Senior ML Engineer

Madfish; United Kingdom; Remote; GBP 100,000 - 125,000; Full time

Job summary

A leading AI startup in the UK is looking for a Senior ML Engineer to design and optimize large-scale models. You'll be responsible for building ML pipelines, mentoring teammates, and ensuring compliance across the ML lifecycle. The ideal candidate has over 5 years of experience, strong Python skills, and expertise in MLops, including Docker and Kubernetes. Competitive pay and equity offered, with opportunities to shape the future of AI in healthcare.

Benefits

Competitive equity & pay

Work closely with experienced founders

Fast-paced work environment

Qualifications

• 5+ years as an ML Engineer or applied ML researcher with production model deployment experience.
• Solid understanding of model evaluation, A/B testing, and ML performance metrics.
• Excellent collaboration skills with product, backend, and data teams.

Responsibilities

• Design, fine-tune, and evaluate large language models and neural networks.
• Build robust ML pipelines including data ingestion and model training.
• Mentor junior ML teammates and establish team-level best practices.

Skills

Strong Python skills

Experience with ML frameworks (TensorFlow, PyTorch)

Expertise in MLops (Docker, Kubernetes)

Collaboration skills

Tools

Docker

Kubernetes

HuggingFace

Job description

The Role

We’re looking for a Senior ML Engineer to lead the design, deployment, and optimization of large-scale models powering intelligent agents. Your role spans model architecture, operational deployment, and production monitoring.

Key Responsibilities

• Design, fine-tune, and evaluate large language models and neural networks for modular agent behavior
• Build robust ML pipelines (data ingestion, feature engineering, model training, serving, monitoring)
• Develop containerized model serving infrastructure (Docker, Kubernetes), integrating with backend APIs
• Implement evaluation frameworks, A/B testing, and performance metrics to quantify agent effectiveness
• Ensure reproducibility, traceability, and compliance across ML lifecycle
• Collaborate with backend engineers to define inference service SLAs and efficient real-time ML delivery
• Mentor junior ML teammates and establish team-level best practices

Requirements

• 5+ years as an ML Engineer or applied ML researcher with production model deployment experience
• Strong Python skills, experience with ML frameworks (TensorFlow, PyTorch) and LLM tooling (HuggingFace)
• Expertise in MLops: Docker, Kubernetes, model serving (e.g., Triton, FastAPI), CI/CD
• Familiarity with data pipelines, SQL, cloud platforms (AWS SageMaker, GCP Vertex, Azure ML)
• Solid understanding of model evaluation, A/B testing, and ML performance metrics
• Excellent collaboration skills with product, backend, and data teams

Nice to Haves

• Prior work with conversational agents, retrieval-augmented generation, or multi-model orchestration
• Experience with vector search stacks (e.g. Pinecone, FAISS)
• Knowledge of embedding techniques, prompt engineering, or Reinforcement Learning from Human Feedback (RLHF)
• Startup experience and ability to navigate ambiguity and shape technical direction

Why You Should Join

• Competitive equity & pay - get in early and own what you build.
• Work closely with experienced founders with a proven startup track record.
• Move fast, ship fast - no corporate bureaucracy.
• Shape the AI revolution in healthcare - massive market, untapped potential