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Reinforcement Learning Engineer Jobs in Denver, CO

ARKA Group

Staff AI/ML Engineer (TS/SCI) {S}

Aurora, CO · On-site

$155K - $200K/yr

This position is for a Staff AI/ML Engineer looking for new challenging problems. You should have ... Experience with LLMs, Transformers, YOLO, GANs, Reinforcement Learning * Linux and AWS experience

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All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Denver, CO

Reinforcement Learning Engineer information

See Denver, CO salary details

$39.1K

$119.3K

$197.1K

How much do reinforcement learning engineer jobs pay per year?

As of Sep 4, 2026, the average yearly pay for reinforcement learning engineer in Denver, CO is $119,257.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,400.00 and $155,900.00 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 Denver, CO?

For Reinforcement Learning Engineer jobs in Denver, CO, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Engineer jobs in Denver, CO look for?

The top searched job categories for Reinforcement Learning Engineer jobs in Denver, CO are:

What cities near Denver, CO are hiring for Reinforcement Learning Engineer jobs?

Cities near Denver, CO with the most Reinforcement Learning Engineer job openings:

Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Denver, CO as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, and 3% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $119,257 per year, or $57.3 per hour.

Principal AI/ML Engineer with Security Clearance

ClearanceJobs Workforce Solutions

Centennial, CO • On-site

Contractor

Re-posted 26 days ago

Job description

Duties: Location Englewood, Colorado Hybrid opportunity Will need to be able to obtain a clearance This is a Contract opportunity In this role, you will help shape the long-term AI/ML technical vision for the organization, guide high-impact R&D initiatives, and lead the development of advanced autonomy, perception, analytics, and generative AI capabilities.
You will be responsible for setting technical direction across multiple simultaneous efforts, defining architectural standards, and ensuring that JTF Sierra’s prototypes and initiatives represent industry-leading innovation.
This role requires exceptional technical depth, the ability to operate with extreme autonomy, and the leadership presence to influence engineering culture, collaborate with program leadership, mentor staff, and represent the team to senior executives, customers, and external partners.
Role Expectations Specific to This Team • Translate broad mission objectives into program-level AI/ML architectures, strategies, staffing needs, data plans, and technical frameworks
• Drive system-level AI/ML decision-making, establishing technical standards and guiding engineering trade studies that shape platform-level autonomy and perception capabilities
• Identify and champion high-value R&D opportunities, emerging technologies, and cross-organizational partnerships that accelerate SNC’s AI/ML advancements
• Provide deep technical consultation across JTF Sierra and adjacent Business Units product lines, ensuring architectural coherence and technical excellence
• Ensure AI/ML solutions are architected for scalability and integration with enterprise-wide platforms, collaborating with IT and infrastructure teams to define and implement the necessary tools. Data pipeline, and computing resources for sustainable AI/ML operations across the organization.
• Balance program-specific AI/ML solution development with strategic focus on establishing reusable frameworks, common data assets, and infrastructure that support cross-program and enterprise-wide AI/ML adoption
Skills: • Lead design and technical direction for next-generation architectures spanning deep learning, reinforcement learning, multimodal generative AI, and advanced perception/decision systems
• Architect and oversee end-to-end multi-program AI/ML systems across platforms and embedded systems
• Assist with development of long-term technical strategies, roadmaps, and requirements for emerging AI/ML initiatives
• Identify, define, and advocate for the foundational data, compute, MLOps, and cloud/on-prem infrastructure necessary to support sustainable and secure AI/ML development and deployment across JTF Sierra and related business units.
• Establish and promote best practices for the full AI/ML lifecycle—including data management, model versioning, CI/CD for ML, monitoring, and continuous improvement—to ensure reliable deployment and operation of AI/ML models in production.
• Oversee multiple development streams, providing technical reviews, risk assessments, and mitigations plans
• Shape system-level behavior and engineering tradeoffs when requirements are ambiguous
• Lead development of simulations, sensor fusion models, vision models, and planning/decision algorithms
• Represent JTF Sierra to leadership, customers, and partners (assist in developing and presenting briefings, demos, high-level technical presentations, etc)
• Establish adaptive, agile AI/ML validation, verification, and safety frameworks for proof-of-concept level mission-critical systems
• Evaluate and introduce emerging technologies (examples: transformers, RLHF, edge AI, XAI, GPU acceleration)
• Partner with Program Manager and Project Engineer to define staffing, data, schedules, and resources required to execute JTF Sierra technical initiatives
• Coach and develop engineering talent, raising JTF Sierra’s overall AI/ML capabilities
Education: • Bachelor’s degree in Computer Science, Engineering, Math, Statistics, or related STEM field
• 14+ years of experience in AI/ML or related fields, or 16+ years without a degree
• Demonstrated mastery of deep learning, reinforcement learning, generative models, and large-scale AI/ML system architecture
• Proven experience architecting and deploying mission-critical and/or large-scale AI/ML systems
• Strong proficiency in Python, C++, C#, and/or Java with experience building scalable Machine Learning systems
• Experience providing technical leadership across teams, projects, or programs
• Ability to define technical strategies, influence senior stakeholders, and make organization-level architecture decisions
• In-depth experience with aerospace/defense-relevant regulatory and cybersecurity considerations
• Demonstrated ability to mentor and grow engineering talent within an organization
Qualifications We Prefer
• Advanced degree (MS or PhD) in AI/ML or related field
• Experience applying AI/ML to autonomy, multimodal sensor fusion, or embedded/real-time platforms
• Experience establishing or scaling ML engineering standard (MLOps, validation frameworks, data management)
• Expertise with GPU acceleration, CUDA/TensorRT, or parallel computing
• Publications, patents, or thought leadership in AI/ML
• Familiarity with edge AI, explainable AI (XAI), or emerging/advanced ML topics
• Experience translating high-level mission objectives into complex AI/ML system architectures (HMI scenarios, autonomy stacks)

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