Robotics AI / Application Layer Engineer (AMR + Manipulation + Teleoperation)

Robotics AI / Application Layer Engineer (AMR + Manipulation + Teleoperation) ATHES Palo Alto, CA Job Details Part-time $70,403.02 - $84,786.42 a year 14 hours ago Qualifications AI models Software engineering PyTorch Software implementation Robotics engineering AI platforms (beyond public GPTs) Computational framework Model deployment Navigation systems Machine learning frameworks Full Job Description About the Company APEX is a Silicon Valley-based robotics startup building humanoid robots to automate labor-intensive and repetitive jobs across industries. We are currently offering part-time, in-person roles for individuals who want to gain hands-on experience in real-world robotics development. This is an opportunity to work closely with our engineering team on cutting-edge robotics systems. This position is ideal for students or recent graduates who are eager to build practical experience in robotics, AI, and hardware systems.

Location Requirement:

This is an in-person role. Applicants must currently reside in the Bay Area. About the Role We are looking for a Robotics AI / Application Layer Engineer to build the intelligence layer of our robotic systems, focusing on autonomous mobile robots (AMRs), robotic arms, and humanoid manipulation systems . You will be responsible for implementing and deploying advanced AI policies, teleoperation pipelines, and navigation systems that bring real-world robotic systems to life. This role sits at the intersection of robot learning, computer vision, and real-world robotics deployment . Key Responsibilities1. Teleoperation & Robot Learning Pipelines Build and maintain teleoperation pipelines using LeRobot and Hugging Face ecosystems Implement data collection systems for robotic manipulation tasks Support imitation learning workflows from human demonstrations Integrate real-time control interfaces for robotic arms and humanoids 2. Model Deployment for Robotic Manipulation Deploy trained manipulation models on real robotic arms Implement and optimize diffusion policy models for robot control Ensure robust sim-to-real transfer of learned policies Optimize inference latency for real-time execution on edge hardware 3. AMR Navigation & SLAM Systems Implement SLAM-based navigation systems for indoor AMR operation Work with LiDAR, depth cameras, and IMU data fusion Develop path planning and obstacle avoidance pipelines Integrate navigation stack with robot task planning systems 4. Robot Intelligence & Application Layer Design high-level behavior logic for autonomous robot tasks Integrate perception, planning, and control into unified pipelines Work closely with hardware and systems engineers to ensure full-stack functionality Support deployment, testing, and iteration in real-world environments Required Skills & Experience Strong experience in Python and robotics software development Experience with ROS2 (or ROS1), Gazebo, Isaac Sim, or equivalent Hands-on experience with: SLAM (2D/3D) Robot navigation stacks (Nav2 or similar) Computer vision or sensor fusion Familiarity with robot learning / imitation learning / reinforcement learning Experience with deep learning frameworks (PyTorch preferred) Understanding of robotic manipulation or mobile robotics systems Preferred / Bonus Skills Experience with diffusion policies for robotics Experience using Hugging Face / LeRobot / open robotics datasets Experience with robotic arms (UR, Franka, or custom manipulators) Experience deploying models on edge devices (Jetson, NVIDIA platforms) Familiarity with teleoperation systems and human-in-the-loop control Experience in simulation-to-real-world transfer What You'll Work On Building real-world autonomous robotic systems (AMRs + arms) Deploying state-of-the-art robot learning models into production Enabling robots to learn from human demonstrations Creating navigation and manipulation systems for indoor environments Why This Role Matters You will be directly responsible for the intelligence layer of our robotics platform , turning perception, learning, and control algorithms into real-world autonomous behavior. This is a high-impact role with direct influence on product performance in real environments.

Pay:

$70,403.02 - $84,786.42 per year

Work Location:

In person