform.
The ideal candidate
- Has 2–8 years of experience in software or data engineering with a strong mastery of Python for production-grade services.
- Possesses a proven track record of building and shipping full-stack applications and maintaining automated CI/CD pipelines.
- Exhibits deep curiosity and a desire to solve hard problems, ideally with experience in startup environments or hands-on LLM agent frameworks.