Staff Machine Learning Engineer

Key Responsibilities

• Provide technical leadership in the design and architecture of large-scale, production-grade ML systems and platforms across the organisation.
• Own end-to-end delivery of complex ML solutions — from scientific problem framing and algorithm design through to deployment, operationalisation, and product delivery.
• Apply advanced machine learning science to develop novel algorithms and models, ensuring they are rigorously validated and deployed as scalable, reliable, production-grade products.
• Bridge the gap between scientific research and enterprise deployment — taking ML innovations from experimentation through to productised, maintainable solutions that deliver measurable value.
• Drive engineering excellence across ML systems, including CI/CD, testing, observability, reliability, and MLOps best practices.
• Define technical standards, patterns, and best practices for ML engineering and applied ML science across teams.
• Lead complex, multi-team technical initiatives and influence organisational direction through technical authority.
• Evaluate and integrate emerging approaches — including generative AI, Agentic AI, advanced optimisation, and scientific computing — into scalable solutions.
• Contribute to and shape internal ML platforms, reusable frameworks, and shared scientific computing capabilities.
• Mentor senior engineers and data scientists, raising the technical bar across the discipline.
• Partner with business and scientific customers to shape ML strategy and identify high-value opportunities.
• Present technical strategies, architectural decisions, and outcomes to senior leadership.

Qualifications

• Essential
• MSc or PhD degree in a quantitative field (e.g. Computer Science, Mathematics, Physics, Engineering, or related discipline).
• Hands-on experience (typically 8+ years) designing, prototyping, productionising, and scaling complex ML systems in production environments.
• Deep and demonstrable expertise in machine learning algorithms, statistical modelling, optimisation techniques, and scientific computing — with a proven track record of applying these to deliver production-grade products.
• Strong software engineering and system design expertise, including distributed systems, scalable architectures, and API design.
• Advanced programming experience in languages such as Python, Go, Java, or C++.
• Advanced SQL knowledge.
• Strong experience with MLOps, production ML systems, model lifecycle management, and monitoring.
• Experience working with large-scale data systems and distributed computing frameworks (e.g. Spark, Hadoop).
• Knowledge of experimental design, scientific methodology, and analysis.
• Strong customer management and proven ability to influence large organisations without direct authority.
• Demonstrated ability to lead through technical excellence and deliver high-impact, organisation-wide outcomes.
• Continuous learning and improvement approach.
• Desired
• Deep experience in applied machine learning science — including developing novel algorithms and translating scientific research into deployable, production-grade ML products.
• Experience applying AI/ML to scientific, engineering, or R&D workflows — encompassing experimentation, simulation, optimisation, physics-informed modelling, and autonomous scientific workflows.
• Strong experience with generative AI (LLMs, RAG, multimodal systems) and their deployment in production.
• Experience designing or deploying Agentic AI systems — including autonomous agents, tool use, multi-agent orchestration, reasoning workflows, and agent-driven scientific discovery.
• Proven track record of innovation through publications in peer-reviewed venues, invention disclosures (IDFs), patents, or open-source contributions in machine learning or AI.
• Experience building ML platforms, reusable scientific computing frameworks, or internal tooling that accelerates delivery across teams.
• Familiarity with model interpretability, uncertainty quantification, and advanced experimental frameworks.
• No prior experience in the energy industry required.

What We Offer

• [Complete by TA / HR with local benefits and compensation details]
• Competitive compensation and benefits package.
• Opportunity to lead and shape the future of ML and AI at one of the world's largest energy companies.
• A culture that values scientific rigour, engineering excellence, and continuous learning.
• Hybrid working arrangements and a commitment to work-life balance.
• Career development pathways in a world-class technology organisation.

Equal Opportunity Employer

Why join bp

Our commitment to diversity, equity and inclusion