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Machine Learning Petroleum Engineer Jobs in Austin, TX

Avride

Machine Learning Engineer

Austin, TX · On-site

Avride develops autonomous vehicle and delivery robot technology, and they are seeking an experienced Machine Learning Engineer to enhance their autonomous systems. The role involves developing and ...

Avride

Machine Learning Engineer

Austin, TX

About the role We are looking for an experienced Machine Learning Engineer with a strong background in developing and deploying modern machine learning solutions for complex real-world challenges. In ...

Avride

Machine Learning Engineer

Austin, TX · On-site

About the role We are looking for an experienced Machine Learning Engineer with a strong background in developing and deploying modern machine learning solutions for complex real-world challenges. In ...

Q2 India

Machine Learning Engineer

Austin, TX · On-site

$100 - $130/hr

Machine Learning Engineer page is loaded## Machine Learning Engineerlocations: Austin, TXtime type: Full timeposted on: Posted Yesterdayjob requisition id: REQ-12438# **As passionate about our people ...

New

Q2

Machine Learning Engineer

Austin, TX · On-site

SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and scaling AI solutions, while collaborating with cross-functional teams to advance machine learning ...

Q2 Software, Inc.

Machine Learning Engineer

Austin, TX · On-site

SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and scaling AI solutions, while collaborating with cross-functional teams to advance machine learning ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

PTP

Machine Learning Engineer - REMOTE

Austin, TX · On-site +1

We are looking for a Machine Learning Engineer to help us design and deliver CX solutions that provide our clients with a beautiful customer journey that achieves results. At PTP we value aptitude ...

Rokt

Senior Machine Learning Engineer

Austin, TX · On-site

$335K - $400K/yr

We are hiring Senior Machine Learning Engineers We are hiring engineers with significant expertise in both machine learning and software engineering. You will be working with our engineering and ...

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All JobsMachine Learning Petroleum Engineer JobsMachine Learning Petroleum Engineer Jobs in Austin, TX

Machine Learning Petroleum Engineer information

See Austin, TX salary details

$31.2K

$127.6K

$191.8K

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

As of Aug 7, 2026, the average yearly pay for machine learning petroleum engineer in Austin, TX is $127,637.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,600.00 and $153,600.00 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?

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 Austin, TX? For Machine Learning Petroleum Engineer jobs in Austin, TX, the most frequently searched job titles are:
What job categories do people searching Machine Learning Petroleum Engineer jobs in Austin, TX look for? The top searched job categories for Machine Learning Petroleum Engineer jobs in Austin, TX are:
What cities near Austin, TX are hiring for Machine Learning Petroleum Engineer jobs? Cities near Austin, TX with the most Machine Learning Petroleum Engineer job openings:
Machine Learning Petroleum Engineer jobs near you
Infographic showing various Machine Learning Petroleum Engineer job openings in Austin, TX as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $127,637 per year, or $61.4 per hour.

Machine Learning Engineer

Avride

Austin, TX • On-site

Full-time

Re-posted 14 days ago

Job description

Job Summary:
Avride develops autonomous vehicle and delivery robot technology, and they are seeking an experienced Machine Learning Engineer to enhance their autonomous systems. The role involves developing and optimizing machine learning models, managing large-scale datasets, and collaborating with cross-functional teams to integrate solutions into real-world applications.
Responsibilities:
• Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ensure efficiency, scalability, and robustness. This may include developing models for understanding a self-driving vehicle’s surroundings or predicting the intentions of other road users.
• Curate and Manage Large-Scale Datasets: Oversee data collection, preprocessing, and augmentation to maintain high-quality datasets for training and evaluation.
• Enhance and Maintain Training Pipelines: Develop efficient workflows for training, validation, and testing, incorporating distributed training, hyperparameter tuning, and automated monitoring.
• Improve Model Deployment and Efficiency: Optimize inference performance, model compression, and deployment across various hardware platforms.
• Explore and Apply Cutting-Edge ML Techniques: Stay up to date with advancements in deep learning and experiment with novel approaches to improve model performance.
• Collaborate with Cross-Functional Teams: Work closely with researchers, software engineers, and robotics experts to integrate machine learning solutions into real-world autonomous systems.
Qualifications:
Required:
• Strong understanding of fundamental machine learning algorithms and neural network techniques.
• Expertise in at least one modern machine learning domain, such as computer vision, large language models, or generative AI.
• At least three years of experience developing neural network-based algorithms, including data collection, training, and deployment.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, along with PySpark, NumPy, and SciPy.
• Working knowledge of C++ and SQL.
• Ability to quickly absorb new concepts by reviewing research papers, technical reports, and documentation.
• Strong collaboration and communication skills, with the ability to align technical work with business objectives and drive results.
Preferred:
• Advanced degree in Computer Science, Machine Learning, Robotics, or a related field.
• Experience developing ML algorithms for autonomous vehicles or robotics applications.
• Familiarity with neural network deployment and optimization tools such as triton, TensorRT, or similar frameworks.
• Proven ability to set and achieve mid- and long-term goals, prioritize tasks, and meet deadlines independently.
• Experience working in cross-functional teams within a multidisciplinary environment.
• Publications in top-tier ML conferences or contributions to patent applications or ML-related open-source projects.
Company:
Avride is a developer and operator of autonomous vehicles and delivery robots. Founded in 2017, the company is headquartered in Austin, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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