Senior Machine Learning Engineer

Responsibilities

• Design, build, and maintain scalable ML systems and data pipelines for production, applying CI/CD, testing, monitoring, and observability.
• Translate ML science into novel algorithms and models delivered as reliable, scalable products from experimentation through production and operations.
• Develop impactful ML products using statistical modeling, deep learning, and AI techniques across operational, scientific, and R&D domains.
• Convert complex scientific and business problems into clearly scoped ML solutions delivering actionable insights and deployable capabilities.
• Architect and optimize ML systems for performance, scalability, and reliability in production environments.
• Collaborate closely with data scientists, data engineers, software engineers, and domain experts within cross-disciplinary teams.
• Follow and advocate for engineering and data science guidelines including design reviews, unit testing, monitoring, alerting, code reviews, and documentation.
• Present technical results, trade-offs, and product outcomes to peers and senior stakeholders.
• Contribute to improving developer velocity, engineering standards, and shared tooling across teams.
• Mentor junior team members and contribute to the technical growth of the wider group.

Requirements

• An MSc or PhD in a quantitative field (e.g., Computer Science, Mathematics, Physics, Engineering) or equivalent experience.
• Typically 5+ years of hands-on experience designing, prototyping, productionizing, maintaining, and scaling ML and data science products in sophisticated environments.
• Proven expertise in ML algorithms, statistical modeling, and optimization, with a track record of production-grade solutions.
• Practical knowledge of data science and ML toolchains across the full data and model lifecycle.
• Solid mathematical foundations in statistics, machine learning, and scientific computing.
• Proficient in one or more object-oriented languages such as Python, Go, Java, or C++.
• Advanced SQL skills.
• Experience with modern ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring.
• Understanding of experimental design and scientific methodology.
• Customer-focused and pragmatic, delivering value quickly while maintaining rigor and attention to detail.
• Strong stakeholder management and ability to influence across teams and organizations.
• Commitment to continuous learning and improvement.
• Experience with big data technologies such as Hadoop, Hive, and Spark.
• Experience with generative AI, large language models, or retrieval-augmented generation (RAG).
• Familiarity with agentic AI concepts, autonomous agents, tool use, and orchestration frameworks.
• Experience applying ML/AI to scientific or R&D workflows with emphasis on deployable ML products from research (e.g., simulation, optimization, physics-informed models).
• Knowledge of model interpretability, uncertainty quantification, and advanced experimental methodologies.
• Proven record of publications, invention disclosures (IDFs), or patents in ML or AI.
• No prior energy industry experience required.

Technologies

• Python
• Go
• Java
• C++
• SQL
• Hadoop
• Hive
• Spark

Benefits

• Competitive compensation and benefits package.
• Opportunity to work on cutting-edge ML and AI problems at global scale.
• A culture that values scientific rigor, engineering excellence, and continuous learning.
• Hybrid working arrangements with a focus on work-life balance.
• Career development pathways within a world-class technology organization.

Travel

Negligible travel should be expected with this role.

Relocation Assistance

This role is not eligible for relocation.

Remote Type

Hybrid of office and remote working.