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Stealth Ai Robotics Startup Jobs (NOW HIRING)

Dexmate

Robot learning engineer

Santa Clara, CA · On-site

Dexmate is building the foundation for physical AI, creating a unified platform that combines high ... Dexmate is a robotics startup developing general-purpose mobile robots with manipulation ...

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Stealth Ai Robotics Startup information

What is the difference between Stealth Ai Robotics Startup vs Robotics Engineer?

AspectStealth Ai Robotics StartupRobotics Engineer
Required CredentialsBachelor's or Master's in Robotics, AI, or related fieldsBachelor's or Master's in Robotics, Mechanical, Electrical Engineering
Work EnvironmentFast-paced, innovative startup setting, often in early development stagesCorporate or research labs, project-based, with structured teams
Industry UsageEmerging AI-driven robotics solutions, startup-focusedDesign, develop, and test robotic systems across industries

While both roles require a background in robotics and AI, a Stealth Ai Robotics Startup typically involves working in a dynamic, early-stage environment focused on innovative AI applications. In contrast, a Robotics Engineer often works in established companies or research settings, focusing on designing and improving robotic systems. The startup role emphasizes adaptability and rapid development, whereas the engineering role may involve more structured project work.

How much do stealth startups pay?

Salaries at stealth AI robotics startups vary widely depending on the role, experience, and funding stage, but they generally offer competitive compensation to attract specialized talent. Entry-level positions may start around $70,000 to $100,000 annually, while experienced engineers and researchers can earn $150,000 or more, often supplemented with equity or stock options. Compensation packages often reflect the startup environment, requiring skills in AI, robotics, and software development.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, often involving advanced skills in machine learning, data science, or robotics. Such roles may require extensive experience, specialized knowledge, and may include executive or senior-level responsibilities in innovative tech companies or startups like stealth AI robotics firms.

What is the salary of stealth robotics startup?

Salaries for roles at a stealth AI robotics startup vary depending on the position, experience, and location, but generally range from $80,000 to $150,000 annually for technical roles such as robotics engineers or AI specialists. Compensation may also include stock options or bonuses, especially for senior or specialized positions. Entry-level roles typically start lower, around $70,000, while senior roles can exceed $200,000 with additional benefits.

Is the Stealth AI startup legit?

As a job seeker, it's important to verify the legitimacy of a startup by researching its company website, reviews, and any publicly available information. Stealth AI startups often operate in early development stages, which can limit public details, so caution and due diligence are advised before engaging or accepting offers.
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Stealth Ai Robotics Startup jobs near you
Infographic showing various Stealth Ai Robotics Startup job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 24% Part Time, and 3% Contract. Highlights an 65% Physical, 3% Hybrid, and 32% Remote job distribution.

Machine Learning Engineer - Robot Perception

Maven Robotics

San Francisco, CA

Other

Posted 26 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 Perception to design, implement, test, and deploy robot perception algorithms that power our robots' ability to understand and interact with the world.

In this role you will:

  • Develop, train, and deploy ML-based perception algorithms for object detection, pose estimation, tracking, and scene understanding.
  • Integrate sensor fusion techniques using cameras, depth sensors, IMUs, and tactile feedback.
  • Optimize real-time perception pipelines for low-latency and robust performance in dynamic environments.
  • Work closely with hardware engineers to design sensor configurations and optimize perception models for onboard deployment.
  • Contribute to our broader AI and autonomy stack, ensuring seamless integration with reasoning, manipulation, planning and control.
  • 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 background in computer vision, deep learning, and sensor fusion.
  • Proficiency in Python and C++, with experience in frameworks like PyTorch, TensorFlow, OpenCV, and ROS.
  • Hands-on experience with real-world robotics perception systems (e.g., SLAM, 3D reconstruction, multimodal perception).
  • Experience working with hardware, including setting up and calibrating cameras, LiDAR, and other sensors.
  • 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:
    • Developing models that can handle noisy, incomplete, or sparse data.
    • Deployment of ML models to edge devices for real-time inference (e.g., NVIDIA Jetson).
    • Accelerating ML training processes using GPU, TPU, or other HW accelerators.
    • General knowledge of robotics principles, including kinematics, dynamics, and control.

Apply