Senior Machine Learning Engineer

bp offers a path to tackle ambitious ML and AI challenges at global scale, backed by a culture that prioritizes scientific rigor, engineering excellence, and continuous learning. This hybrid role in Houston invites an accomplished machine learning engineer to design, build, and deploy production-grade systems across NLP, optimization, simulation, and generative AI, collaborating with cross-disciplinary teams to deliver deployable ML products that drive real impact.

Benefits

• Competitive compensation and a comprehensive benefits package.
• Opportunity to work on advanced ML and AI problems at global scale.
• A culture that emphasizes scientific rigor, engineering excellence, and lifelong learning.
• Hybrid work arrangements that support work-life balance.
• Career development pathways within a world-class technology organization.

Responsibilities

• Design, build, and maintain scalable, production-grade ML systems and pipelines with modern engineering practices such as CI/CD, testing, monitoring, and observability.
• Translate ML science into reliable, scalable products that progress from experimentation through production delivery and operational use.
• Develop impactful ML products using statistical modeling, deep learning, and AI techniques across operational, scientific, and R&D domains.
• Turn complex scientific and business problems into well-scoped ML solutions that yield 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.
• Advocate for engineering and data science guidelines, including design reviews, unit testing, monitoring and 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.
• Mentor junior team members and contribute to the technical growth of the wider team.

Requirements

• 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 or data science products in sophisticated environments.
• Strong expertise in ML algorithms, statistical modeling, and optimization with a track record of delivering production-grade solutions.
• Applied knowledge of the data science lifecycle and ML tooling across all stages.
• Solid understanding of statistics, machine learning foundations, and scientific computing.
• Proficiency in one or more object-oriented languages (Python, Go, Java, C++).
• Advanced SQL abilities.
• Experience with modern ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring.
• Experience with experimental design and scientific methodology.
• Customer-focused and pragmatic, delivering value swiftly while maintaining rigor and attention to detail.
• Strong stakeholder management and the ability to influence across teams and organizations.
• Commitment to continuous learning and improvement.
• Experience with big data technologies (Hadoop, Hive, Spark).
• Experience with generative AI, large language models, or retrieval-augmented generation (RAG).
• Exposure to Agentic AI concepts, including autonomous agents and orchestration frameworks.
• Experience applying ML/AI to scientific or R&D workflows, focusing on deployable ML products from research (e.g., simulation, optimization, physics-informed models).
• Familiarity with model interpretability, uncertainty quantification, and advanced experimental methodologies.
• Proven publications, invention disclosures, or patents in ML or AI.
• Energy industry experience is not required.

Technologies

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

Location and Work Model

Location: Houston, TX. This is a hybrid role combining in-person and remote work. The position requires negligible travel and relocation assistance is not provided.