... by reinforcement learning. You will work on applying RL in closed-loop, safety-critical ... Knowledge of model optimization (quantization, pruning) and CUDA is a plus * Knowledge of traffic ...
... by reinforcement learning. You will work on applying RL in closed-loop, safety-critical ... Knowledge of model optimization (quantization, pruning) and CUDA is a plus * Knowledge of traffic ...
AI Research Scientist, Reinforcement Learning
New York, NY · On-site
$122K - $181K/yr
... optimization algorithms • Experience in C/C++ and Python and deep learning frameworks (e.g ... using reinforcement learning techniques. It will also involve novel modalities such as 3D and ...
AI Research Scientist, Reinforcement Learning
New York, NY · On-site
$122K - $181K/yr
... optimization algorithms • Experience in C/C++ and Python and deep learning frameworks (e.g ... using reinforcement learning techniques. It will also involve novel modalities such as 3D and ...
AI Research Scientist, Reinforcement Learning Responsibilities: * Explore and develop novel post ... optimization algorithms * Experience in C/C++ and Python and deep learning frameworks (e.g ...
AI Research Scientist, Reinforcement Learning Responsibilities: * Explore and develop novel post ... optimization algorithms * Experience in C/C++ and Python and deep learning frameworks (e.g ...
Research Scientist, Reinforcement Learning - Atlas
Waltham, MA · On-site
$175K - $230K/yr
Design, implement, and train reinforcement learning algorithms for challenging whole-body mobile ... Strong foundations in algorithms, debugging, performance optimization, and robotics fundamentals ...
Research Scientist, Reinforcement Learning - Atlas
Waltham, MA · On-site
$175K - $230K/yr
Design, implement, and train reinforcement learning algorithms for challenging whole-body mobile ... Strong foundations in algorithms, debugging, performance optimization, and robotics fundamentals ...
... by reinforcement learning. You will work on applying RL in closed-loop, safety-critical ... Knowledge of model optimization (quantization, pruning) and CUDA is a plus * Knowledge of traffic ...
... by reinforcement learning. You will work on applying RL in closed-loop, safety-critical ... Knowledge of model optimization (quantization, pruning) and CUDA is a plus * Knowledge of traffic ...
... by reinforcement learning. You will work on applying RL in closed-loop, safety-critical ... Knowledge of model optimization (quantization, pruning) and CUDA is a plus * Knowledge of traffic ...
Quick apply
... by reinforcement learning. You will work on applying RL in closed-loop, safety-critical ... Knowledge of model optimization (quantization, pruning) and CUDA is a plus * Knowledge of traffic ...
Member of Engineering (Reinforcement Learning Infrastructure)
$110K - $144K/yr
ABOUT THE ROLE You would be working on our reinforcement learning team focused on improving ... optimization * Familiarity with deep learning frameworks (PyTorch or JAX) and RL workflows ...
Member of Engineering (Reinforcement Learning Infrastructure)
$110K - $144K/yr
ABOUT THE ROLE You would be working on our reinforcement learning team focused on improving ... optimization * Familiarity with deep learning frameworks (PyTorch or JAX) and RL workflows ...
Staff Scientist - Post-Training and Reinforcement Learning for AI for Science
Lemont, IL · On-site +1
We are looking for a creative and collaborative scientist who is excited to develop, scale, and evaluate post-training methods, including reinforcement learning, preference optimization, adaptation ...
Staff Scientist - Post-Training and Reinforcement Learning for AI for Science
Lemont, IL · On-site +1
We are looking for a creative and collaborative scientist who is excited to develop, scale, and evaluate post-training methods, including reinforcement learning, preference optimization, adaptation ...
We are looking for a creative and collaborative scientist who is excited to develop, scale, and evaluate post-training methods, including reinforcement learning, preference optimization, adaptation ...
We are looking for a creative and collaborative scientist who is excited to develop, scale, and evaluate post-training methods, including reinforcement learning, preference optimization, adaptation ...
... reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our ...
... reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our ...
Machine Learning Engineer - Reinforcement Learning
Fremont, CA · On-site
$150K - $250K/yr
Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led ... Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and ...
Quick apply
Machine Learning Engineer - Reinforcement Learning
Fremont, CA · On-site
$150K - $250K/yr
Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led ... Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and ...
Machine Learning Engineer - Reinforcement Learning
$150K - $250K/yr
Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led ... Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and ...
Machine Learning Engineer - Reinforcement Learning
$150K - $250K/yr
Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led ... Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and ...
Machine Learning Engineer - Reinforcement Learning
Fremont, CA · On-site
$150K - $250K/yr
Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led ... Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and ...
Machine Learning Engineer - Reinforcement Learning
Fremont, CA · On-site
$150K - $250K/yr
Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led ... Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and ...
Research Engineer - Reinforcement Learning
San Francisco, CA · On-site +1
$180K - $290K/yr
Research Engineer - Reinforcement Learning You'll bring reinforcement learning to Firecrawl's core ... You understand PPO, RLHF, reward modeling, and policy optimization - and you understand how modern ...
Research Engineer - Reinforcement Learning
San Francisco, CA · On-site +1
$180K - $290K/yr
Research Engineer - Reinforcement Learning You'll bring reinforcement learning to Firecrawl's core ... You understand PPO, RLHF, reward modeling, and policy optimization - and you understand how modern ...
Research Engineer - Reinforcement Learning
San Francisco, CA · On-site +1
$150/hr
We enable researchers, startups and enterprises to run end-to-end reinforcement learning at ... optimization techniques. * Contribute to the development of our open-source libraries and ...
Research Engineer - Reinforcement Learning
San Francisco, CA · On-site +1
$150/hr
We enable researchers, startups and enterprises to run end-to-end reinforcement learning at ... optimization techniques. * Contribute to the development of our open-source libraries and ...
Senior Staff Research Engineer - Reinforcement Learning for AI Agents
Santa Clara, CA · On-site
$122K - $168K/yr
Responsibilities : • Reinforcement learning methods for LLM-driven agents and decision systems. • Policy optimization for long-horizon reasoning and planning. • Learning from human or AI ...
Senior Staff Research Engineer - Reinforcement Learning for AI Agents
Santa Clara, CA · On-site
$122K - $168K/yr
Responsibilities : • Reinforcement learning methods for LLM-driven agents and decision systems. • Policy optimization for long-horizon reasoning and planning. • Learning from human or AI ...
Apply reinforcement learning, optimization algorithms, and hybrid ML + OR approaches Transportation Domain Solutions Build solutions for: * Maritime logistics (vessel scheduling, port operations)
Apply reinforcement learning, optimization algorithms, and hybrid ML + OR approaches Transportation Domain Solutions Build solutions for: * Maritime logistics (vessel scheduling, port operations)
Build agentic optimization systems that automatically improve code, run experiments, analyze ... reinforcement learning and deep learning. * You have proficiency in using AI coding tools (e.g ...
Build agentic optimization systems that automatically improve code, run experiments, analyze ... reinforcement learning and deep learning. * You have proficiency in using AI coding tools (e.g ...
Apply reinforcement learning, optimization algorithms, and hybrid ML + OR approaches Transportation Domain Solutions Build solutions for: * Maritime logistics (vessel scheduling, port operations)
Quick apply
Apply reinforcement learning, optimization algorithms, and hybrid ML + OR approaches Transportation Domain Solutions Build solutions for: * Maritime logistics (vessel scheduling, port operations)
... optimization of RL algorithms for large foundation models. Research areas include but are not ... to Reinforcement Learning Algorithms, Reward Modeling, and World Models. We will conduct large ...
... optimization of RL algorithms for large foundation models. Research areas include but are not ... to Reinforcement Learning Algorithms, Reward Modeling, and World Models. We will conduct large ...
Reinforcement Learning Optimization information
See salary details
$21.8K is the 25th percentile. Wages below this are outliers.
$11K - $22.7K
27% of jobs
$22.7K - $34.5K
0% of jobs
$34.5K - $46.2K
0% of jobs
$46.2K - $57.9K
0% of jobs
$57.9K - $69.6K
0% of jobs
The median wage is $80.4K / yr.
$69.6K - $81.4K
25% of jobs
$81.4K - $93.1K
18% of jobs
$101.5K is the 75th percentile. Wages above this are outliers.
$93.1K - $104.8K
7% of jobs
$104.8K - $116.5K
2% of jobs
$116.5K - $128.3K
0% of jobs
$128.3K - $140K
21% of jobs
$11K
$83.9K
$140K
How much do reinforcement learning optimization jobs pay per year?
As of Jun 7, 2026, the average yearly pay for reinforcement learning optimization in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 per year, depending on experience, location, and employer.
What are some common challenges faced by professionals in Reinforcement Learning Optimization roles, and how can they be addressed?
Professionals in Reinforcement Learning Optimization often encounter challenges such as sparse or delayed rewards, high computational requirements, and difficulty in ensuring model stability during training. Addressing these issues typically involves leveraging techniques like reward shaping, using experience replay buffers, and adopting robust exploration strategies. Collaborating closely with data engineers, software developers, and domain experts is also crucial to ensure that the RL models are well-integrated and perform reliably in production environments.
What are the key skills and qualifications needed to thrive as a Reinforcement Learning Optimization Specialist, and why are they important?
To thrive in Reinforcement Learning Optimization, a strong background in mathematics, probability theory, machine learning algorithms, and programming (often Python) is essential, typically supported by an advanced degree in computer science or a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), experience with RL libraries (like OpenAI Gym), and knowledge of optimization techniques are highly valued. Analytical thinking, problem-solving skills, and effective communication set top performers apart in this role. These capabilities are crucial for developing, fine-tuning, and deploying RL models that solve complex, real-world problems efficiently.
What is Reinforcement Learning Optimization?
Reinforcement Learning Optimization is a process in machine learning where agents learn to make decisions by interacting with an environment to achieve a specific goal. Through trial and error, the agent receives feedback in the form of rewards or penalties, which it uses to refine its actions over time. This optimization technique is widely used in robotics, gaming, and autonomous systems to develop intelligent behaviors. The core idea is to maximize cumulative rewards by finding the best sequence of decisions. Reinforcement Learning Optimization combines elements of computer science, mathematics, and statistics to solve complex real-world problems.
Other
Posted 10 days ago
Job description
We are building next-generation end-to-end autonomous driving systems powered by reinforcement learning.
You will work on applying RL in closed-loop, safety-critical environments, leveraging large-scale simulation and real-world driving data to improve safety, comfort, and robustness.
- Train and deploy RL policies in closed-loop driving environments
- Scale RL training using massively parallel simulation systems
- Design and optimize reward functions for complex driving behaviors
- Improve sim-to-real transfer for real-world robustness
- Collaborate with cross-functional teams to integrate models into production systems
Requirements
Core Technical Skills
- Proficiency in modern RL algorithms: DQN, PPO, SAC, TD3, etc.
- Proficiency in modern RLHF algorithms: PPO, DPO, GRPO, etc.
- Hands-on experience training reward models and finetuning LLM/VLM/VLA
- Knowledge of distributed RL training at scale
- Proficiency with massively parallel simulation environments
- Knowledge of sim-to-real transfer techniques and domain randomization
- Proficiency in Python, comfortable with C++
- Proficiency in deep learning frameworks such as PyTorch
- Experience with distributed training frameworks (Ray, Horovod, etc.)
- Knowledge of model optimization (quantization, pruning) and CUDA is a plus
- Knowledge of traffic rules, driving behavior modeling
Preferred Qualifications
- Publications in top-tier venues (ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, ICRA, IROS, etc.)
- Open-source contributions to RL libraries or autonomous driving projects
- Previous experience with LLM fine-tuning using RLHF
- Knowledge of safe RL, interpretable AI, or robustness techniques
- Familiarity with autonomous vehicle regulations and safety standards