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Postdoctoral In Reinforcement Learning Jobs in New York

Responsibilities : โ€ข 5+ years of experience in deep learning research or reinforcement learning, with a focus on embodied agents or simulation environments. โ€ข Strong foundation in representation ...

Reinforcement Learning Engineer

New York, NY

$87.50K - $118.20K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site | Full-time Compensation ... Our client operates primarily in-person . Benefits * High-Stakes Autonomy: Unmatched ownership over ...

Reinforcement Learning Engineer

New York, NY ยท On-site

$87.50K - $118.20K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site | Full-time Compensation ... Our client operates primarily in-person . Benefits * High-Stakes Autonomy: Unmatched ownership over ...

Reinforcement Learning Engineer

New York, NY ยท On-site

$87.50K - $118.20K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site Full-time Compensation ... Our client operates primarily in-person . Benefits * High-Stakes Autonomy: Unmatched ownership over ...

... reinforcement learning โ€ข Explore and develop novel LLM post-training recipes using 3D data โ€ข ... Degree must be completed prior to joining Meta โ€ข Currently has or is in the process of obtaining ...

What We're Looking For * 5+ years of experience in deep learning research or reinforcement learning, with a focus on embodied agents or simulation environments. * Strong foundation in representation ...

Post Doctorate Associate

New York, NY ยท On-site

$62.50K/yr

... reinforcement learning for wearable robots, surgical robots, image-guided intervention, and ... postdoctoral training. Candidates will be required to present eligibility to work in the United ...

... In this role, you will lead the design, development, and productionization of advanced ML models and pricing algorithms, covering deep learning, causal modeling, and reinforcement learning. You will ...

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Postdoctoral In Reinforcement Learning information

What are the key skills and qualifications needed to thrive as a Postdoctoral Researcher in Reinforcement Learning, and why are they important?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is a Postdoctoral Researcher in Reinforcement Learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

What are popular job titles related to Postdoctoral In Reinforcement Learning jobs in New York? For Postdoctoral In Reinforcement Learning jobs in New York, the most frequently searched job titles are:
What job categories do people searching Postdoctoral In Reinforcement Learning jobs in New York look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in New York are:
What cities in New York are hiring for Postdoctoral In Reinforcement Learning jobs? Cities in New York with the most Postdoctoral In Reinforcement Learning job openings:

Research Scientist, Reinforcement Learning

Basis Research

New York, NY โ€ข On-site

$120K - $180K/yr

Full-time

Posted 8 days ago


Job description

About Basis
Basis is a nonprofit applied AI research organization with two mutually reinforcing goals.
The first is to understand and build intelligence. This means to establish the mathematical principles of what it means to reason, to learn, to make decisions, to understand, and to explain; and to construct software that implements these principles.
The second is to advance society's ability to solve intractable problems. This means expanding the scale, complexity, and breadth of problems that we can solve today, and even more importantly, accelerating our ability to solve problems in the future.
To achieve these goals, we're building both a new technological foundation that draws inspiration from how humans reason, and a new kind of collaborative organization that puts human values first.
About the Role
Research scientists lead Basis' efforts to develop a deeper understanding of the conceptual, mathematical, and computational principles of intelligence.
We are looking for people who are technically excellent, and who value probing concepts at their foundations. Our research scientists/engineers aspire to do rigorous, high-quality, robust science, but are not afraid to tinker, make mistakes, and explore radically different ideas in order to get there.
Basis is a collaborative effort, both internally and with our external partners; we are looking for people who enjoy working with others on problems larger than ones they can tackle alone.
Research Focus
Our research within the MARA project aims to develop new foundations and technologies for modeling, abstraction, and reasoning in AI systems. MARA's overarching goal is to uncover principled methods for how intelligence constructs, refines, and utilizes world models through interactive experimentation.
For this role, we are specifically looking for experts in Reinforcement Learning & Planning who can advance the state of the art in model-based RL, exploration strategies, optimal control, and Bayesian optimization. You will work on developing agents that can learn efficient policies in complex, partially observable environments by leveraging structured world models.
The immediate mission of MARA is to solve concrete challenges such as AutumnBench, physical and simulated robotics benchmarks, and the Abstract Reasoning Corpus (ARC), with the broader mission of building systems capable of learning in an open, growing portfolio of domains using human-comparable amounts of data and interaction.
Who we're looking for
  • Researchers holding a PhD in computer science, artificial intelligence, machine learning, cognitive science, or related fields.
  • Strong background in reinforcement learning, planning, MDPs, optimal control, and sequential decision making.
  • Experience in developing AI systems that combine neural and symbolic methods is highly valued.
  • Interest in foundational AI research and its applications to modeling, abstraction, and reasoning.
  • Individuals with a demonstrated track record in scientific research, evidenced through publications, technical reports, or impactful software projects.
  • Excited about solving real world problems and having positive societal impact.

Responsibilities
  • Conduct independent and collaborative research focused on the MARA project.
  • Develop new methods and algorithms for reinforcement learning, planning, and decision-making in AI systems.
  • Apply these methods to concrete challenges such as AutumnBench, physical and simulated robotics environments, and other domains.
  • Disseminate research findings through academic publications and presentations at leading conferences.
  • Provide mentorship to junior team members and contribute to the scientific discourse through seminars, workshops, and collaborative projects.
  • Develop and maintain open-source software
  • (Optionally) Publish and present findings in journals and conferences
  • Contribute to the culture and direction of Basis
Role Details
Exceptional candidates who may not meet all of the following criteria are still encouraged to apply.
  • FT/PT: This is a full-time position
  • In-person Policy: We are in the office four days a week. Be prepared to attend multi-day Basis-wide in-person events.
  • Location: This role is in-person in either New York City or Cambridge, MA.
  • Salary range: Competitive salary.
  • Start date: Immediate start possible.

Non-Discrimination Notice
Basis Research Institute provides equal employment opportunities without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or genetics and prohibits discrimination based on all protected characteristics.
Privacy Notice
By submitting your application, you grant Basis permission to use your materials for both hiring evaluation and recruitment-related research and development purposes. Your information may be processed in different countries, including the US. You retain copyright while providing Basis a license to use these materials for the stated purposes.
Read our full Global Data Privacy Notice here.