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Machine Learning Petroleum Engineer Jobs in Berkeley, CA

Gotion, Inc.

Machine Learning Engineer

Fremont, CA · On-site

Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains ...

Eve

Machine Learning Engineer

San Francisco, CA · On-site

We're hiring an Machine Learning Engineer as the volume and complexity of legal AI workflows in our system scale rapidly. As more firms rely on Eve to automate high-stakes legal work - from intake ...

Human Archive

Machine Learning Engineer

San Francisco, CA · On-site

$100K - $150K/yr

The Opportunity As a Machine Learning Engineer, you'll work on multimodal perception, VLA training, robotics post-training, and downstream policy evaluation. This is a hands-on role at the ...

Canals

Machine Learning Engineer

San Francisco, CA · Remote

We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining high quality, owning end-to-end ML pipelines while shaping product features that deliver real-world ...

Plenful

Machine Learning Engineer

San Francisco, CA · On-site

About the Role We're looking for a Machine Learning Engineer to design, build, and deploy production-grade ML systems that power the next generation of Plenful's AI platform. You'll own the end-to ...

Blank Bio

Machine Learning Engineer

San Francisco, CA · On-site

The Machine Learning Engineer will be responsible for scaling models, building training infrastructure, and ensuring reproducibility across large-scale biological datasets while collaborating with ...

Goodfire

Machine Learning Engineer

San Francisco, CA

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

OpenReq

Machine Learning Engineer

San Francisco, CA · On-site

As a Machine Learning Engineer, you will shape the technical direction of the company by automating the ML life-cycle and engaging directly with customers while contributing to the architectural ...

RXinsider LTD.

Machine Learning Engineer

San Francisco, CA · On-site

$150 - $200/hr

About The Role We're looking for a Machine Learning Engineer to design, build, and deploy production‑grade ML systems that power the next generation of Plenful's AI platform. You'll own the ...

Goodfire

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

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Showing results 41-60

All JobsMachine Learning Petroleum Engineer JobsMachine Learning Petroleum Engineer Jobs in Berkeley, CA

Machine Learning Petroleum Engineer information

See Berkeley, CA salary details

$38.6K

$157.7K

$236.9K

How much do machine learning petroleum engineer jobs pay per year?

As of Sep 7, 2026, the average yearly pay for machine learning petroleum engineer in Berkeley, CA is $157,670.00, according to ZipRecruiter salary data. Most workers in this role earn between $124,300.00 and $189,800.00 per year, depending on experience, location, and employer.

What is a machine learning petroleum engineer?

A Machine Learning Petroleum Engineer is a specialist who combines expertise in petroleum engineering with machine learning and data science techniques. They use advanced algorithms and data analytics to optimize oil and gas exploration, drilling, production, and reservoir management. Their work helps improve decision-making, reduce operational costs, and increase efficiency by analyzing large datasets from various sources such as sensors, seismic data, and production logs. These professionals often work closely with geoscientists, data engineers, and other stakeholders in the energy sector.

What are the key skills and qualifications needed to thrive as a machine learning petroleum engineer?

To thrive as a Machine Learning Petroleum Engineer, you need a strong background in petroleum engineering, programming (such as Python or R), and applied machine learning, usually supported by a relevant engineering degree. Familiarity with data analysis platforms, machine learning frameworks (like TensorFlow or Scikit-learn), and petroleum industry software (such as Petrel or Eclipse) is essential. Strong analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for integrating technical insights with business goals. These competencies enable the effective application of data-driven solutions to optimize exploration, production, and operational efficiency in the energy sector.

How does a machine learning petroleum engineer typically collaborate with geoscientists and drilling teams to optimize oil and gas production?

A Machine Learning Petroleum Engineer works closely with geoscientists and drilling teams by integrating data-driven models into exploration and production workflows. They analyze geological, seismic, and operational data to develop predictive algorithms that identify optimal drilling locations, forecast reservoir performance, and improve recovery rates. Regular collaboration involves translating complex data insights into actionable recommendations that guide drilling strategies and inform real-time decisions, ensuring all teams are aligned to maximize efficiency and safety. This multidisciplinary approach fosters continuous learning and innovation across teams.

What is the difference between Machine Learning Petroleum Engineer vs Reservoir Engineer?

AspectMachine Learning Petroleum EngineerReservoir Engineer
Required CredentialsBachelor's/Master's in Petroleum Engineering, Data Science, or related fields; knowledge of machine learningBachelor's/Master's in Petroleum Engineering or Geosciences; strong understanding of reservoir simulation
Work EnvironmentData analysis, modeling, software development in oil & gas companiesReservoir modeling, field development planning in oil & gas operations
Industry UsageApplying machine learning to optimize extraction, predict reservoir behaviorEstimating reservoir properties, managing production strategies

The Machine Learning Petroleum Engineer focuses on integrating data science and machine learning techniques to optimize oil extraction processes, while the Reservoir Engineer specializes in modeling and managing subsurface reservoirs to maximize recovery. Both roles are vital in the oil & gas industry but differ in their core skills and daily tasks.

What are popular job titles related to Machine Learning Petroleum Engineer jobs in Berkeley, CA?

For Machine Learning Petroleum Engineer jobs in Berkeley, CA, the most frequently searched job titles are:

What cities near Berkeley, CA are hiring for Machine Learning Petroleum Engineer jobs?

Cities near Berkeley, CA with the most Machine Learning Petroleum Engineer job openings:

Machine Learning Petroleum Engineer jobs near you

Machine Learning Engineer

Gotion, Inc.

Fremont, CA • On-site

Full-time

Re-posted 15 hours ago

Job description

Gotion Inc. is based in Silicon Valley, CA, currently building a Manufacturing facility in Manteno, IL and has R&D centers in Ohio, China, Japan and Europe. We innovate in the next generation electric vehicle and energy storage technologies (lithium batteries and related systems) with the aim to accelerate electrified transportation and achieve sustainable development. Gotion is powered by a leading power battery technology company that provides solutions for vehicles including the world's first mass commercial e-bus route.
Gotion is a career destination - we are not simply attempting to just fill another job, but to pursue a dream of global green energy together! We offer outstanding opportunities to individuals seeking an exciting and challenging working environment. Everyone is highly valued and plays a vital role in the growth of our organization.
About The TeamThe Product Development Team at Gotion Illinois New Energy Inc. focuses on the design and development of advanced battery products for next-generation energy storage system (ESS) and electric vehicle (EV) applications. We lead the full product development cycle, integrating mechanical, electrical, thermal, and control system to create high-performance battery solutions, along with comprehensive system integration, validation, and certification activities.
Role Summary
We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains such as battery technology, energy systems, or related physical sciences. This is a fully on-site role based in Manteno, IL focused on building innovative ML models from the ground up.
You will collaborate closely with cross-disciplinary R&D teams to develop and deploy machine learning solutions that address real-world challenges in advanced materials, electrochemical systems, and high-throughput data environments.
Essential Duties and Responsibilities:
  • Design and implement novel machine learning and deep learning models tailored to internal research needs
  • Prototype and evaluate state-of-the-art algorithms, including Transformers, LLMs, and hybrid model architectures
  • Conduct rigorous experimentation, benchmarking, and ablation studies
  • Collaborate with battery scientists and domain experts to incorporate physical constraints or scientific priors into modeling
  • Contribute to internal documentation and present research outcomes to technical and leadership teams
  • Track and integrate advances from the ML research community to ensure technical excellence

Required Qualifications:
  • Ph.D. (preferred) or M.S. in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field
  • Demonstrated expertise in model development, optimization, and algorithmic innovation
  • Proficiency in Python and ML libraries/frameworks such as PyTorch, TensorFlow, etc.
  • Solid understanding of learning theory concepts such as regularization, generalization, loss functions, and evaluation metrics
  • Experience working with scientific or time-series datasets, especially in battery, materials, or energy domains, is highly desirable
  • A publication record in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR) is a strong plus
  • Excellent communication, collaboration, and problem-solving skills in interdisciplinary environments

The US base salary range for this full-time position is $80,000.00 - $90,000.00 + 15% bonus + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Gotion Inc. is proud to be an equal opportunity employer. We are dedicated to fostering a diverse workforce that reflects the communities we serve, cultivating a culture of inclusion and belonging, and ensuring equal employment opportunities for all.
We provide equal opportunity to all individuals regardless of race, creed, color, religion, gender, sexual orientation, gender identity or expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related conditions (including breastfeeding), parental status, criminal histories consistent with legal requirements, or any other characteristic protected by law.
At Gotion Inc., we strive to create an environment where everyone feels valued, respected, and empowered to thrive.

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