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Reinforcement Learning Engineer Jobs in North Carolina

Belk

Decision Scientist

Charlotte, NC · On-site

... to reinforcement learning, multi-armed bandit, or contextual bandit approaches; willingness to develop deeper expertise * Strong SQL skills for complex data extraction and feature engineering from ...

Belk

Decision Scientist

Charlotte, NC · On-site

... to reinforcement learning, multi-armed bandit, or contextual bandit approaches; willingness to develop deeper expertise * Strong SQL skills for complex data extraction and feature engineering from ...

CoVar

Signal Processing Engineer

Durham, NC

Signal Processing Engineer About CoVar CoVar is a small AI/ML R&D software company in Durham, NC ... AI/ML algorithm design in the areas of computer vision, reinforcement learning (RL), and natural ...

Cree

Manager of Research Science

Durham, NC

Use your previous expertise in AI/ML techniques for computer vision, physics informed ML, and reinforcement learning to mentor early-in-career engineers * Bring your previous semiconductor/materials ...

Wolfspeed, Inc.

Manager of Research Science

Durham, NC · On-site

Use your previous expertise in AI/ML techniques for computer vision, physics informed ML, and reinforcement learning to mentor early-in-career engineers * Bring your previous semiconductor/materials ...

1 point system

Application Architect III

Charlotte, NC · On-site

Strong experience in agent based intelligent control systems and reinforcement learning. Strong ... Hands-on in symbolic programming, event-based system design. Experience in software development of ...

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Reinforcement Learning Engineer information

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$34.5K

$105.3K

$174K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 29, 2026, the average yearly pay for reinforcement learning engineer in North Carolina is $105,298.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,400.00 and $137,700.00 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 are popular job titles related to Reinforcement Learning Engineer jobs in North Carolina?

For Reinforcement Learning Engineer jobs in North Carolina, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Engineer jobs in North Carolina look for?

The top searched job categories for Reinforcement Learning Engineer jobs in North Carolina are:

What cities in North Carolina are hiring for Reinforcement Learning Engineer jobs?

Cities in North Carolina with the most Reinforcement Learning Engineer job openings:

Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in North Carolina as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $105,298 per year, or $50.6 per hour.

Senior Deep Learning Framework Communications Engineer

Durham, NC • On-site

NVIDIA AI
See more jobs at NVIDIA AIexternal

Full-time

Re-posted 21 days ago

Job description

Job Summary:
NVIDIA AI is leading the way in groundbreaking developments in Artificial Intelligence and High Performance Computing. They are looking for a motivated Deep Learning engineer to bring advanced communication technologies into AI stacks and improve AI compilers for better performance in multi-GPU environments.
Responsibilities:
• Integrate new communication libraries features in AI frameworks: from PoC to performance analysis to production
• Perform deep analysis of AI workloads and frameworks to identify multi-GPU communication requirements and opportunities. Collaborate hands-on with teams working on the latest AI models.
• Improve AI compilers to hide communications or perform automatic fusion.
• Conduct in-depth AI workload performance characterization on multi-GPU clusters.
• Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads.
• Author custom communication or fused compute-communication kernels to showcase ultimate performance on NV platforms.
• Influence the roadmap of communication libraries - NCCL & NVSHMEM.
• Collaborate with a very dynamic team across multiple time zones.
Qualifications:
Required:
• B.S, M.S. or PHD in Computer Science, or related field (or equivalent experience) with 5+ software engineering and HPC/AI experience
• Development or integration experience with Deep Learning Frameworks such PyTorch, JAX, and Inference Engines such as TRT-LLM, vLLM, SGLang
• Rapid prototyping and development with Python, C++, CUDA or related DSLs (Triton, cuTe)
• Solid grasp of AI models, parallelisms, and/or compiler technologies (e.g. torch.compile)
• Experience conducting performance benchmarking on AI clusters. Familiarity with at least one performance profiler toolchain (PyTorch profiler, NVIDIA Nsight Systems)
• Understanding of HPC/AI communication concepts (1-sided v 2-sided communication, elasticity, resiliency, topology discovery, etc)
• Adaptability and passion to learn new areas and tools
• Flexibility to work and communicate effectively across different teams and timezones
Preferred:
• Experience with parallel programming on at least one communication runtime (NCCL, NVSHMEM, MPI). Good understanding of computer system architecture, HW-SW interactions and operating systems principles (aka systems software fundamentals)
• Expertise in one or more of these areas: Training, Distributed inference, MoE, Reinforcement Learning, kernel authoring (on CUDA, Triton, cuTe, etc). Experience with programming for compute & communication overlap in distributed runtimes
• Experience with AI compiler pattern matching and lowering. Solid understanding of memory hierarchy, consistency model, and tensor layout
Company:
Explore the latest breakthroughs made possible with AI. Founded in , the company is headquartered in Santa Clara, CA, US, , with a team of 10001+ employees. The company is currently Late Stage.

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