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Reinforcement Learning Engineer Jobs in Fords, NJ

Knowledge of Reinforcement Learning (RL) for sequential layout optimization. * Familiarity with ... From the programming and movies we create to employee benefits/programs and social impact outreach ...

Knowledge of Reinforcement Learning (RL) for sequential layout optimization. * Familiarity with ... From the programming and movies we create to employee benefits/programs and social impact outreach ...

ML Infrastructure Engineer, Fauna

New York, NY · On-site

$117K - $154K/yr

We are seeking a Machine Learning Engineer to work directly alongside our research scientists to ... You'll bring deep expertise in reinforcement learning, computer vision, and supervised learning ...

ML Infrastructure Engineer, Fauna

New York, NY · On-site

$117K - $154K/yr

We are seeking a Machine Learning Engineer to work directly alongside our research scientists to ... You'll bring deep expertise in reinforcement learning, computer vision, and supervised learning ...

Lead, Machine Learning Engineer

Newark, NJ · On-site

$107K - $141K/yr

... and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural ... Programming Languages: Python, R, SQL, Java or Scala, SQL You'll Love Working Here Because You Can ...

Showing results 21-40

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:

Senior Machine Learning Engineer, Advertiser Intelligence

Unitytech

New York, NY

$159K - $238K/yr

Full-time

Medical, Life, Retirement, PTO

Re-posted 6 hours ago


Job description

The opportunity

Unity's Advertiser Growth team leverages artificial intelligence and statistical optimization to maximize returns and reduce effort for advertisers running campaigns on the Unity ad network. Our ads reach billions of devices, and every improvement we ship directly moves outcomes for thousands of advertisers worldwide.

We are looking for a senior Machine Learning Engineer to lead the design of the optimization algorithms at the heart of this mission. In this role, you will be a technical authority on how we automate campaign optimization: how bids, budgets, and targeting decisions are made to serve each advertiser's goals with minimal manual effort. You will bridge advanced quantitative methodology and large-scale engineering, working where multi-objective optimization, exploration-exploitation trade-offs, long-term value measurement, and LLM-powered agentic systems meet real-time production systems.

What you'll be doing

  • Multi-objective campaign optimization: Design and ship algorithms that optimize across a variety of advertiser goals - installs, ROAS, retention, and spend efficiency - under real-world budget and marketplace constraints.

  • Exploration-exploitation strategy: Design strategies (e.g., bandits, reinforcement learning) that balance short-term performance against long-term value, so campaigns learn efficiently without sacrificing advertiser returns.

  • Long-term impact measurement: Develop efficient methodologies to measure the long-term impact of model and product changes, including surrogate metrics that give short-term signals for long-term advertiser and user value.

  • Metrics frameworks for launch decisions: Define the metrics frameworks that guide what we ship - clear, trustworthy criteria that connect model improvements to advertiser outcomes and business results.

  • Agentic automation for advertisers: Build LLM agent systems that automate campaign diagnostics and setup recommendations - agents that analyze campaign performance, identify root causes and recommend or apply configuration changes, so advertisers state their goals and trust the system to deliver.

  • Cross-functional technical leadership: Serve as a lead subject matter expert for ML, Product, and Engineering partners, ensuring quantitative rigor from model design through live production auctions.

What we're looking for

  • 5+ years in machine learning, data science, or applied research, ideally within ad tech, marketplaces, or other large-scale optimization domains.

  • An MS or PhD in a quantitative field (Computer Science, Statistics, Operations Research, Economics, or equivalent).

  • Quantitative depth: The skills to design algorithms that optimize among competing goals, design exploration-exploitation strategies balancing short-term and long-term value, build efficient methodologies for measuring long-term impact, and define metrics frameworks that guide launch decisions.

  • Agentic system experience: Hands-on experience building LLM-based agentic systems - tool use, orchestration, retrieval over domain data, and evaluation of agent quality - ideally applied to diagnostics, recommendations, or workflow automation.

  • Technical proficiency: Strong programming skills in Python or Scala, and experience with large-scale data processing frameworks such as Spark, Snowflake, or BigQuery.

  • Production track record: A history of shipping ML systems that operate on high-volume, real-time data and delivering measurable business impact.

  • Strategic leadership: Ability to translate complex quantitative concepts into clear product roadmaps and mentor engineers on modeling and optimization rigor.

You might also have

  • Prior experience with dynamic ads or creative optimization - e.g., dynamic creative assembly, creative ranking and selection, or generative creative pipelines.

  • Hands-on experience with bidding, pricing, pacing, or auction systems in digital advertising.

  • Familiarity with long-term value (LTV) prediction and surrogate metric design in mobile gaming or digital advertising.

  • Experience with reinforcement learning or contextual bandits in production.

  • Experience fine-tuning or evaluating LLMs, or building multi-agent orchestration frameworks.

Additional information

Relocation support is not available for this position

Annual Gross Pay: USD $159,100 - $238,700


This range reflects the anticipated base salary for this position. Beyond base salary, this role may be eligible for equity awards and participation in our company incentive plans (such as annual discretionary bonuses or sales commissions). The final offer amount will depend on several factors, including geographic location and the candidate's relevant experience, professional background, and skill set.


Benefits


At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.


Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status.


While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: Comprehensive health, life, and disability insurance | Commute subsidy | Employee stock ownership | Competitive retirement/pension plans | Generous vacation and personal days | Support for new parents through leave and family-care programs | Office food snacks | Mental Health and Wellbeing programs and support | Employee Resource Groups | Global Employee Assistance Program | Training and development programs | Volunteering and donation matching program


Life at Unity


Unity [NYSE: U] is the world's leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D - closing the gap between ideas and reality. For more information, please visit www.unity.com.


Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.


This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.
This posting is intended to fill an existing vacancy, and we are committed to providing applicants with updates throughout the hiring process in accordance with applicable law.


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