At a Glance
- Tasks: Lead the design and evolution of ML infrastructure and MLOps capabilities.
- Company: Join iProov, a leader in biometric identity assurance with a diverse and inclusive culture.
- Benefits: Negotiable salary, performance bonus, share options, and great company benefits.
- Other info: Hybrid role with excellent growth opportunities in a dynamic team.
- Why this job: Make a real impact by bridging research and production in machine learning.
- Qualifications: Experience in fast-paced tech environments and strong technical leadership skills.
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.
How you can make an impact
- 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
What we would like to see from you
You will have experience working in high growth, fast paced tech-first environments. You are passionate about building.
Remote ML Infrastructure Lead employer: iProov
iProov is an exceptional employer that champions diversity and inclusion, creating a supportive environment where every employee can thrive. With a hybrid work model based in the vibrant WeWork Waterloo, employees benefit from competitive compensation, performance bonuses, and share options, alongside opportunities for professional growth in cutting-edge biometric technology. Join us to be part of a team that values your contributions and empowers you to make a meaningful impact in the world of secure digital identity.
StudySmarter Expert Advice🤫
We think this is how you could land Remote ML Infrastructure Lead
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects and contributions to ML infrastructure. This gives you a chance to demonstrate your hands-on experience and technical prowess beyond just a CV.
✨Tip Number 3
Prepare for interviews by brushing up on both technical and soft skills. Practice common ML scenarios and be ready to discuss how you've tackled challenges in previous roles. Remember, they want to see how you think and solve problems!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at iProov and contributing to our mission.
We think you need these skills to ace Remote ML Infrastructure Lead
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the ML Infrastructure Lead role. Highlight your hands-on experience in machine learning, platform engineering, and MLOps to show us you’re the perfect fit!
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're excited about this role at iProov. Share specific examples of how you've tackled challenges in ML infrastructure and how you can contribute to our mission of secure biometric solutions.
Showcase Your Technical Skills:Don’t hold back on showcasing your technical prowess! Include relevant projects or achievements that demonstrate your ability to design and evolve ML systems, as well as your experience with CI/CD and model management.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at iProov
✨Know Your Tech Inside Out
Make sure you’re well-versed in the latest ML infrastructure trends and tools. Familiarise yourself with concepts like CI/CD, model versioning, and MLOps practices. Being able to discuss these topics confidently will show that you’re not just a leader but also technically savvy.
✨Showcase Your Leadership Skills
Prepare examples of how you've led teams in previous roles, especially in high-growth environments. Highlight your experience in mentoring engineers and driving best practices. This will demonstrate your capability to lead and inspire others in the ML space.
✨Understand iProov's Mission
Research iProov’s biometric solutions and their impact on security. Be ready to discuss how your skills can contribute to their mission of providing secure remote onboarding and authentication. Showing genuine interest in their work will set you apart from other candidates.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think about challenges you’ve faced in ML infrastructure and how you overcame them. This will help interviewers see your strategic thinking and hands-on approach in action.
