Machine Learning Engineer - Fixed Term Contract

Location: Hybrid (minimum 1 day/week in our Vauxhall, London office)
Contract Type: Full-time, Fixed-Term (3 months)
Salary: Competitive, based on experience
Eligibility: UK-based applicants only

About Oddbox

Oddbox is on a mission to fight food waste and transform the food system through our fruit and veg subscription service. To date, we've rescued over 50 million kilograms of produce that would have otherwise gone to waste - but we're just getting started.

As we scale our environmental impact, we're investing in smarter technology and forward-looking ML capabilities. We're now seeking an accomplished Lead Machine Learning Engineer to spearhead innovation in forecasting and customer behaviour modelling, enabling more sustainable, data-driven operations.

About the Role

This is a contract-to-impact opportunity for an experienced ML leader or Staff+ individual contributor who thrives in lean, product-oriented environments. You'll be responsible for shaping and delivering end-to-end forecasting systems that influence core supply chain and customer engagement decisions.

You will:

Lead high-stakes forecasting projects - from ambiguous ideas to productionised ML systems

Architect and deploy solutions in collaboration with Product, Engineering, and Ops

Ensure data and model pipelines are scalable, reproducible, and value-driven

Create measurable business impact within a focused 3-month engagement, with potential for longer-term collaboration

We're looking for someone autonomous, decisive, and outcome-obsessed - capable of steering technical decisions, influencing stakeholders, and shipping ML systems that matter.

Responsibilities

Forecasting Impact: Design, develop, and deploy forecasting models that optimise supply, reduce food waste, and improve customer outcomes

Technical Leadership: Define modelling approaches, data strategies, and validation pipelines in a cross-functional context

Strategic Execution: Rapidly prioritise, structure, and execute on projects with evolving requirements and commercial pressure

System Architecture: Build production-grade, containerised models using modern MLOps practices - integrating with cloud pipelines and multi-source data

Data-Driven Culture: Drive experimentation, model performance monitoring, and business integration of ML outputs

8+ years of experience delivering ML models into production, including time-series forecasting or customer lifecycle modelling

Proven ability to lead complex technical initiatives from ideation through to impact in high-autonomy environments

Track record of building forecasting systems with measurable commercial or operational value

Advanced Python proficiency and familiarity with libraries like XGBoost, Prophet, PyTorch, Scikit-learn

Deep knowledge of MLOps, reproducibility practices, and scalable experimentation

Experience deploying models with CI/CD pipelines, containerisation, and cloud tools (e.g., SageMaker, Vertex AI, GCP pipelines)

Strong data engineering instincts - including building and optimising ETL processes for large, structured and unstructured datasets

Exceptional collaboration skills with the ability to influence both technical and non-technical stakeholders

Our Hiring Process

We value efficiency and depth. Our process is designed to evaluate how you think, architect, and lead - not just what you can code.

Introductory Call (15 minutes)
A short conversation to align expectations, discuss the scope, and answer your initial questions.

Strategic Forecasting Deep Dive (Take-Home Proposal)
You'll receive a realistic brief outlining a forecasting challenge relevant to our domain. We'll ask you to prepare a structured proposal describing how you would approach the problem - including your assumptions, modelling choices, data needs, and delivery roadmap.

We're not testing syntax - we're evaluating your ability to frame problems, communicate clearly, and drive outcomes.

Final Interview (90 minutes)
A collaborative session with our Data and Product teams focused on your approach, technical architecture, decision-making, and stakeholder alignment.

Final Note

This is a rare opportunity to make an immediate, mission-driven impact at a company that values experimentation, autonomy, and sustainability. If you're a Lead or Staff-level ML Engineer ready to shape forecasting strategy and ship high-leverage systems, we'd love to hear from you.