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Deep Reinforcement Learning Jobs (NOW HIRING)

Starfish

Software Developer

Colorado Springs, CO · On-site

The selected candidate will drive the design, development, and integration of innovative Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) capabilities into defense and mission ...

Centific

Applied Reinforcement Learning Engineer

Applied Reinforcement Learning Engineer Location: Palo Alto, CA or Seattle, WA (Hybrid/Remote ... This role requires deep expertise in both classical RL methodologies and modern LLM-based agent ...

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Deep Reinforcement Learning information

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$28.5K

$58.3K

$80K

How much do deep reinforcement learning jobs pay per year?

As of Jun 18, 2026, the average yearly pay for deep reinforcement learning in the United States is $58,347.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,500.00 and $68,000.00 per year, depending on experience, location, and employer.

What does a typical day look like for someone working in Deep Reinforcement Learning?

A typical day for a Deep Reinforcement Learning professional involves designing algorithms, running experiments, analyzing results, and optimizing models to improve performance. You may collaborate regularly with data scientists, software engineers, and domain experts to integrate RL solutions into larger systems or products. Tasks often include reading the latest research, contributing to code reviews, and documenting findings while troubleshooting technical challenges. This dynamic environment encourages continuous learning and teamwork, ensuring you stay at the forefront of AI innovation.

What is a Deep Reinforcement Learning job?

A Deep Reinforcement Learning (DRL) job involves researching, developing, and applying AI models that use reinforcement learning techniques combined with deep learning. Professionals in this role design algorithms that enable agents to learn optimal decision-making policies through trial and error. Common applications include robotics, game AI, autonomous systems, and financial modeling. This job typically requires expertise in machine learning, neural networks, and programming languages like Python, along with frameworks such as TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive in the Deep Reinforcement Learning position, and why are they important?

To thrive in Deep Reinforcement Learning, you need expertise in machine learning, programming (Python, TensorFlow, or PyTorch), and applied mathematics, often supported by an advanced degree in computer science or a related field. Familiarity with version control systems, cloud computing platforms, and relevant certifications in AI or data science are valuable assets. Strong problem-solving abilities, collaboration, and effective communication are important soft skills in this position. These skills are essential for developing, implementing, and iterating cutting-edge algorithms that solve complex real-world problems in dynamic environments.

More about Deep Reinforcement Learning jobs
What are the most commonly searched types of Deep Reinforcement Learning jobs? The most popular types of Deep Reinforcement Learning jobs are:
What states have the most Deep Reinforcement Learning jobs? States with the most job openings for Deep Reinforcement Learning jobs include:
What job categories do people searching Deep Reinforcement Learning jobs look for? The top searched job categories for Deep Reinforcement Learning jobs are:
Deep Reinforcement Learning jobs near you
Infographic showing various Deep Reinforcement Learning job openings in the United States as of June 2026, with employment types broken down into 88% Full Time, 11% Part Time, and 1% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $58,347 per year, or $28.1 per hour.
Machine Learning Engineer - Reinforcement Learning

Machine Learning Engineer - Reinforcement Learning

Pony.ai

Fremont, CA • On-site

Full-time

Posted 6 days ago

Job description

Job Summary:
Pony.ai is a global leader in autonomous mobility, recognized for its innovative technologies and services in the field. The role involves building scalable systems for training large generative models, implementing reinforcement learning methods, and shipping deep learning solutions to enhance self-driving behaviors.
Responsibilities:
• Build scalable systems for training and fine-tuning large generative models that produce realistic, informative driving behaviors for evaluation and scenario coverage.
• Implement and iterate on RL-style methods: algorithms, reward / preference objectives, and training setups suited to high-fidelity, insightful behaviors in simulation-aligned workflows (closed-loop evaluation mindset).
• Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led triaging, automate high-volume workflows, and support nuanced analysis of self-driving behavior to surface critical anomalies.
• Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and iteration of models used to judge performance across large real-world exposure.
• Design and evolve data + evaluation systems inspired by RL from human preferences (RLHF) and related paradigms—turning preference/judgment signals into repeatable, scalable training and evaluation loops.
• Partner broadly with teams such as Prediction, Planning, Research, and platform/engineering leads to land cross-cutting improvements with clear metrics.
Qualifications:
Required:
• M.S. or Ph.D. in Computer Science, Machine Learning, AI, or a related field—or equivalent practical experience.
• Hands-on experience building and applying ML in production-grade settings, with a strong RL component (policy learning, preference/feedback optimization, or offline/online RL pipelines).
• Depth in deep learning, sequence modeling, and generative models.
• Demonstrated impact via strong publications or a clear history of shipping impactful ML systems end-to-end.
• Experience with large-scale distributed training and large-scale data processing.
• Ability to lead ambiguous technical work from problem framing through reliable delivery.
Preferred:
• Background in autonomous vehicles, robotics, or complex simulation environments.
• Strong grasp of modern RL and post-training techniques in LLM, dLLM, VLA and video generations.
• Hands-on integration of simulation platforms with ML training and evaluation workflows.
• Python fluency and frameworks such as PyTorch.
• Experience defining and operating metrics for complex, safety-critical AI systems.
• Technical leadership: influencing stakeholders, aligning teams, and raising the bar for evaluation rigor.
• Excellent communication—simple explanations of complex trade-offs.
Company:
Pony.ai develops autonomous driving technology for vehicles that operates using artificial intelligence and machine learning. Founded in 2016, the company is headquartered in Fremont, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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