AI/ML & Systems Engineer

 AI/ML & Systems Engineer
Location: Cambridge / Hybrid - UK only
Salary: £8,000–£9,000 per month
Contract: Full-time, initial 3-month fixed-term contract with potential for extension
Start Date: ASAP

Join an early-stage AI startup founded by a recognised leader in data science.
Our client is building a new predictive intelligence platform that combines machine learning, real-world sensor data, geospatial information and engineering principles to predict asset condition and emerging risk.
You'll work closely with the Founder - a highly regarded data science leader and Cambridge and Oxford alumnus - at a stage where the fundamental technology and architecture are still being shaped.
This isn't a role where you'll inherit an established ML platform. You'll have the opportunity to help build the core technology from the ground up, tackling difficult problems around probabilistic forecasting, scarce datasets, sensor signals and real-world physical systems. 

Must-Have Requirements

• Strong Python experience, particularly for data pipelines.
• Experience with probabilistic time-series forecasting, including Bayesian methods, PyMC, Amazon DeepAR or similar.
• Experience working with small or scarce datasets for distribution prediction.
• Proven ability to build data pipelines / database architectures using PostgreSQL, PostGIS, Redis or time-series databases.
• Experience or knowledge in at least one of:
• Vibration analysis
• Digital signal / waveform processing (DSP)
• Physics-informed models or neural networks
• Experience working in an Agile, sprint-based environment and delivering against technical milestones.

Desirable

• React, Grafana or Plotly/Dash
• AWS, Azure or GCP
• Docker / containerisation
• FastAPI / Flask, REST APIs or WebSockets
• Git / GitHub Actions / GitLab CI
• Real-time data visualisation and geospatial data

Why Join?

• Work alongside an exceptional technical founder - collaborate directly with a recognised data science leader with an academic background spanning Cambridge and Oxford.

• Build, don't maintain - influence the ML approach, data architecture and technical direction while the platform is still being created.

• Solve genuinely difficult ML problems - work with imperfect and scarce datasets, probabilistic forecasting, sensor data, DSP and physics-informed approaches rather than straightforward large-dataset modelling.

• See the whole system - work from raw sensor/data pipelines through modelling, APIs and real-time visualisation.

• Have genuine influence - this is a small technical team where an individual engineer can materially shape the product.

If you have the technical experience we’re looking for and this sounds like the kind of challenge you’d enjoy, we’d love to hear from you. Apply with your CV and get in touch directly to find out more about the project and opportunity.