193 Best 8 to 5 Jobs in Cambridge (June 2026)

Remote ML Infrastructure Lead

12 days ago

Cambridge

ML Infrastructure Lead About iProov iProov provides science-based biometric solutions that enable the world’s most security-conscious organizations to streamline secure remote onboarding and authentication for digital and physical access. Our award-winning liveness technology and iSOC offer unmatched resilience against deepfakes and generative AI threats while ensuring effortless, scalable user experiences. Trusted by leading governments and enterprises, including the U.S. Department of Homeland Security, U.K. Home Office, GovTech Singapore, ING, and UBS, iProov sets the standard in biometric identity assurance. This global trust is built not only on our technology but on the strength of the people behind it. For us, diversity at iProov is about reflecting the customers we serve, holding the principles of equality and inclusion at the heart of everything we do and all that we stand for, embracing differences, creating possibilities, and growing together. We aim to foster a culture where individuals of all backgrounds feel confident in bringing their whole selves to work, feel included, and their talents are nurtured, empowering them to contribute fully to our purpose. The Role Reports to: Chief Scientific Officer Location: WeWork Waterloo - Hybrid Comp: Negotiable (Base) + Company Performance Bonus (20%) + Share Options + iProov Benefits We are looking for a highly capable and hands-on Senior ML Infrastructure Lead to build and scale the technical foundations that enable machine learning to operate effectively in production. This is a hybrid leadership role sitting across machine learning infrastructure, platform engineering and MLOps. You will be responsible for designing and evolving the systems, tooling, processes and standards that allow ML teams to train, deploy, monitor and improve models reliably, securely and at scale. You will work at the intersection of machine learning, software engineering, data, cloud infrastructure and platform reliability, helping bridge the gap between research and production. This role is ideal for someone who can think strategically about long-term platform capability, while still being technically hands-on enough to solve complex engineering and operational challenges. • Lead the design and evolution of our ML platform, infrastructure and MLOps capability, • Build and maintain scalable, reliable and secure systems for model training, testing, deployment, monitoring and lifecycle management, • Develop the infrastructure and tooling that enable ML Engineers, Data Scientists and Researchers to work efficiently and ship models with confidence, • Design robust workflows for CI/CD, model versioning, reproducibility, experimentation, feature management and release management, • Own and improve the production environment for machine learning systems, ensuring strong standards for availability, performance, observability and resilience, • Define and implement monitoring across model and platform layers, including system health, data quality, drift, latency, throughput and cost efficiency, • Build or optimise internal self-service tooling and platform capabilities to reduce friction for teams working on ML use cases, • Partner closely with ML, Data, Software and Platform Engineering teams to productionise models and improve the end-to-end ML development lifecycle, • Support the scaling of infrastructure for both training and inference workloads, including high-throughput, real-time or compute-intensive use cases where relevant, • Drive best practice in governance, security, compliance, auditability and operational rigour across the ML lifecycle, • Improve the efficiency and cost-effectiveness of ML systems, including cloud resource usage, compute environments and deployment patterns, • Mentor engineers and act as a technical leader across ML platform and operations topics, • Help define the roadmap for ML enablement, ensuring the platform can support current needs while scaling for future growth You will have experience working in high growth, fast paced tech-ﬁrst environments. You are passionate about building & launching quality products that have a positive impact. You’re an experienced product leader with a background in security, identity (IAM), or enterprise SaaS. You combine strategic vision with operational rigour, and you’re motivated by delivering usable, secure, and elegant solutions to complex technical problems. • Proven experience in a senior MLOps, ML Platform, ML Infrastructure, Platform Engineering or Machine Learning Systems role, • Strong hands-on background in software engineering and cloud infrastructure, ideally with direct experience supporting production machine learning environments, • Experience building and operating systems that support the full ML lifecycle, from experimentation and training through to deployment and monitoring, • Strong knowledge of Python and sound engineering principles, including testing, automation and code quality, • Strong experience with cloud platforms such as GCP, • Experience with Docker, Kubernetes and modern containerised deployment patterns, • Strong experience with CI/CD pipelines, infrastructure-as-code and workflow orchestration, • Experience with tools such as Airflow or similar platform and orchestration technologies, • Good understanding of model observability, data quality, feature pipelines, lineage and reproducibility, • Experience designing scalable infrastructure for ML workloads, including training, batch inference and real-time serving, • Strong appreciation of reliability, security, governance and operational excellence in customer-facing or production-critical systems, • Ability to operate across both strategic and hands-on technical work, • Strong communication skills and the ability to work effectively across engineering, product and data teams Nice-to-haves • Experience supporting computer vision, deep learning, LLM or other compute-intensive ML workloads, • Experience with GPU infrastructure, distributed training or high-performance compute environments, • Familiarity with feature stores, model registries and automated retraining pipelines, • Experience building internal developer platforms or self-service ML tooling, • Experience in regulated, high-security or high-availability environments, • Experience leading or mentoring engineers in a scale-up or high-growth technology business, • Familiarity with responsible AI, model governance or risk controls in production ML setting, • 25 days Annual Leave, plus 8 Bank Holidays (more holiday with service - up to an extra 5 days off per year based on your continuous service), • Growth Shares allocated after passing probation (6 months of service), • Salary sacrifice schemes including: Pension, Cycle To Work and Electric Car Scheme, • Nursery Sacrifice Scheme, • Work Overseas Perk - Work globally for up to 2 weeks, • Life Assurance, • SmartHealth - Access to private GP, Psychologist, Nutritionist along with tailored fitness plans for both you and your family, • Benefit from personalized 1:1 career coaching with our in-house Occupational Psychologist, • Award winning L&D platform with personal allocated training budgets, • Enhanced paid family leave, • Pension - 5% employee, 3% employer, • Flexible hybrid working environment, • Free Barista Coffee/Tea, biscuits with fruit in the WeWork office, • Free access to WeWork discounts and free online well-being sessions, • Vitality Health - a range of options available on this below The Vitality Programme includes a number of reward benefits that all employees have access to as part of the plan, for example: • Private Health cover including Dental, Optical, and Audiology, • 50% off monthly gym memberships, • Apple watches significantly discounted based member vitality status, • Half price trainers with Runners Need, • Weekly rewards – Free coffee with Café Nero, • Monthly rewards – Free Cinema ticket, • Discounts on travel with Expedia (hotels) and Mr & Mrs Smith with discounts getting greater throughout the year based on a members vitality status, • Amazon prime free months based on activity, • Up to 25% cashback at Waitrose when buying healthy foods, • 75% off stays at Champneys Health Spas, • Allen Carr’s £299 no smoking programme for free, • Access to Vitality Healthy Mind with 30% off Headspace subscriptions and the ability to earn Vitality points for using Buddhify, Calm and Headspace, • Discounts on Weight Watchers, • 50%-80% off Comprehensive Private Health screenings Our Culture & Recruitment Process At iProov, we're incredibly proud of the culture we've carefully curated. Our culture enables diverse thought, curiosity and innovation. Our team strives to do everything to the highest standard possible to achieve the remarkable. To do that we need different perspectives, experiences and ideas alongside an environment where these are welcomed - we want everyone to feel confident in bringing their full capabilities to work. We firmly believe psychological safety is key to building and nurturing great teams. We’re a small and dynamic company, that means having the right skills is important, and we know that our best work emerges when people feel secure, welcomed and respected. As an equal opportunities employer, we encourage applications from people of all backgrounds. We’re committed to building a workforce that is representative of the people we serve. We will not put someone at a disadvantage or treat them less favourably because of race, color, national origin, ancestry, age, disability, creed, religion or belief, sex, sexual orientation, gender reassignment, marriage or civil partnership, or pregnancy and maternity. Our goal is to find people who are passionate about creating a safer, more secure world. Our recruitment process is designed to be fair and transparent, focusing solely on your qualifications, competence, and suitability for the role. We review all applications carefully and will be in touch with shortlisted candidates regarding the next steps in our interview process. If you need an adjustment for a disability or any other reason during the hiring process, please send a request to _

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