ZipRecruiter

Log In

2

Remote Reinforcement Learning Jobs in Edison, NJ

previous page

Showing results 21-21

All JobsReinforcement Learning JobsRemote Reinforcement Learning JobsRemote Reinforcement Learning Jobs in Edison, NJ

Remote Reinforcement Learning information

See Edison, NJ salary details

$11.4K

$86.8K

$144.9K

How much do remote reinforcement learning jobs pay per year?

As of Aug 12, 2026, the average yearly pay for remote reinforcement learning in Edison, NJ is $86,843.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,500.00 and $143,900.00 per year, depending on experience, location, and employer.

What is a remote reinforcement learning?

A Remote Reinforcement Learning job involves developing and applying reinforcement learning algorithms while working from a location outside of a traditional office environment. Professionals in this field focus on creating systems where agents learn optimal behaviors through trial and error, often using feedback from their environment. These jobs typically require expertise in machine learning, programming, and mathematics, and are commonly found in industries like robotics, gaming, and autonomous systems. Working remotely allows researchers and engineers to collaborate with global teams using digital tools and platforms.

What are the key skills and qualifications needed to thrive as a remote reinforcement learning engineer?

To thrive as a Remote Reinforcement Learning Engineer, you need a strong background in machine learning, statistics, and programming (especially Python), often supported by an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and RL-specific libraries like OpenAI Gym, along with experience using cloud computing platforms, is typically required. Excellent problem-solving skills, self-motivation, and effective remote communication help individuals excel in distributed teams. These skills ensure the successful design, implementation, and deployment of reinforcement learning solutions while collaborating efficiently in a remote work environment.

What is the difference between Remote Reinforcement Learning vs Remote Machine Learning Engineer?

AspectRemote Reinforcement Learning
Required CredentialsMaster's or PhD in Computer Science, AI, or related fields; knowledge of RL algorithms
Work EnvironmentResearch-focused, experimental, often involves simulation and algorithm development
Employer & Industry UsageTech companies, research labs, AI startups focusing on autonomous systems
Common Search & Comparison IntentUnderstanding specialized AI roles, research focus, and technical skills

Remote Reinforcement Learning specialists focus on developing algorithms that enable machines to learn through trial and error in simulated or real environments. In contrast, Remote Machine Learning Engineers typically work on deploying and optimizing various machine learning models across applications. While both roles require strong programming skills and knowledge of AI, reinforcement learning emphasizes decision-making processes, whereas machine learning engineering covers a broader range of models and deployment strategies.

What are common challenges faced when working remotely in a reinforcement learning role and how can they be addressed?

Working remotely in a Reinforcement Learning role often involves overcoming communication barriers with cross-functional teams, managing large-scale experiments without on-site resources, and staying updated with rapidly evolving research. To address these challenges, it's important to establish regular check-ins with colleagues, utilize cloud-based platforms for experiment management, and participate in virtual seminars or journal clubs. Developing strong self-motivation and time management skills is also crucial to maintain productivity in a remote environment.
What are the most commonly searched types of Reinforcement Learning jobs in Edison, NJ? The most popular types of Reinforcement Learning jobs in Edison, NJ are:
What are popular job titles related to Remote Reinforcement Learning jobs in Edison, NJ? For Remote Reinforcement Learning jobs in Edison, NJ, the most frequently searched job titles are:
What job categories do people searching Remote Reinforcement Learning jobs in Edison, NJ look for? The top searched job categories for Remote Reinforcement Learning jobs in Edison, NJ are:
What cities near Edison, NJ are hiring for Remote Reinforcement Learning jobs? Cities near Edison, NJ with the most Remote Reinforcement Learning job openings:
Remote Reinforcement Learning jobs near you

Revenue Enablement Program Manager

Hex Technologies

New York, NY • On-site, Remote

Full-time

Medical, PTO

Re-posted 26 days ago

Job description

About the role:
The Revenue Enablement Program Manager is a builder-operator role for someone equally energized by designing the systems that make enablement work and owning the programs where that infrastructure matters most. Hex is growing fast, and Revenue Enablement is a strategic priority for the business. The work you do here directly shapes how new hires ramp, retain knowledge, and successfully execute in the field. This role is the connective tissue between foundational enablement systems and day-to-day field execution, and we're building it at a moment when AI is redefining field productivity, freeing sellers to focus on strategic work and deliver world-class customer experiences.
This is a systems-and-workflow role first, an enablement role second. We're not looking for someone who's spent years running enablement programs - we're looking for someone who's spent years building the systems that make teams effective, wherever they sat: ops, product, solutions/technical architecture, pre-sales, GTM Engineering, post-sales, or enablement itself. If you've partnered closely with customers or internal teams to design AI-driven workflows, automate the manual stuff, and make people more effective at their day-to-day - this role is built for that instinct, not for a specific title on your resume.
You will work directly with the Head of Revenue Enablement to set priorities and shape the function, supporting a revenue org that spans SDRs, AEs, Sales Engineers, Customer Engineers, and Product Experts. You partner closely with Revenue Leaders, RevOps, and Product Marketing across everything you build. You own the full loop: design the system, launch the program, inspect what's landing, and improve it based on data and field signal. This is a high-ownership role with a lot of white space.
Location: This role is based in our NYC or SF offices, with 2x weekly in-office expectations for candidates in those locations. We're also open to remote candidates with strong GTM enablement and systems experience.
What you will own:
Knowledge architecture
  • Own and continuously improve Hex's centralized knowledge base as the single source of truth sellers trust and actually use
  • Design a push-and-pull information model: proactively surface the right content at the right moment while building a self-service system sellers can trust when they need it fast
  • Establish standards for how content is created, maintained, accessed, and retired - and hold those standards as the business evolves
  • Partner with PMM and Marketing on content review cadence and ownership; you are the accountability layer, not the primary content creator for all assets

Systems and workflow infrastructure
  • Design and build workflows across the GTM tech stack (Hex, Gong, SFDC, Notion, AI tools) in close partnership with RevOps, embedding execution standards directly into how sellers work every day
  • Automate reinforcement signals, certification triggers, and coaching prompts so behavior change doesn't depend on manual follow-through
  • Surface actionable insights to managers so coaching is proactive, not reactive
  • Build coaching, information sharing, and certification automations at the right moment, creating a scalable self-service enablement model
  • Track ramp progression, field execution, and program effectiveness to understand what's working and why

New Hire Onboarding
  • Own Sales Onboarding end-to-end: scope, curriculum coordination with SMEs, milestone tracking, certifications, and continuous iteration based on metrics and field feedback
  • Build a scalable experience that combines structured learning with just-in-time, in-workflow enablement
  • Create visibility into ramp progression, gaps, and leading indicators in partnership with RevOps and the Data team

About You:
  • 3 to 5 years in a role where you built systems or workflows that made a team more effective - this could be technical sales (pre or post), gtm engineering, enablement, or RevOps, at a growing B2B SaaS startup
  • You've built workflows or operational systems before, not just run programs - you can point to something you scoped, built, and shipped end to end with minimal oversight
  • A first-principles thinker: you identify a problem, design the solution (often AI-led), and drive implementation without waiting for a roadmap
  • Comfortable across the GTM tech stack (CRM, Gong, Notion, AI tools) and able to connect them without being handed a plan
  • Proactively experiments with AI tools to drive efficiencies - you've likely already built something with them that stuck
  • Field credibility: you deeply understand what it actually takes to sell and support software, because you've sat close to that work - whether internally or with customers
  • Comfortable balancing systems thinking with change management - you don't just hand people a finished workflow, you bring them along so it actually gets adopted
  • High ownership; you identify what needs fixing and move without waiting to be told
  • Thrives in ambiguity; brings structure to messy environments without over-engineering

Nice to haves
  • Experience instrumenting onboarding or ramp metrics
  • Background in knowledge systems, content governance, or internal tooling
  • Experience partnering directly with customers on workflow or process design (solutions engineering, technical CS, post-sales architecture)

About Hex
Hex is growing our team of builders on a mission to make everyone a data person. Our platform solves key pain points with today's data and analytics tooling, and empowers anyone to explore data using natural language, with or without code, on trusted context. Thousands of customers like Ramp, Figma, Stubhub, Anthropic, and Gamma love Hex for our beautiful UI, agentic superpowers, and boundless flexibility.
Hex has raised over $100M from leading investors like Sequoia, a16z, Snowflake and Amplify. With office hubs in San Francisco and New York, as well as remote team members. We're a high-agency team that ships fast and has fun doing it. Check out our Intro to Hex video and employee handbook to learn more about what it's like to work at Hex.
In addition to our unique culture, Hex proudly offers a competitive total rewards package, including but not limited to, market-benched salary & equity, comprehensive health benefits, and flexible paid time off.
The base pay band for this role is: $120,000-$165,000.
The salary range shown may be a reflection of additional factors such as geographical location and skill ranges/levels we're open to. Placement in the salary range will be decided upon completion of the interview process, taking into account factors like leaving room for growth, internal fairness & parity, your demonstrated skills, and the depth of your experience. Our Recruiting team will be able to provide more details during the interview process.
By submitting an application the candidate consents to the use of their personal information in accordance with the Hex Privacy policy: https://learn.hex.tech/docs/trust/privacy-policy.
Hex Technologies uses AI-assisted tools as part of our application review process, including for resume screening and fraud detection. These tools help our team evaluate applications and verify applicant information. All AI-generated recommendations are reviewed by a member of our recruiting team before any hiring decision is made. No application is automatically rejected based solely on an AI tool's output.

Apply