Staff Machine Learning Engineer
Harnham
- Manchester, England, United Kingdom
- Manchester, England, United Kingdom
About
This organisation is a forward‑thinking technology business building data‑driven products powered by advanced machine learning. They solve complex challenges across areas such as NLP, automation, and large‑scale model deployment. With a distributed technical team across Europe, they emphasise collaboration, experimentation, and strong engineering standards. You'll join at a time of investment in AI, where your decisions directly shape the road‑map and overall model capability. THE ROLE
As a
Staff Machine Learning Engineer
you will… Lead the design, development, and deployment of end‑to‑end ML solutions. Architect scalable ML systems and pipelines that integrate seamlessly with cloud infrastructure. Mentor engineers, championing best practices across coding, experimentation, and MLOps. Collaborate with Product and Engineering teams to define priorities and model strategy. Apply deep learning, NLP or classical ML techniques to real‑world, high‑impact problems. Uphold responsible AI principles across model development and evaluation. YOUR SKILLS & EXPERIENCE
The successful
Staff Machine Learning Engineer
will have: Strong Python skills and experience with ML frameworks such as TensorFlow or PyTorch. Deep knowledge of machine learning and modern deep learning techniques. Experience deploying ML models to production using cloud platforms (AWS, GCP or Azure). Familiarity with big‑data or distributed tools (Spark, Kafka or similar). A track record of leading complex ML projects and influencing technical decisions. Excellent communication skills across distributed, cross‑functional teams. WHAT THEY OFFER
The successful
Staff Machine Learning Engineer
will receive: Fully remote work across Europe with flexible collaboration hours. Modern tooling, supportive leadership, and clear progression into senior technical influence. Exposure to diverse ML applications and the chance to shape long‑term AI strategy. HOW TO APPLY
Please register your interest by applying via the link on this page with your CV.
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Languages
- English
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