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Simulation Phd Jobs in Newark, NJ (NOW HIRING)

Mecka

Research Scientist, RL & Simulation

Manhattan, NY · On-site

$85 - $120/hr

MSc/PhD (or equivalent research experience) in robotics, ML, or a related field. * Strong hands-on experience with robot simulation and policy learning. * Proficiency in Python; solid engineering ...

Mecka AI

Research Scientist, RL & Simulation

New York, NY · On-site

$200K - $250K/yr

MSc/PhD (or equivalent research experience) in robotics, ML, or a related field. * Strong hands-on experience with robot simulation and policy learning. * Proficiency in Python; solid engineering ...

Aaru

Population Simulation Researcher

New York, NY · On-site

$250K - $800K/yr

You have a PhD in ML, computational social science, cognitive science, statistics, psychometrics ... Have published work on agent-based simulation, synthetic data, or LLM-based behavioral modeling

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Simulation Phd information

See Newark, NJ salary details

$40.8K

$129K

$199.2K

How much do simulation phd jobs pay per year?

As of Aug 22, 2026, the average yearly pay for simulation phd in Newark, NJ is $129,042.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,200.00 and $153,200.00 per year, depending on experience, location, and employer.

What is a Simulation PhD?

A Simulation PhD is a doctoral degree focused on the development, application, and analysis of simulation techniques across various fields such as engineering, physics, computer science, or healthcare. Students in this program conduct advanced research to create or improve models that simulate real-world processes or systems. The degree typically involves coursework, comprehensive exams, and an original dissertation contributing new knowledge to the field of simulation. Graduates often pursue careers in academia, research, industry, or government, where they apply simulation to solve complex problems.

What are the key skills and qualifications needed to thrive as a Simulation PhD?

To thrive as a Simulation PhD, you need advanced expertise in simulation modeling, mathematics, and data analysis, usually supported by a doctoral degree in a relevant field such as engineering or computer science. Proficiency with technical tools like MATLAB, Simulink, Python, or specialized simulation software, along with experience in programming and statistical analysis, is essential. Strong problem-solving skills, critical thinking, and effective communication are crucial soft skills for collaborating with interdisciplinary teams and presenting complex results. These abilities are important to drive innovation, ensure accurate research outcomes, and translate simulation results into practical solutions.

What are the typical research collaboration opportunities available to a Simulation PhD within academia or industry?

Simulation PhD holders often work closely with multidisciplinary teams, including engineers, computer scientists, and domain experts, to develop and validate complex models. In academia, collaboration may involve joint research projects, co-authored publications, or participation in research consortia. In industry, Simulation PhDs might partner with product development teams, data analysts, or IT professionals to solve real-world problems and optimize processes. These collaborations not only enhance research impact but also provide valuable networking and career advancement opportunities.

What job categories do people searching Simulation Phd jobs in Newark, NJ look for?

The top searched job categories for Simulation Phd jobs in Newark, NJ are:

Simulation Phd jobs near you
Infographic showing various Simulation Phd job openings in Newark, NJ as of August 2026, with employment types broken down into 74% Full Time, 24% Part Time, and 2% Contract. Highlights an 77% Physical, 5% Hybrid, and 18% Remote job distribution, with an average salary of $129,042 per year, or $62 per hour.

Research Scientist, RL & Simulation

Mecka

Manhattan, NY • On-site

$85 - $120/hr

Other

Posted 4 days ago

Job description

About Mecka AI

Mecka AI is building the data infrastructure layer for robotics and embodied AI.

We partner with leading AI labs and robotics companies to deliver high-quality, real-world datasets used to train, evaluate, and deploy robotic systems. Our work sits directly between research, data, and real-world execution — where model performance is dictated by data quality.

The Role

We are looking for a Research Scientist, RL & Simulation to own the RL + simulation engine that turns large-scale human demonstrations into scalable robot learning signals.

This is a research-meets-systems role: you’ll build simulation environments, retarget human motion to robot actions, train and evaluate policies, and drive sim-to-real transfer with clear metrics.

What You’ll Work On Simulation Environments
  • Build and maintain simulation environments for robotics learning (e.g., Isaac Sim / Isaac Gym, MuJoCo, Genesis, Habitat, ManiSkill).
  • Decide what environments and assets to build first to maximize learning velocity.
Retargeting (Human to Robot)
  • Convert human demonstrations into robot-executable trajectories.
  • Explore IK-based, optimization-based, and learning-based retargeting approaches.
Policy Learning & Evaluation
  • Train policies from demonstrations using imitation learning + RL:
    • Behavior Cloning, DAgger-style aggregation, Offline RL
    • PPO / SAC (or similar) when online fine-tuning is required
  • Define evaluation: success metrics, stress tests, generalization, and regression tracking.
Sim-to-Real
  • Drive transfer via domain randomization, system identification, contact modeling, and failure-mode analysis.
  • Use real data to identify domain gaps that matter.
Who You Are Required Background
  • MSc/PhD (or equivalent research experience) in robotics, ML, or a related field.
  • Strong hands-on experience with robot simulation and policy learning.
  • Proficiency in Python; solid engineering discipline (reproducible experiments, clean code, debugging).
  • Comfort working end-to-end: environment to data to training to evaluation.

Strong Signals:

  • Experience with manipulation, dexterous hands, or locomotion.
  • Experience with retargeting, IK, trajectory optimization, or differentiable simulation.
  • Deep intuition for what makes sim-to-real succeed or fail.
Why This Role
  • Define how Mecka turns egocentric human behavior into scalable robot learning signals.
  • High ownership, fast iteration, and direct connection to real-world datasets.
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