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Reinforcement Learning Engineer Jobs in Oregon (NOW HIRING)

Nvidia

OR · On-site

Design and train innovative large-scale models-including generative, imitation, and reinforcement ... Strong programming skills in Python and proficiency with major deep learning frameworks.

Nvidia

OR

$125.40K - $172.80K/yr

Design and train innovative large-scale models-including generative, imitation, and reinforcement ... Strong programming skills in Python and proficiency with major deep learning frameworks.

Nvidia

OR

$129.40K - $175.80K/yr

Open-source leadership in deep learning, agentic AI systems, or reinforcement learning. * Experience with GPU programming and performance optimization. With competitive salaries and a generous ...

Nvidia

OR

Collaborate with research and engineering teams across all of NVIDIA to transfer research to ... Experience in foundation and diffusion models, reinforcement learning, agent learning, and applied ...

Netflix

OR

You will work in close partnership with our Machine Learning Engineers to bridge the gap between ... reinforcement learning to enhance reasoning, tool-use, and agentic workflows for interactive game ...

Nvidia

OR

This role involves a mix of consulting, hands-on engineering, and collaboration with internal and ... Understanding of large-scale model training, reinforcement learning or distributed simulation on ...

Nvidia

OR · On-site

$122.40K - $161.30K/yr

Publications or open-source leadership in deep learning, multi-agent systems, reinforcement ... If you're a creative and autonomous engineer with a real passion for technology, we want to hear ...

Merlin Labs

OR · On-site

You are an experienced engineer when it comes to all things perception for robotics and autonomous ... deep learning and reinforcement learning techniques. * Familiarity with ML frameworks such as ...

Nvidia

OR

Publications or open-source leadership in deep learning, multi-agent systems, reinforcement ... If you're a creative and autonomous engineer with a real passion for technology, we want to hear ...

Netflix

OR

$466K - $750K/yr

Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models * Evidence Personalization * Page Simulation for Better Offline Metrics at Netflix * RecSysOps As a software ...

Netflix

OR · On-site

$466K - $750K/yr

Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models Evidence Personalization Page Simulation for Better Offline Metrics at Netflix RecSysOps As a software engineer ...

Nvidia

OR

Ability to design and implement Reinforcement learning and post-training pipelines for LLM to improve model reasoning, safety, and instruction-following capabilities. * Strong programming skills with ...

Nvidia

OR

$63 - $83/hr

... Reinforcement Learning, inference optimization and model fine-tuning. We specialize on engineering new solutions to fit our customers needs by integrating their enterprise data sources into ...

Netflix

OR

$466K - $750K/yr

We are looking in all areas of machine learning, reinforcement learning, artificial intelligence ... with engineering and business teams. Solid research experience, in industry and/or academia ...

Netflix

OR

$466K - $750K/yr

We are looking in all areas of machine learning, reinforcement learning, artificial intelligence ... with engineering and business teams. * Solid research experience, in industry and/or academia ...

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Reinforcement Learning Engineer information

See Oregon salary details

$40.2K

$122.5K

$202.5K

How much do reinforcement learning engineer jobs pay per year?

As of May 31, 2026, the average yearly pay for reinforcement learning engineer in Oregon is $122,502.00, according to ZipRecruiter salary data. Most workers in this role earn between $87,800.00 and $160,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Reinforcement Learning Engineer, and why are they important?

To thrive as a Reinforcement Learning Engineer, you need a strong background in machine learning, mathematics (especially probability and statistics), and programming languages like Python, often supported by a relevant degree in computer science or engineering. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like OpenAI Gym), and cloud computing platforms is typically required. Problem-solving skills, creativity, and effective collaboration help set outstanding engineers apart in this field. These competencies enable the design and deployment of advanced RL solutions that address real-world challenges and drive innovation.

What are some common challenges faced by Reinforcement Learning Engineers when deploying models in real-world environments?

One of the main challenges Reinforcement Learning (RL) Engineers face is bridging the gap between simulation and real-world deployment. Models that perform well in controlled environments may struggle with unpredictable data, safety constraints, or limited feedback in production. Additionally, RL algorithms often require significant computational resources and careful tuning to avoid instability. Collaboration with domain experts and software engineers is essential to address these issues and ensure successful integration of RL solutions into existing systems.

What are Reinforcement Learning Engineers?

Reinforcement Learning Engineers are specialized professionals who design, develop, and implement algorithms based on reinforcement learning, a type of machine learning where agents learn to make decisions by receiving rewards or penalties. They work on building models that enable machines to learn optimal actions through trial and error in complex environments. Their responsibilities often include developing RL architectures, tuning hyperparameters, running simulations, and applying RL methods to real-world problems like robotics, gaming, or recommendation systems. RL Engineers typically have strong backgrounds in computer science, mathematics, and deep learning, along with experience in programming languages like Python and frameworks such as TensorFlow or PyTorch.

What is the difference between Reinforcement Learning Engineer vs Machine Learning Engineer?

AspectReinforcement Learning EngineerMachine Learning Engineer
CredentialsBachelor's/Master's in CS, AI, or related; experience with RL frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on RL applicationsTech companies, data-driven firms, AI departments across industries
Industry UsageSpecialized in RL projects like robotics, game AI, autonomous systemsBroader applications including predictive modeling, NLP, computer vision

Reinforcement Learning Engineers focus on developing algorithms that learn through interactions with environments, often in robotics or gaming. Machine Learning Engineers work on a wider range of models and applications. While both roles require strong programming and math skills, RL Engineers specialize in sequential decision-making, whereas ML Engineers handle diverse data-driven tasks across industries.

What are popular job titles related to Reinforcement Learning Engineer jobs in Oregon? For Reinforcement Learning Engineer jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Engineer jobs in Oregon look for? The top searched job categories for Reinforcement Learning Engineer jobs in Oregon are:
Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Oregon as of May 2026, with employment types broken down into 74% Full Time, and 26% Contract. Highlights an 100% In-person job distribution, with an average salary of $122,502 per year, or $58.9 per hour.
Deep Learning Senior Engineer, End-To-End Autonomous Driving

Deep Learning Senior Engineer, End-To-End Autonomous Driving

Nvidia

On-site

Full-time

Posted 7 days ago

Job description

At NVIDIA, we are seeking exceptional engineers to join our autonomous driving teamto design, implement, and deploy cutting-edge end-to-end autonomous driving systems, running on NVIDIA chips in mass-production vehicles. Our strategy has evolved fromAI 1.0 - building a driver from scratch - to AI 2.0 - teaching an intelligent agent to drive. This next phase leveragesLLMs, VLMs, and VLAsto bring unprecedented reasoning, planning capabilities, andinteractivity with the driving systemto autonomous vehicles and general robotics. Let's build the future of autonomy-together!

What You'll Be Doing:

  • Design and train innovative large-scale models-including generative, imitation, and reinforcement learning-to improve the planning and reasoning capabilities of our driving systems.

  • Build, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics applications.

  • Explore novel data generation and collection strategies to improve diversity and quality of training datasets.

  • Collaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability standards are met.

  • Integrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software.

What We Need to See:

  • Hands-on experience building LLMs, VLMs, or VLAs from scratchora proven track record as a top-tier coder passionate about autonomous systems.

  • Deep understanding of modern deep learning architectures and optimization techniques.

  • Proven record of deploying production-grade ML models for self-driving, robotics, or related fields at scale.

  • Strong programming skills in Python and proficiency with major deep learning frameworks.

  • Familiarity with C++ for model deployment and integration in safety-critical systems.

  • PhD with 4+ years, MS (or equivalent experience) with 6+years of relevantexperience in Computer Science,Computer Engineering, or a relatedtechnical field.

Ways to Stand Out from the Crowd:

  • Experience with LLM/VLM/VLA systems deployable to autonomous vehicles or general robotics.

  • Publications, open-source contributions, or competition wins related to LLM/VLM/VLA systems.

  • Deep understanding of behavior and motion planning in real-world AV applications.

  • Experience building and training large-scale datasets and models.

  • Proven ability to optimize algorithms for real-time performance in resource-constrained environments and strong track record of taking projects from concept to production deployment.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until March 26, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.#deeplearning
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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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