1

Manager Robotics Simulator Jobs in California (NOW HIRING)

Alutiiq Information Management, LLC a subsidiary of Alutiiq, LLC has an excellent opportunity for a ... Government This role involves the research, design, development, simulation, evaluation, and ...

Robotics Engineer

San Diego, CA · On-site

$90K - $105K/yr

Taxable Entity ALUTIIQ INFORMATION MANAGEMENT LLC Job Title Robotics Engineer Location CA San Diego ... Government. This role involves the research, design, development, simulation, evaluation, and ...

These testing and integration activities span virtual tests (e.g., simulation), subsystem and ... Ability to manage multiple projects simultaneously and prioritize tasks based on project goals and ...

These testing and integration activities span virtual tests (e.g., simulation), subsystem and ... Ability to manage multiple projects simultaneously and prioritize tasks based on project goals and ...

Dyna Robotics makes general-purpose robots powered by a proprietary embodied AI foundation model ... software/simulation), including 2+ years in a team lead or management role. * Technical Skills:

We prefer: * Robotics & Simulation: Proficiency in ROS2 and IsaacSIM for modeling robotic ... Vendor Management: Experience conducting FAT/SAT validation and managing technical deliverables ...

next page

Showing results 1-20

Manager Robotics Simulator information

What are some common challenges faced by a Manager Robotics Simulator and how can they be addressed?

Managers of Robotics Simulator teams often encounter challenges such as integrating new simulation technologies, ensuring accurate modeling of real-world robotics environments, and facilitating effective collaboration between simulation engineers and robotics developers. To overcome these hurdles, it's important to stay updated with the latest simulation tools, foster clear communication across multidisciplinary teams, and implement structured testing processes. Regular team meetings and cross-functional workshops also help ensure alignment on project goals and technical requirements.

What is the difference between Manager Robotics Simulator vs Robotics Engineer?

AspectManager Robotics SimulatorRobotics Engineer
Required CredentialsBachelor's or master's in robotics, engineering, or related field; leadership experienceBachelor's or master's in robotics, mechanical, electrical, or software engineering
Work EnvironmentTeam leadership, project management, overseeing simulation developmentDesign, develop, and test robotic systems and simulations
Employer & Industry UsageTech companies, research labs, manufacturing firmsRobotics firms, automation companies, research institutions

The Manager Robotics Simulator primarily oversees simulation projects, manages teams, and ensures project delivery, focusing on leadership and coordination. In contrast, a Robotics Engineer is hands-on, involved in designing and developing robotic systems and simulations. Both roles require technical expertise, but the Manager focuses on management, while the Engineer emphasizes technical development.

What are Manager Robotics Simulators?

A Manager Robotics Simulator oversees the development, implementation, and maintenance of robotics simulation software and environments. They manage teams of engineers and developers to ensure high-quality, accurate simulations that support the testing and training of robotic systems. This role bridges technical expertise with leadership responsibilities, coordinating projects, setting goals, and ensuring timely delivery of simulation products. Additionally, they often collaborate with other departments to integrate simulation tools into larger robotics workflows.

What are the key skills and qualifications needed to thrive as a Manager Robotics Simulator, and why are they important?

To thrive as a Manager Robotics Simulator, you need expertise in robotics engineering, simulation software, and team leadership, often supported by a degree in robotics, engineering, or computer science. Familiarity with simulation platforms (such as Gazebo or ROS), programming languages (like Python or C++), and project management tools is typically required, along with relevant certifications. Strong communication, problem-solving, and organizational skills enable effective collaboration and project execution. These competencies are crucial for ensuring high-quality simulation environments, leading teams, and delivering innovative robotics solutions efficiently.
What are the most commonly searched types of Robotics Simulator jobs in California? The most popular types of Robotics Simulator jobs in California are:
What are popular job titles related to Manager Robotics Simulator jobs in California? For Manager Robotics Simulator jobs in California, the most frequently searched job titles are:
What job categories do people searching Manager Robotics Simulator jobs in California look for? The top searched job categories for Manager Robotics Simulator jobs in California are:
What cities in California are hiring for Manager Robotics Simulator jobs? Cities in California with the most Manager Robotics Simulator job openings:

Machine Learning Engineer - Robot Manipulation

Maven Robotics

San Francisco, CA

Other

Posted 12 days ago


Job description

Company Overview

Maven Robotics is building the world's leading general-purpose AI robots.

We are currently operating in stealth and are growing the world's best team in AI robotics. We are looking for self-starters that are the world's best in their field, who can innovate from a deep understanding of the fundamentals, and who share our values of unwavering truth seeking and integrity, humility, curiosity, and relentless determination.

Role Description

We are looking to recruit an exceptional Machine Learning Engineer - Robot Manipulation to design, implement, test, and deploy robot manipulation algorithms that enable assembly and material movement tasks.

In this role you will:

  • Design and implement machine learning algorithms, with a focus on reinforcement learning (RL) and imitation learning (IL), to enable robotic manipulators to perform complex tasks in dynamic environments.
  • Translate high-level objectives into machine learning problems and deploy robust, scalable models to real-world robotic systems.
  • Integrate your ML solutions into existing robotics workflows, ensuring that models are performant in both simulated and real-world settings.
  • Drive innovation by incorporating the latest research in machine learning into practical applications that push the boundaries of robotic manipulation.
  • Take ownership of critical ML projects, seeing them through from conception to successful deployment.
  • Collaborate across disciplines to ensure seamless integration of ML models and provide technical mentorship to junior engineers.
Qualifications

Must-have:

  • MS or PhD in machine learning, computer science, robotics, or a related field.
  • Strong practical experience in training and deploying machine learning models for real-world applications.
  • Deep understanding of reinforcement learning (RL) and imitation learning (IL) and their application to robotics.
  • Proficiency in programming languages and tools commonly used in machine learning (e.g., Python, PyTorch).
  • Experience with data collection, preprocessing, and management in the context of training ML models.
  • Self-starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
  • Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.

Nice-to-have:

  • Familiarity with robotic simulation environments (e.g., Gazebo, MuJoCo) and experience in sim-to-real transfer.
  • Experience in:
    • Designing and implementing reward functions for complex manipulation tasks.
    • Developing models that can handle noisy, incomplete, or sparse data.
    • Deployment of ML models to edge devices for real-time inference.
    • Accelerating ML training processes using GPU, TPU, or other HW accelerators.
    • Using reinforcement learning frameworks, e.g. Stable Baselines, RLlib, or similar.
  • General knowledge of robotics principles, including kinematics, dynamics, and control.
  • Publications or contributions to the machine learning community, particularly in areas related to robotics or reinforcement learning.