Senior Machine Learning Engineer

bp • Houston, TX

13d • Hybrid

About The Position

We are looking for a Senior Machine Learning Engineer who combines deep machine learning expertise with strong software engineering discipline to design, build, and deploy production-grade ML and AI systems. This role goes beyond traditional ML engineering. You will apply machine learning science as a core discipline — developing novel algorithms and models that are not only experimentally validated but architected and deployed as scalable, reliable products. Whether it's advancing NLP, optimisation, simulation, or generative AI, you will deliver solutions that transition seamlessly from research to production and create measurable value. You will work as part of a cross-disciplinary team alongside data scientists, software engineers, data engineers, and domain experts — translating complex scientific and business problems into deployable ML products.

Requirements

• MSc or PhD degree or equivalent experience in a quantitative field (e.g. Computer Science, Mathematics, Physics, Engineering, or related discipline).
• Hands-on experience (typically 5+ years) designing, prototyping, productionizing, maintaining, and scaling ML/data science products in sophisticated environments.
• Strong and demonstrable expertise in machine learning algorithms, statistical modelling, and optimisation techniques — with a track record of applying these to build production-grade solutions.
• Applied knowledge of data science and ML tools across all stages of the data and model lifecycle.
• Thorough understanding of the mathematical foundations of statistics, machine learning, and scientific computing.
• Strong programming experience in one or more object-oriented languages (e.g. Python, Go, Java, C++).
• Advanced SQL knowledge.
• Experience with modern ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring.
• Knowledge of experimental design, analysis, and scientific methodology.
• Customer-centric and pragmatic mentality with a focus on value delivery and swift execution, while maintaining rigour and attention to detail.
• Strong stakeholder management and ability to influence across teams and organisations.
• Continuous learning and improvement mindset.

Nice To Haves

• Experience with big data technologies (e.g. Hadoop, Hive, Spark).
• Experience with generative AI, LLMs, or retrieval-augmented generation (RAG).
• Exposure to Agentic AI concepts, including autonomous agents, tool use, and orchestration frameworks.
• Experience applying machine learning and AI to scientific or R&D workflows — with emphasis on building deployable ML products from scientific research (e.g. simulation, optimisation, physics-informed models).
• Familiarity with model interpretability, uncertainty quantification, and advanced experimental methodologies.
• Proven record of publications, invention disclosures (IDFs), or patents in machine learning or AI.
• No prior experience in the energy industry required.

Responsibilities

• Design, build, and maintain scalable, production-grade machine learning systems and pipelines using modern engineering practices (CI/CD, testing, monitoring, observability).
• Apply machine learning science to develop novel algorithms and models that are deployed as reliable, scalable products — not limited to experimentation but extending through to production delivery and operational use.
• Build impactful ML products leveraging statistical modelling, deep learning, and AI techniques across operational, scientific, and R&D domains.
• Translate complex scientific and business problems into well-scoped ML solutions, delivering actionable insights and deployable capabilities.
• Architect and optimise ML systems for performance, scalability, and reliability in production environments.
• Collaborate closely with data scientists, data engineers, software engineers, and domain experts as part of cross-disciplinary teams.
• Adhere to and advocate for engineering and data science guidelines (technical design, design reviews, unit testing, monitoring & alerting, code reviews, documentation).
• Present technical results, trade-offs, and product outcomes to peers and senior interested parties.
• Actively contribute to improving developer velocity, engineering standards, and shared tooling.
• Mentor junior team members and contribute to the technical growth of the wider team.

Benefits

• Competitive compensation and benefits package.
• Opportunity to work on cutting-edge ML and AI problems at global scale.
• 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.
• flexible working options
• a generous paid parental leave policy
• excellent retirement benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior