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Reinforcement Learning Engineer Jobs in New York

You'll work on cutting-edge problems involving time-series forecasting, reinforcement learning, and ... Mentor junior engineers and contribute to engineering best practices * Research and evaluate new ML ...

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Explore reinforcement learning ideas, focusing on reward functions and training behavior ... research-engineering role. * Experience in training and evaluating ML models and running ...

Posted today

Lead Machine Learning Engineer

Manhattan, NY · On-site

$112K - $148K/yr

... and reinforcement learning techniques. * Develop and deploy robust feature engineering pipelines ... and ML services optimized for low latency and high throughput. * Establish and utilize robust A/B ...

Senior Machine Learning Engineer

Manhattan, NY · On-site

$114K - $157K/yr

... and reinforcement learning techniques. * Develop and deploy robust feature engineering pipelines ... and ML services optimized for low latency and high throughput. * Establish and utilize robust A/B ...

Strong background in one or more of the following: reinforcement learning, causal inference, LLM ... Solid backend engineering skills in Python, including APIs and data modeling * A/B testing and ...

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Reinforcement Learning Engineer information

See New York salary details

$41.6K

$126.8K

$209.5K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for reinforcement learning engineer in New York is $126,760.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,800.00 and $165,700.00 per year, depending on experience, location, and employer.

What is a reinforcement learning engineer?

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 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 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 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 cities in New York are hiring for Reinforcement Learning Engineer jobs?

Cities in New York with the most Reinforcement Learning Engineer job openings:

Infographic showing various Reinforcement Learning Engineer job openings in New York as of August 2026, with employment types broken down into 92% Full Time, and 8% Contract. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $126,760 per year, or $60.9 per hour.

Senior Machine Learning Engineer

Rapidtrade

Manhattan, NY • On-site

$180 - $240/hr

Other

Posted 3 days ago

New


Job description

About the Role

We're looking for a Senior Machine Learning Engineer to join our AI team and help build the next generation of predictive trading models. You'll work on cutting-edge problems involving time-series forecasting, reinforcement learning, and large-scale data processing.

What You'll Do
  • Design and implement machine learning models for market prediction and risk assessment
  • Build and maintain scalable ML pipelines processing billions of data points daily
  • Collaborate with quantitative analysts to translate trading strategies into ML systems
  • Optimize model inference for sub-millisecond latency requirements
  • Mentor junior engineers and contribute to engineering best practices
  • Research and evaluate new ML techniques and frameworks
What We're Looking For
  • 5+ years of experience in machine learning or related field
  • Strong background in Python, with experience in PyTorch or TensorFlow
  • Experience with time-series analysis and forecasting
  • Familiarity with distributed computing (Spark, Ray, or similar)
  • MS or PhD in Computer Science, Machine Learning, or related field
  • Track record of deploying ML models to production
Nice to Have
  • Experience in fintech or quantitative trading
  • Knowledge of reinforcement learning
  • Contributions to open-source ML projects
  • Experience with Rust or C++ for performance-critical code
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