World Model / Action Policy Researcher at Modal

New York, New York, United States

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$350,000 – $450,000Compensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Gaming, Artificial Intelligence, TechnologyIndustries

Requirements

5+ years of experience in deep learning research or reinforcement learning, with a focus on embodied agents or simulation environments
Strong foundation in representation learning and generative modeling, particularly using architectures such as diffusion models, VAEs, and transformers applied to video
Experience with world models and predictive control — understanding how to train models that simulate dynamics and plan actions in learned environments
Proficiency in reinforcement learning (RL, model-based RL, or imitation learning) and the ability to design and evaluate policy networks
Programming fluency in Python and deep learning frameworks such as PyTorch
Strong experimental skills — comfort with large-scale training, evaluation pipelines, and managing complex datasets or simulations
Publications or open-source contributions in areas like world modeling, simulation learning, or agent policies (strong plus)
In-person work in NYC, 5 days in the office
Ownership & scientific rigor: Seeing ideas through from concept to proof to deployment, writing clean, reproducible code, and maintaining high experimental validity
Performance and scaling mindset: Understanding compute efficiency, distributed training, and data bottlenecks
Curiosity-driven and result-oriented: Excited by open-ended problems, defining measurable goals, and shipping impactful systems
Gaming & simulation passion: Interest in interactive environments, physics-based simulations, or gaming AI; experience with Unity, Unreal Engine, or custom simulators (plus)

Responsibilities

Designing, training, and evaluating world models and action policies that operate within games and based on gaming data
Experimenting rapidly and iterating on architectures
Collaborating closely with product and engineering teams to bring research ideas to production
Working with a team to contribute to intelligent agents and simulation systems that understand, predict, and act in complex 3D environments

Skills

Key technologies and capabilities for this role

PythonPyTorchdeep learningreinforcement learningworld modelsdiffusion modelsVAEstransformersgenerative modelingrepresentation learningembodied agentssimulation environmentsmodel-based RLimitation learningpolicy networks

Questions & Answers

Common questions about this position

What is the salary range for this position?

The salary range is $350K - $450K.

Is this role remote or onsite, and where is it located?

This is an onsite role requiring 5 days in the office in NYC.

What skills and experience are required for this role?

Candidates need 5+ years in deep learning research or reinforcement learning focused on embodied agents or simulations, strong foundation in representation learning and generative modeling (diffusion models, VAEs, transformers for video), experience with world models and predictive control, proficiency in RL and policy networks, and Python/PyTorch fluency.

What is the company culture like for this research role?

The team emphasizes ownership and scientific rigor, a performance and scaling mindset for translating research to production, and being curiosity-driven yet result-oriented. They value working with a talented team of engineers and researchers on large-scale systems.

What makes a strong candidate for this researcher position?

Strong candidates have publications or open-source contributions in world modeling, simulation learning, or agent policies, plus passion for gaming, simulations, or tools like Unity or Unreal Engine.

Modal

Employee training and skill development platform

About Modal

Modal Learning focuses on improving employee performance through skill development for businesses. Their main product, the Modal Mastery Platform, uses active learning techniques, including live cohort sessions, labs, and one-on-one coaching, to help employees engage with the material effectively. Unlike competitors, Modal Learning offers a subscription model that provides structured eight-week training programs, aligning skill development with organizational goals. The company's goal is to empower employees and help organizations retain talent by providing clear career development paths.

San Francisco, CaliforniaHeadquarters

2021Year Founded

$30.8MTotal Funding

EARLY_VCCompany Stage

Consulting, EducationIndustries

51-200Employees

Risks

Competition from established players and emerging startups could dilute Modal's market share.

Focus on data and AI may limit appeal to companies seeking broader skill development.

Economic downturns could reduce corporate spending on employee training, impacting revenue.

Differentiation

Modal offers personalized technical skills training with on-demand coaching.

The platform uses cohort-based learning to enhance engagement and retention.

Modal's strategic skills planning aligns training with business goals.

Upsides

Increased demand for personalized learning experiences boosts Modal's market potential.

Growing emphasis on data and AI skills aligns with Modal's course offerings.

Subscription model provides steady revenue and predictable growth for Modal.

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