ML Team Lead

Top Remote Talent
Manchester
10 months ago
Applications closed

Related Jobs

View all jobs

Data Engineering Team Lead - Remote - Databricks - Azure - 80k

Data Engineering - £70,000 - Hybrid

Team Lead - Data Engineering

Computer Vision Team Lead - Space Robotics & Autonomy

Data Engineering Lead - Elite HFT Firm - Trading Systems - WFH - London - Up to £600k TC

Head of Data Engineering – Azure Lakehouse Lead


A software development company is looking for a talented, long-term ML Team Lead. 

The company is a team of experts providing analytical services to healthcare clients. You will join an international team of first class professionals who are passionate to create products that improve quality of medical services. 

We have the highest expectations in the industry regarding your ability to deliver high quality, performant, scalable, clean, and tested software. 

Responsibilities:

  • Strong analytical skills (good statistics is a must);

• Team Leadership and Development: Lead a small team of 2-4 data scientists focused on solving challenging problems using tabular data with large, feature-rich datasets. Create a collaborative and productive environment, support team members’ technical and professional growth;

• Technical Task Planning and Prioritization: Work with the Product Manager to understand business goals, set task priorities, and manage team resources effectively. Recommend task prioritization strategies to increase product impact and efficiency;

• Technical Oversight and Quality Assurance: Lead the team through all stages of model development, from design to deployment, ensuring best practices in code quality, documentation, and reproducibility. Set clear, repeatable documentation standards to support scalability and knowledge sharing. Implement systems to monitor model performance and keep ML services stable;

• Cross-Functional Collaboration: Work closely with other teams (e.g., Machine Learning Engineers, MLOps, UI) to ensure ML models integrate smoothly with the overall system and align with other technical projects.

Requirements:
• 5+ years in machine learning with hands-on model development experience;
• 2+ years in a technical leadership role;
• Practical experience with Git, Airflow and MLflow (or similar tools);
• Strong Python skills with experience using popular ML libraries and tools (sklearn, CatBoost, optuna, etc);

•Experience with cloud platforms (AWS, GCP, Azure), especially their ML and data engineering services;

• Advanced SQL skills and experience building/managing data pipelines with Airflow or similar tools;
• Understanding of MLOps practices, including CI/CD for ML;
• English level B2 or higher. 

Preferred Qualifications (Optional):
• Experience in healthcare or medical insurance projects;
• Experience with Google Cloud Platform (GCP). 

Benefits:

  • Flexible working hours; 
  • Remote work; 
  • Interesting projects to work on; 
  • Paid vacations.

#Li - remote

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.