Senior Machine Learning Scientist

Project
We are looking for a Senior Machine Learning Scientist to join our growing AI team and help shape the next generation of privacy-preserving artificial intelligence solutions.
In this role, you will work at the intersection of machine learning research, generative AI, and privacy-enhancing technologies to design and develop innovative AI systems that protect sensitive personal and enterprise data by design. You’ll collaborate with multidisciplinary teams to research, prototype, and deliver reliable AI systems and components for real-world and production environments.
This is an opportunity to work on technically challenging problems, contribute to published research, mentor talented engineers and researchers, and contribute to the development of responsible AI solutions.
What You’ll Do
Lead or substantially contribute to technical research initiatives in machine learning and generative AI, defining experimental approaches and delivering high-quality outcomes.
Design, develop, and validate machine learning models focused on privacy-preserving AI, including techniques such as differential privacy, federated learning, and secure inference.
Research and implement novel algorithms or adapt state-of-the-art methods to solve complex AI challenges.
Translate research into production-ready prototypes, proof-of-concepts, and reusable AI components.
Collaborate closely with cross-functional teams and external partners to understand technical requirements, constraints, business objectives, and delivery priorities.
Mentor machine learning engineers and researchers, providing technical guidance and supporting their professional development.
Contribute to technical papers, research publications, conference presentations, and knowledge-sharing initiatives.
Promote best practices in machine learning, software engineering, experimental rigour, reproducibility, and responsible AI development.
Communicate technical concepts, findings, and trade-offs effectively to both technical and non-technical stakeholders.
What We’re Looking For
Required Qualifications
Master’s degree or PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a related discipline (or equivalent industry experience).
5+ years of experience in machine learning, artificial intelligence, or a related field.
Demonstrated expertise in applied AI or machine learning, with experience in one or more of the following: Privacy-preserving machine learning, Generative AI, Large Language Models (LLMs), Federated Learning.
Strong understanding of modern machine learning techniques, their assumptions, strengths, limitations, and practical applications.
Excellent Python development skills, including experience with ML frameworks, version control, CI/CD pipelines, and reproducible experimentation.
Experience preparing, managing, and organising, augmenting and engineering complex datasets for AI and machine learning applications.
Experience across the complete machine learning lifecycle, from problem framing, research and prototyping through evaluation, deployment, and production integration.
Ability to produce high-quality technical documentation, research publications, or technical reports.
Experience mentoring or leading junior engineers or researchers.
Strong communication skills with the ability to explain complex technical concepts and trade-offs to diverse audiences.
Ability to thrive in fast-paced, collaborative environments with multiple stakeholders.
Nice to Have
Hands-on experience with privacy-enhancing technologies, including differential privacy, secure computation, federated learning, or membership inference resistance.
Experience developing or evaluating Generative AI systems, LLMs, agentic AI solutions, or evaluation methodologies for frontier AI.
Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
Familiarity with MLOps tools and modern ML deployment practices.
Contributions to open-source AI projects or published research in relevant fields.
Experience working across industry, academia, or government research collaborations.
Consulting or client-facing AI delivery experience.
Experience working within regulated industries such as financial services, insurance, healthcare, or the public sector.
Familiarity with agentic AI architectures, Model Context Protocol (MCP), Agent-to-Agent (A2A) communication, or inference-time privacy techniques.
Experience using modern AI-assisted development and research tools such as GitHub Copilot, Claude, Claude Code, or similar platforms.

Work arrangement
No