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About This Role
AI/ML Engineer
Partnering with the best
Baker Hughes is a global energy technology company serving customers in more than 120 countries. We design, manufacture, and deliver industry-leading solutions that support energy extraction, production, and processing. Our commitment to innovation and operational excellence drives safer, cleaner, and more efficient energy solutions worldwide.
Fueling Your Passion
The Data, Analytics & AI team develops innovative data-driven products and AI solutions that power the Cordant suite of digital products. The AI/ML Engineer will work across data engineering, analytics, machine learning, and Agentic AI technologies to create scalable solutions that enable predictive maintenance, operational optimization, intelligent search, and AI-powered assistants. This role combines advanced analytics, Generative AI, and modern software engineering practices to help deliver the next generation of industrial intelligence solutions.
As an AI/ML Engineer your role will be to help build scalable data and AI solutions that enable predictive analytics, equipment intelligence, operational optimization, intelligent search, and AI-powered assistants. This role combines traditional machine learning, data engineering, Generative AI, and emerging agentic AI technologies to accelerate business value for industrial customers.
As an AI/ML Engineer, you will be responsible for:
- Develop AI, machine learning, and analytics solutions for the Cordant product suite.
- Design scalable data pipelines and AI workflows using structured and unstructured data.
- Develop Generative AI and Agentic AI applications, including copilots, assistants, and intelligent search.
- Implement RAG, agent orchestration, prompt engineering, and LLM evaluation frameworks.
- Deploy, monitor, and optimize AI/ML solutions using MLOps and LLMOps best practices.
- Collaborate with product teams, domain experts, and software engineers to deliver customer value.
- Ensure solutions are secure, scalable, governed, and aligned with software engineering standards.
To be successful in this role you will have:
- A Bachelor's degree in computer science or “STEM” Majors (Science, Technology, Engineering and Math)
- 2+ years of experience developing AI, machine learning, analytics, or data-driven solutions.
- Hands-on experience with Generative AI, LLMs, RAG, vector databases, cloud platforms (Azure/AWS), and data engineering pipelines.
- Knowledge of MLOps, software engineering, best practices, APIs, microservices, and Agile development, with strong analytical and problem-solving skills.
- Proficiency in Python, SQL, and modern AI/ML frameworks such as PyTorch, TensorFlow, Scikit-learn, LangChain, or LangGraph.
Work in a Way That Works for You
This role requires flexibility to support customer engagements across the globe. Travel expectations include commitment to travel 10%, or as required based on customer and business needs.
Working with us
Our people are at the heart of what we do at Baker Hughes. We know we are stronger when our people are developed, engaged, and empowered to bring their authentic selves to work. We invest in wellbeing, capability development, and leadership growth across all levels.
Working for you
Our innovations have transformed the energy industry for over a century. To continue progressing, we look for individuals who embrace change and contribute to building the future.
What You Can Expect
- Flexible working opportunities with travel expectations
- Contemporary work-life balance policies and wellbeing activities
- Comprehensive private medical care options
- Safety net of life insurance and disability programs
- Tailored financial programs
- Education assistance
- Generous parental leave
- Mental health resources
- Dependent care support
- Additional elected or voluntary benefits
This position requires valid work authorization in the country of employment. The employer is unable to sponsor work authorization for this role at any time.
You will be eligible to participate in Company-sponsored benefit programs, including health & welfare programs and the Thrift Plan (401k). You will have a choice of coverage options that best suit your needs. Coverage options and contribution amounts are related to your benefit elections, base salary level, and specific requirements of each of these plans.
This position is eligible for our comprehensive and competitive benefits package, which can be found here, and is further eligible for additional forms of compensation such as bonuses subject to the terms of the applicable benefit plans or policies.
The Baker Hughes internal title for this role is: Digital Technology Experienced Professional - Data and AnalyticsAbout Us: We are an energy technology company that provides solutions to energy and industrial customers worldwide. Built on a century of experience and conducting business in over 120 countries, our innovative technologies and services are taking energy forward – making it safer, cleaner and more efficient for people and the planet.
Join Us: Are you seeking an opportunity to make a real difference in a company that values innovation and progress? Join us and become part of a team of people who will challenge and inspire you! Let’s come together and take energy forward.
Baker Hughes Company is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Baker Hughes, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Baker Hughes AI Hiring
Baker Hughes 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, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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