ML Software Engineering Lead in London

ML Software Engineering Lead in London

London Full-Time 80000 - 100000 £ / year (est.) Home office (partial)
Greater Giving, Inc.

At a Glance

  • Tasks: Lead the development of high-profile ML products and drive technical strategy.
  • Company: Join Global Payments, a leader in powering the future of commerce.
  • Benefits: Enjoy competitive salary, bonuses, wellness week, and family support.
  • Other info: Collaborate with top minds in an inclusive, global team.
  • Why this job: Make a real impact on innovative ML solutions in a dynamic environment.
  • Qualifications: 7+ years in ML software engineering with strong leadership skills.

The predicted salary is between 80000 - 100000 £ per year.

Global Payments, including Worldpay, is powering the future of commerce. We are seeking an experienced and visionary ML Software Engineering Lead to serve as the technical and functional leader for the Data Science Enablement engineering function, which owns the production development and ongoing operations of high-profile ML products.

While you will have no direct people‑management responsibilities, you will be accountable for the technical strategy, operational maturity, engineering standards, platform capabilities, and long-term effectiveness of the ML software engineering practice. You will balance strategic leadership with hands‑on technical contribution, dividing your time between setting technical direction and actively participating in architecture, design reviews, code reviews, and selected implementation efforts.

In this strategic role, you will collaborate closely with product, data science, platform engineers, and other product delivery teams to translate ML models and data‑driven algorithms into robust, scalable, and low‑latency production solutions. You will inherit a high‑impact ML product portfolio with a mandate to grow and mature our capabilities to meet expanding business and technical needs.

What you’ll own

  • Strategy and vision: Define the technical vision and strategy for ML software engineering initiatives, aligning them with business goals. Develop scalable capabilities to power real‑time decisioning engines throughout the payment lifecycle and beyond. Enable rapid experimentation while ensuring robust, scalable, and secure deployment of ML solutions.
  • Engineering and operational excellence: Establish and evolve engineering standards, operating practices, and technical governance. Mentor engineers, provide technical coaching, and promote technical excellence. Champion collaboration, continuous improvement, and knowledge sharing. Drive alignment across teams through technical influence, architectural guidance, and shared engineering standards rather than direct management authority. Identify capability gaps and drive improvements to tooling, automation, observability, and operational processes. Drive consistency in engineering practices and operational processes across teams delivering and supporting ML‑powered products. Establish operational standards for production ML systems, including reliability objectives, observability, incident management, and support processes.
  • Technical leadership and contribution: Guide the architecture, implementation, deployment, and operation of ML products and reusable components. Ensure systems and components meet requirements for scalability, latency, explainability, and regulatory compliance. Establish and promote best practices for ML software engineering. Stay abreast of industry trends and emerging technologies to drive adoption of modern tools, frameworks, and infrastructure. Contribute to QA and code as needed.
  • Cross‑functional collaboration: Partner closely with research‑focused data science teams, business stakeholders, infrastructure support teams, data engineering teams, security/compliance teams, etc. to identify opportunities and incorporate ML into products and systems. Collaborate with other data science and engineering leaders to establish an operating model for machine learning R&D that optimizes end‑to‑end delivery of business value. Communicate complex technical concepts to non‑technical stakeholders effectively.

What you’ll bring

  • Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related field (PhD a plus).
  • 7+ years of ML software engineering, ML ops, ML engineering, or ML research experience.
  • 5+ years of experience deploying large‑scale, real‑time ML models in customer‑facing, production environments, including significant experience hands‑on.
  • 2+ years of technical leadership experience on an early‑stage ML software engineering team.
  • 2+ years of data science research experience.
  • Proven experience developing microservices at scale (API design, monitoring, deployment strategies, containerization) in a cloud environment (preferably AWS and DataBricks).
  • Strong understanding of the data science/ML research process.
  • Strong understanding of software engineering, MLOps, and DevOps best practices.
  • Strong Python skills, including in relevant libraries such as Pandas, NumPy, scikit‑learn.
  • Proficiency in SQL and NoSQL databases.
  • Excellent communication, leadership, and stakeholder management skills.

It’s a bonus if you have

  • Experience in a merchant acquiring, payment service provider, or card network environment.
  • Familiarity with tokenization, real‑time payments, and the authorization lifecycle.
  • Experience in a large, complex organization in a highly regulated industry.
  • Experience working in an agile environment.

Impact: Play a key role as the technical owner of high‑profile ML products delivering meaningful business impact to merchants and advancing key pillars of the company’s strategy. Your work will directly influence the reliability, scalability, and evolution of critical production systems.

Autonomy: Take end‑to‑end technical ownership of your product area, with the freedom and responsibility to drive technical solutions, shape best practices, and deliver results in a fast‑paced, supportive environment.

Collaboration: Join a cross‑functional, high‑performing team where your expertise is valued and your contributions make a real difference.

About the team: Our inclusive and global teams win together every day. We’re proud to have the best minds in the industry, who you can learn from as you grow your career. The people, the energy, the connections – it’s unmatched. Come and be part of an ever‑evolving company and get dynamic opportunities that go beyond borders.

What makes a Globalpayer? Globalpayers think like a client, act like an owner and win as one team. We’re curious and innovative – always finding better ways to deliver impact. We empower each other to make decisions, and it’s our passion that drives excellence in everything we set out to do.

My Health: We offer access to medical coverage that supports the health and wellbeing for you and your family.

My Money: In addition to base salary, you may be eligible for bonuses and/or equity awards, determined by role, level, impact, and our policies.

My Lifestyle: We have a dedicated Wellness Week each year – which includes an extra paid day off - so you can take time to relax, recharge and invest in your wellbeing.

My Family: FLEX, our family inclusion community, offers support for all Globalpayers at every stage, from raising families to caring for loved ones.

My Time Off: We offer paid time off and public holidays so you can rest, recharge and spend time on what matters most.

Greater Giving, Inc.

Contact Details:

Greater Giving, Inc. Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land ML Software Engineering Lead in London

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Contribute to Open Source Projects

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We think you need these skills to ace ML Software Engineering Lead in London

Machine Learning (ML) Software Engineering
Technical Leadership
Architecture Design
Code Reviews
Real-time ML Model Deployment
Microservices Development
API Design

Some tips for your application 🫡

Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.

Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Greater Giving, Inc..

Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Greater Giving, Inc. and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!

Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!

How to prepare for a job interview at Greater Giving, Inc.

Brush Up on Your Coding Skills

For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.

Know Your Tools and Frameworks

Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Greater Giving, Inc. uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.

Showcase Your Projects

Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.

Prepare for Behavioural Questions

While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.