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Reinforcement Learning Engineer Jobs in Fords, NJ

Traversal

AI Researcher

Manhattan, NY · On-site

... reinforcement learning, and reward modeling techniques to align AI behavior with real-world SRE workflows and debugging practices. • Design pipelines to generate synthetic incidents and ...

Amazon

ML Infrastructure Engineer, Fauna

New York, NY · On-site

$117K - $154K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

You'll bring deep expertise in reinforcement learning, computer vision, and supervised learning ... programming language experience - 5+ years of leading design or architecture (design patterns ...

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All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Fords, NJ

Reinforcement Learning Engineer information

See Fords, NJ salary details

$38.7K

$118.1K

$195.2K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for reinforcement learning engineer in Fords, NJ is $118,119.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,600.00 and $154,400.00 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 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 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 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 near Fords, NJ are hiring for Reinforcement Learning Engineer jobs?

Cities near Fords, NJ with the most Reinforcement Learning Engineer job openings:

Reinforcement Learning Engineer jobs near you

Senior Machine Learning Operations Engineer

ZeroMark, Inc.

Manhattan, NY • On-site

$115K - $158K/yr

Full-time

Re-posted 12 days ago

Job description

Job Summary:
ZeroMark, Inc. builds AI-driven counter-drone systems that work in combat. They are seeking a Senior Machine Learning Operations Engineer to design and implement machine learning pipelines, collaborate with software engineers, and mentor junior engineers in a fast-paced environment.
Responsibilities:
• Design, develop, and implement end-to-end machine learning pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment.
• Collaborate with the general software engineering team to integrate ML models into existing software systems and ensure scalability and maintainability.
• Work in conjunction with computer vision specialists to apply and optimize ML techniques for image and video analysis, object detection, tracking, and recognition in defense contexts.
• Research and evaluate new machine learning algorithms, tools, and technologies to enhance our capabilities and solve challenging problems.
• Perform rigorous model testing, validation, and performance tuning to ensure robustness and accuracy in real-world scenarios.
• Contribute to the development of best practices for ML engineering, including MLOps, version control, and reproducible research.
• Mentor junior engineers and contribute to a culture of continuous learning and knowledge sharing.
• Communicate technical concepts effectively to both technical and non-technical stakeholders.
Qualifications:
Required:
• Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
• Experience: 5+ years of experience in machine learning engineering, with a proven track record of deploying ML models in production environments.
• Technical Skills: Strong proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Technical Skills: Solid understanding of core machine learning concepts, including supervised, unsupervised, and reinforcement learning.
• Technical Skills: Experience with various machine learning model architectures and their application (e.g., CNNs, RNNs, Transformers, decision trees, support vector machines).
• Technical Skills: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Technical Skills: Experience with MLOps tools and practices.
• Technical Skills: Experience deploying a variety of edge systems.
• Technical Skills: Experience with TensorRT and other similar technologies.
• Technical Skills: Deep knowledge of C++ and Python.
• Domain Knowledge: Experience or strong interest in defense, aerospace, or related industries is highly desirable.
• Domain Knowledge: Understanding of the unique challenges and considerations for deploying ML in defense applications (e.g., adversarial robustness, real-time constraints, data security).
• Collaboration & Communication: Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams.
• Collaboration & Communication: Ability to translate complex technical concepts into clear and concise language.
• Problem-Solving: Strong analytical and problem-solving skills, with a proactive and innovative approach.
• Problem-Solving: Ability to work independently and manage multiple priorities in a fast-paced environment.
Preferred:
• Experience with specific computer vision tasks such as object detection, segmentation, or tracking.
• Familiarity with real-time ML systems and embedded systems.
• Contributions to open-source projects or publications in relevant fields.
Company:
ZeroMark empowers US and allied forces with cutting-edge defense technology, elevating mission success and safeguarding personnel. Founded in 2022, the company is headquartered in New York, USA, with a team of 2-10 employees. The company is currently Early Stage.

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