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Machine Learning Petroleum Engineer Jobs in Riverside, CA

PDS Health

Sr Engineer, AI Solutions

Irvine, CA · On-site

$130K - $168K/yr

Design and implement AI/Machine Learning (ML) solutions across domains such as computer vision and ... Mentor junior engineers in AI/Machine Learning (ML) fundamentals, coding standards, and deployment ...

PDS Health

Sr Engineer, AI Solutions

Irvine, CA · On-site

$130K/yr

The Senior Engineer, AI Solutions collaborates with cross-functional teams to design, develop, and ... Design and implement AI/Machine Learning (ML) solutions across domains such as computer vision and ...

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site +1

$131K - $173K/yr

You will work closely with machine learning engineers, robotics engineers, and infrastructure teams to ensure reliable training, evaluation, deployment, and monitoring of ML models. This is an ...

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site

$131K - $173K/yr

You will work closely with machine learning engineers, robotics engineers, and infrastructure teams to ensure reliable training, evaluation, deployment, and monitoring of ML models. This is an ...

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site +1

$131K - $173K/yr

You will work closely with machine learning engineers, robotics engineers, and infrastructure teams to ensure reliable training, evaluation, deployment, and monitoring of ML models. This is an ...

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site +1

$131K - $173K/yr

You will work closely with machine learning engineers, robotics engineers, and infrastructure teams to ensure reliable training, evaluation, deployment, and monitoring of ML models. This is an ...

Reply

AI/ML Engineer (AWS)

Irvine, CA · Remote

$120K - $155K/yr

As a Senior Developer, you will play a key role in building and evolving ML/AI applications and ... Create, evaluate, and deploy machine learning solutions, including applications powered by large ...

Qcells

Applied AI Engineer

Irvine, CA

$160K - $190K/yr

Train, fine-tune, validate, and optimize machine learning models for performance, scalability, and ... Collaborate with data engineers to collect, preprocess, and clean structured and unstructured data ...

New

Arch Telecom

AI Engineer

Irvine, CA · On-site

$121K - $145K/yr

We are looking for a motivated and innovative AI Engineer with 4 yrs of full-time experience in Machine Learning and AI Automation to join our growing team at Arch Telecom. This role is ideal for ...

Arch Telecom

AI Engineer

Irvine, CA · On-site

$130K - $140K/yr

We are looking for a motivated and innovative AI Engineer with 4 yrs of full-time experience in Machine Learning and AI Automation to join our growing team at Arch Telecom. This role is ideal for ...

Corvel

Data Scientist II

Irvine, CA · On-site +1

$82K - $127K/yr

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field; Master's preferred * 2-5+ years of experience in data science, machine learning, or ...

Corvel

Data Scientist II

Irvine, CA · On-site +1

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field; Master's preferred * 2-5+ years of experience in data science, machine learning, or ...

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

Machine Learning Petroleum Engineer information

See Riverside, CA salary details

$32.9K

$134.3K

$201.9K

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

As of Jun 6, 2026, the average yearly pay for machine learning petroleum engineer in Riverside, CA is $134,341.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,900.00 and $161,700.00 per year, depending on experience, location, and employer.

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 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, and why are they important?

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.
What are popular job titles related to Machine Learning Petroleum Engineer jobs in Riverside, CA? For Machine Learning Petroleum Engineer jobs in Riverside, CA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Petroleum Engineer jobs in Riverside, CA look for? The top searched job categories for Machine Learning Petroleum Engineer jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Machine Learning Petroleum Engineer jobs? Cities near Riverside, CA with the most Machine Learning Petroleum Engineer job openings:
Machine Learning Petroleum Engineer jobs near you

Staff ML Systems Engineer, Distributed Systems

FieldAI

Irvine, CA • On-site

Full-time

Posted 7 days ago

Job description

Job Summary:
FieldAI is a company that specializes in building reliable, field-ready AI systems for robotics. They are seeking a Senior / Staff ML Systems Engineer to architect and build distributed infrastructure for large-scale machine learning workflows, focusing on scalable systems that support data processing and model training.
Responsibilities:
• Design and build scalable distributed machine learning pipelines across data processing, model training, evaluation, and post-processing workflows.
• Architect distributed execution systems, including parallelization strategies, workload scheduling, resource allocation, and fault tolerance mechanisms.
• Develop reusable abstractions, frameworks, and libraries that simplify distributed pipeline development.
• Optimize performance across distributed CPU and GPU environments, improving throughput, utilization, and reliability.
• Design systems that effectively manage data partitioning, memory utilization, serialization overhead, and compute efficiency.
• Partner closely with ML engineers, data engineers, and infrastructure teams to productionize research workflows and enable large-scale model development.
• Establish best practices and engineering standards for distributed machine learning infrastructure.
• Evaluate and guide decisions around distributed computing frameworks, infrastructure technologies, and system design trade-offs.
• Improve observability, debugging, monitoring, and operational tooling for distributed systems at scale.
Qualifications:
Required:
• 5+ years of experience building distributed systems, backend infrastructure, machine learning platforms, or large-scale data processing systems.
• Strong Python programming skills, including experience with concurrency, performance optimization, and systems development.
• Experience with distributed computing frameworks such as Ray, Spark, Dask, Flink, or similar technologies.
• Experience designing and scaling data pipelines or machine learning workflows.
• Strong system design skills with demonstrated expertise in scalability, reliability, and performance optimization.
• Experience diagnosing and resolving bottlenecks in distributed environments.
• Ability to work cross-functionally and drive technical decisions across multiple teams.
Preferred:
• Experience building infrastructure for machine learning training and inference systems.
• Familiarity with modern ML frameworks such as PyTorch or TensorFlow.
• Experience with multi-node or multi-GPU training architectures, including DDP, FSDP, DeepSpeed, or similar technologies.
• Experience operating Kubernetes-based infrastructure and large-scale cloud systems.
• Deep understanding of distributed systems concepts including data locality, serialization costs, scheduling, and resource management.
• Experience with distributed debugging, observability, and workflow orchestration platforms.
• Proven ability to establish technical direction and influence architecture across organizations.
Company:
FieldAI is the general-purpose brain making robots autonomous in complex, risky, real-world environments. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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