At a Glance
- Tasks: Lead and scale our Machine Learning Engineering capability while managing a talented team.
- Company: Join Hiscox, a forward-thinking company shaping the future of machine learning.
- Benefits: Autonomy to set standards, mentor talent, and explore emerging technologies.
- Other info: Collaborative environment with opportunities for professional growth.
- Why this job: Make a real impact in ML engineering and influence strategic decisions.
- Qualifications: Experience in production ML systems and strong leadership skills required.
The predicted salary is between 80000 - 100000 £ per year.
Role Purpose
As a Lead Machine Learning Engineer (MLE) at Hiscox, you will shape and scale our Machine Learning Engineering capability and ensure the successful deployment and operation of ML in production. You will lead the MLE sub‑chapter, line‑manage Machine Learning Engineers, and partner closely with the Head of Data Science, the Data Science sub‑chapters and Platform/Group teams to enable scalable, reusable, and well‑governed ML solutions. You will be accountable for the MLOps platform, ensuring it is reliable, secure, and continuously evolved and for ensuring our business unit ships ML to production in a scalable way that is reusable across value streams, enabling efficient model maintenance, monitoring, and lifecycle management. Combining deep technical expertise with leadership, you will set standards, uplift capability, and enable squads to deliver robust, production‑grade ML systems.
Responsibilities
- People Leadership: Manage and grow talent; set objectives, conduct performance reviews, and guide career progression for the MLE sub‑chapter. Foster a strong engineering culture: Promote collaboration, psychological safety, and high standards of quality and reliability. Provide coaching and mentorship: Support technical and professional development of Machine Learning Engineers.
- Strategic Capability Development: Define and evolve chapter strategy; align sub‑chapter goals with chapter and organisational objectives. Shape technical direction: Establish standards for ML engineering, deployment patterns, and MLOps. Drive upskilling and cross‑skilling: Build capability in production ML, platform usage, and software engineering best practices.
- Technical Enablement & Platform Ownership: Own and evolve the MLOps platform; ensure it is reliable, secure, and scalable. Enable scalable and reusable ML delivery: Ensure ML solutions for the business unit are deployable across value streams and efficient to operate. Lead technical spikes and proof‑of‑concepts: De‑risk architectural decisions and explore new tools and approaches.
- Governance & Standards: Ensure compliance, security, architecture, and operational standards. Define guardrails for production ML systems covering deployment, monitoring, retraining, and decommissioning in collaboration with Data Science.
- Collaboration & Influence: Partner closely with the Data Science sub‑chapters and delivery team to ensure effective handover from experimentation to production. Represent Machine Learning Engineering in strategic forums and advocate for platforms, tooling, and scalable ML practices.
Qualifications
- Bachelor’s or Master’s in Computer Science, Engineering, or a related quantitative field (or equivalent experience).
- Experience as a Senior/Lead Machine Learning Engineer delivering production ML systems at scale.
- Solid understanding of core data science concepts, including supervised and unsupervised learning, feature engineering, and model evaluation.
- Working knowledge of statistical concepts and model evaluation techniques sufficient to review, validate, and productionise data science work.
- Proven line management and/or technical mentorship of engineers; building capability and setting standards.
- Demonstrated ownership of MLOps platforms or critical ML services, including CI/CD, model serving, monitoring, and incident management.
- Proven ability to design, implement, and operate technical frameworks for evaluating the commercial impact of machine learning systems in production.
- Effective collaboration with Data Scientists across the end‑to‑end ML lifecycle.
- Experience working in Agile, cross‑functional squads.
- Insurance or financial services experience is a plus but not essential.
Technical Skills
- Strong Python in a machine learning engineering context, with solid software engineering fundamentals (OOP, testing, design patterns).
- Production ML systems: Experience deploying, monitoring, and maintaining ML models in live environments.
- Cloud & infrastructure: Hands‑on experience with a major cloud platform (GCP, AWS, or Azure), including containerised deployments.
- MLOps & CI/CD: Experience with CI/CD pipelines, Git‑based workflows, and Infrastructure as Code (e.g. Terraform).
- Operational excellence: Understanding of API operations, monitoring, logging, and reliability considerations for ML services.
- Data & integration: Working knowledge of SQL and integrating ML services into wider data and application ecosystems.
Why Join Us?
This is an opportunity to shape the future of machine learning engineering at Hiscox, build a high‑performing sub‑chapter, and influence strategic decisions, while staying close to the craft you love. You’ll have the autonomy to set standards, mentor talent, and explore emerging technologies, all within a collaborative and forward‑thinking environment.
Lead ML Engineer in London employer: Hiscox Underwriting Group Services Ltd (HUGS)
At Hiscox, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Lead Machine Learning Engineer, you will not only have the opportunity to shape our ML capabilities but also benefit from a strong focus on employee growth through mentorship and strategic development initiatives. Our commitment to autonomy and exploration of emerging technologies ensures that you can thrive in your role while contributing to meaningful advancements in the field.
Contact Details:
Hiscox Underwriting Group Services Ltd (HUGS) Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Lead ML Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage in online forums. The more people you know, the better your chances of landing that Lead ML Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those that demonstrate your ability to deploy and maintain production ML systems. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and leadership skills. Be ready to discuss your experience with MLOps platforms and how you've managed teams in the past. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Lead ML Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Lead ML Engineer role. Highlight your leadership experience, technical expertise, and any relevant projects you've worked on that showcase your ability to deliver production ML systems.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about machine learning and how you can contribute to our team. Share specific examples of your past achievements and how they relate to the responsibilities outlined in the job description.
Showcase Your Technical Skills:Don’t shy away from detailing your technical skills, especially in Python, MLOps, and cloud platforms. We want to see your hands-on experience and how you've applied these skills in real-world scenarios, so be specific!
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people!
How to prepare for a job interview at Hiscox Underwriting Group Services Ltd (HUGS)
✨Know Your ML Fundamentals
Brush up on your core machine learning concepts, especially supervised and unsupervised learning, feature engineering, and model evaluation. Be ready to discuss how you've applied these in real-world scenarios, as this will show your depth of understanding.
✨Showcase Your Leadership Skills
Since the role involves managing a team, prepare examples of how you've successfully led projects or mentored others. Highlight your experience in fostering collaboration and setting high standards within your team.
✨Demonstrate Technical Expertise
Be prepared to discuss your hands-on experience with MLOps platforms and CI/CD pipelines. Share specific instances where you've deployed and maintained ML models in production, and how you ensured their reliability and scalability.
✨Align with Their Vision
Research Hiscox's approach to machine learning and their strategic goals. During the interview, express how your vision aligns with theirs and how you can contribute to shaping their ML engineering capability.
