Forward Deployed Engineer - ML - Remote UK

Social network you want to login/join with: col-narrow-left Client: Few&Far Location: Bradford, United Kingdom Job Category: Other – EU work permit required: Yes col-narrow-right Job Views: 3 Posted: 31.05.2025 Expiry Date: 15.07.2025 col-wide Job Description: Fully Remote within the UK Up to £150k + Stock + Benefits Join a US scale-up on a mission to empower Engineering teams to deliver their best work by simplifying the deployment of Machine Learning models. As they expand their global footprint, raised Series C funding, and have grown over 30% in 3 months, they’re seeking a talented Forward Deployed Engineer to join the UK team! About the Role As a Forward Deployed Engineer, you\’ll collaborate directly with customers to understand challenges and engineer ML-based deployment solutions. You\’ll be instrumental in ensuring clients achieve optimal outcomes with their models, focusing on aspects like optimisation, scalability, and efficiency. You’ll work alongside teams that have joined from world-class tech companies like NVIDIA, Amazon, Datadog, Vercel, Meta, GitHub, and Uber. Key Responsibilities Partner with customers to identify and address their ML deployment needs. Implement and optimise ML solutions using Python, open-source tools, and infrastructure. Collaborate with cross-functional teams to enhance product features based on client feedback. Work with Python, PyTorch, TensorFlow, Kubernetes, Docker, and cloud platforms. Ideal Candidates have 5+ years of Backend (Python) Software Engineering experience in a fast-paced, high-growth, product environment. Some interest or experience with the lifecycle of ML model development and deployment. Computer Science degree or similar field of study. Excellent English communication skills: proven experience liaising effectively with customers (other Software Engineers or ML Engineers). Strong problem-solving skills and a proven customer-centric, Product Engineering mindset. This role provides a view into the opportunities and challenges companies face when implementing AI/ML solutions at scale. #J-18808-Ljbffr