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About This Role
Entity:
Technology
Job Family Group:
IT\&S Group
Job Description:
About us
Our purpose is to bring together people, energy and markets to power and navigate a changing world. In a time of constant change and possibility we need new talent to pursue commercial opportunities, fueled by world\-class insight and expertise. We’re always striving for more innovative digital solutions, sustainable outcomes and closer collaboration across our company and beyond, and you could be part of that too. Together we continue to grow as the world’s leading energy company!
Role Summary
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.
Key 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.
Qualifications
Essential
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.
Desired
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.
What We Offer
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.
Equal Opportunity Employer
bp is an equal opportunity employer. We believe that diversity and inclusion drive innovation and are essential to our success. We welcome applications from all qualified individuals regardless of race, colour, religion, gender, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected characteristic.
We are committed to making reasonable adjustments for candidates with disabilities or long\-term conditions. If you require any adjustments during the recruitment process, please let us kn
Why join bp:
At bp, we support our people to learn and grow in a diverse and challenging environment. We believe that our team is strengthened by diversity. We are committed to encouraging an inclusive environment in which everyone is respected and treated fairly.
There are many aspects of our employees’ lives that are meaningful, so we offer benefits to enable your work to fit with your life. These benefits can include flexible working options, a generous paid parental leave policy, and excellent retirement benefits, among others!
Travel Requirement
Negligible travel should be expected with this role
Relocation Assistance:
This role is not eligible for relocation
Remote Type:
This position is a hybrid of office/remote working
Skills:
Cloud Platforms, Cloud Platforms, Collaboration, Communication, Configuration management and release, Continuous deployment and release, Creating a high performing team, Database Design, Digital Project Management, Documentation and knowledge sharing, Emerging technology monitoring, Facilitation, Information Security, Mentoring, Metrics definition and instrumentation, NoSql data modelling, Problem Solving, Relational Data Modelling, Risk Management, Scripting, Secure development, Service operations and resiliency, Software Design and Development, Solution Architecture, Source control and code management {\+ 5 more}
Legal Disclaimer:
We are an equal opportunity employer. We do not discriminate on the basis of protected characteristics like race, religion, color, sex, national origin, sexual orientation, veteran status or disability status. Individuals with an accessibility need may request an adjustment/accommodation related to bp’s recruiting process (e.g., accessing the job application, completing required assessments, participating in telephone screenings or interviews, etc.). If you would like to request an adjustment/accommodation related to the recruitment process, please contact us.
If you are selected for a position and depending upon your role, your employment may be contingent upon adherence to local policy. This may include pre\-placement drug screening, medical review of physical fitness for the role, and background checks.
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Disclaimer
This is a high\-level summary only of terms and current discretionary benefits applicable to certain roles. Some rewards, benefits and policies are at managers’ discretion and vary depending on where you work within the business. All terms subject to contract and all discretionary benefits subject to policy and eligibility.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At bp, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
bp AI Hiring
bp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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