Machine Learning Engineer Type : Contract Length : 3-6 months Location : London (hybrid) Core Responsibilities: Collaborate with our founding team to understand requirements and develop technical solutions. Implement, monitor, and deploy advanced statistical and machine learning models to address customer needs in demand planning and inventory optimization. Shape and enhance our tech stack, tooling, and processes to optimize ML/AI capabilities. Conduct code reviews and support ongoing improvements in machine learning and data science processes, explainability, and visibility. Tech Stack You’ll Be Working With Languages: Python, SQL (Postgres) ML/AI Libraries: TensorFlow, PyTorch, scikit-learn, Pandas/Polars Infrastructure: Docker, AWS Sagemaker, EKS, Lambda, Athena Orchestration & Data Tools: Dagster (or Airflow), ClickHouse, MLFlow We pride ourselves on being technologically adaptable. While the above is our current tech stack, we're open to new technologies that can improve our workflows. Experience with analogous tools is also valuable. Must-Haves: Experience with start-ups A 2:1 degree in a STEM field (preferably Computer Science) or equivalent experience. 5-8 years of experience as a Machine Learning Engineer or Data Scientist, ideally with some of that experience in a startup/scaleup. Strong experience with time series analysis, predictive algorithms, machine learning models for forecasting, and optimization algorithms (such as loss functions, constrained-optimization, and Bayesian models). Proficiency in Python and key libraries such as scikit-learn, TensorFlow, Pandas/Polars, and/or PyTorch. SQL skills. Experience with tooling for model deployment, monitoring, and performance analysis. Nice-to-Haves: Familiarity with AWS (Sagemaker, EKS, Lambda, Athena). Experience with MLFlow or similar model management tools. Familiarity with Dagster or similar orchestration tools (e.g., Airflow). If you feel you are suitable for the role, please apply.