Machine Learning Engineer (AI)

Location: Hybrid in London, UK. Candidates typically need the right to work in the UK; visa sponsorship is generally unavailable.

Salary: £85,000–£100,000 per year, plus equity and bonus.

Company

hirify.global is a climate-tech first-mile intelligence platform using AI, satellite data, and quantitative modelling to provide visibility into risks and performance across agricultural and soft commodity supply chains.

What you will do

• Build and deploy forecasting models for environmental and risk signals, including agricultural stress, weather, and climate volatility.
• Extend weather-data pipelines covering ingestion, standardisation, spatial aggregation, climatology, indices, and stress scoring.
• Use optical and radar satellite data to develop vegetation-stress signals, land-cover classifications, and land-surface condition models.
• Take research from prototype to production by building infrastructure, orchestration, failure handling, monitoring, and reproducible data deliveries.
• Improve experiment design, evaluation protocols, documentation, and uncertainty treatment across the AI pod.
• Partner with Science, Engineering, Product, and Market Intelligence to ensure signals answer customer and commercial questions.

Requirements

• Experience building deep-learning and statistical models for time-series or spatial data, with detailed projects to demonstrate.
• Fluency in the Python scientific stack, including PyTorch, scikit-learn, scipy, and xarray.
• Experience with version control, experiment tracking, orchestration, cloud infrastructure, and reproducible workflows.
• Ability to investigate data, state modelling assumptions, explain implementation decisions, and communicate uncertainty clearly.
• Right to work in the UK is typically required; visa sponsorship is generally not available.

Nice to have

• Experience with weather and climate data, including reanalysis products, numerical forecasts, weather stations, or forecast verification.
• Experience working with remote-sensing datasets.
• Exposure to risk modelling, financial time series, commodity markets, or systematic-strategy backtesting.

Culture & Benefits

• High-trust environment with autonomy, experimentation, continuous learning, and close collaboration across disciplines.
• Unlimited leave, flexible working hours, medical insurance including optical and dental coverage, pension scheme, group life insurance, and group income protection.
• Cycle to Work scheme, company Apple MacBook, office snacks and drinks, and monthly team socials.
• Opportunity to work on applied AI, environmental data, climate risk, commodities, and global supply-chain challenges.

Hiring process

• Recruiter screen lasting 30–45 minutes.
• Hiring manager interview, team and skills session, and product interview.
• Final cross-functional or executive conversation, if applicable.