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Slam Autonomous Navigation Jobs (NOW HIRING)

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Robotics Software Engineer

Santa Monica, CA · On-site

$65K - $85K/yr

This is a full-time, hands-on role focused on autonomy, navigation, and system integration. What You'll Do - Develop robotics software using ROS 2 - Work on SLAM, localization, and navigation systems ...

Anduril Industries

Computer Vision Engineer

Costa Mesa, CA · On-site

$191K - $253K/yr

... SLAM algorithms to create accurate 3D models from multiple camera inputs in real-time. * Integrate perception outputs with path planning algorithms to enable autonomous navigation in complex ...

Saronic Technologies

Software Engineer, Generalist

Austin, TX · On-site

$96K - $132K/yr

Familiarity with sensor fusion techniques, SLAM algorithms, and other technologies relevant to autonomous navigation and perception * Strong problem-solving skills and the ability to work effectively ...

Leidos

Navigation and Autonomy Engineer

Huntsville, AL

$69K - $125K/yr

... of autonomous systems for customers including DARPA, NASA, AFRL, ONR, and the U.S. Army. You'll ... Simultaneous Localization and Mapping (SLAM) * Machine learning approaches for all of the above

Leidos

Navigation and Autonomy Engineer

Huntsville, AL · On-site

$69K - $125K/yr

... of autonomous systems for customers including DARPA, NASA, AFRL, ONR, and the U.S. Army. You'll ... Simultaneous Localization and Mapping (SLAM) * Machine learning approaches for all of the above

GRENZEBACH

Software Engineer

Newnan, GA · On-site

SLAM navigation algorithms * Autonomous robot localization and mapping * Robot motion control * Multi-robot fleet management systems * Industrial automation and controls integration * Sensor fusion ...

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Slam Autonomous Navigation information

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$29K

$105.6K

$169K

How much do slam autonomous navigation jobs pay per year?

As of Jun 5, 2026, the average yearly pay for slam autonomous navigation in the United States is $105,605.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,500.00 and $127,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced when working in SLAM Autonomous Navigation roles, and how can they be addressed?

Professionals in SLAM (Simultaneous Localization and Mapping) Autonomous Navigation often encounter challenges such as dealing with sensor noise, real-time processing constraints, and dynamic environments where objects move unpredictably. Overcoming these challenges typically involves implementing robust sensor fusion algorithms, optimizing computational efficiency, and continuously testing navigation systems in varied real-world scenarios. Collaboration with multidisciplinary teams, including hardware engineers and software developers, is vital to iteratively improve system reliability and adaptability.

What are the key skills and qualifications needed to thrive as a SLAM Autonomous Navigation Engineer, and why are they important?

To thrive as a SLAM Autonomous Navigation Engineer, you need expertise in robotics, computer vision, and probabilistic algorithms, typically supported by a degree in computer science, robotics, or a related field. Familiarity with tools like ROS (Robot Operating System), C++/Python programming, and experience with sensor integration (e.g., LiDAR, cameras, IMUs) are commonly required. Strong problem-solving skills, attention to detail, and the ability to work collaboratively in multidisciplinary teams are important soft skills. These competencies are crucial for developing reliable and efficient autonomous navigation systems that safely interpret and interact with complex real-world environments.

What is the difference between Slam Autonomous Navigation vs SLAM Technician?

AspectSlam Autonomous NavigationSLAM Technician
Required CredentialsAdvanced degrees in robotics, computer science, or related fields; experience with autonomous systemsTechnical certifications in robotics, sensor calibration, and SLAM software; hands-on experience
Work EnvironmentResearch labs, autonomous vehicle development, field testingInstallation, maintenance, and troubleshooting of SLAM systems in various settings
Industry UsageAutonomous vehicles, robotics, mapping, navigationRobotics companies, research institutions, industrial automation

While Slam Autonomous Navigation focuses on developing and implementing autonomous navigation systems using SLAM algorithms, SLAM Technicians primarily handle the installation, calibration, and maintenance of SLAM hardware and software. Both roles require knowledge of SLAM technology but differ in scope and responsibilities within the robotics and autonomous systems industry.

What is SLAM Autonomous Navigation?

SLAM Autonomous Navigation refers to the use of Simultaneous Localization and Mapping (SLAM) techniques in robots or vehicles to autonomously navigate unknown environments. Using sensors like LiDAR, cameras, or sonar, SLAM algorithms build a map of the surroundings while tracking the robot’s position within it. This enables robots to move safely without prior knowledge of the environment and is widely used in robotics, drones, and self-driving cars. SLAM is crucial for real-time decision-making and efficient path planning in dynamic or unstructured settings.
Infographic showing various Slam Autonomous Navigation job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 95% Full Time, 1% Temporary, and 3% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $105,605 per year, or $50.8 per hour.

Senior / Staff Robotics Engineer, Motion Planning

RoboForce

Milpitas, CA • On-site

$121K - $167K/yr

Full-time

Medical, Dental, Vision, Retirement

Posted 15 days ago

Job description

Why RoboForce
RoboForce is an AI robotics company developing Physical AI-powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.
We are seeking a Senior / Staff Robotics Engineer, Motion Planning to own the local mobility and safety stack for our fleet. In this role, you will bridge the gap between AI-driven perception and physical hardware execution. You will consume traversability and semantic costmaps generated by our AI team and translate them into safe, dynamically feasible, and deterministic motion. You are solving the physics of mobility on unpredictable terrain, ensuring our humanoid platform respects its physical limits, shifting center of mass, and traction constraints.
Responsibilities
  • Dynamic Motion Planning: Architect and implement advanced local planners (e.g., optimization-based approaches like MPC, MPPI, or custom contouring control) that translate AI-generated routes into trajectories that a heavy, non-holonomic base can physically execute on uneven terrain.
  • AI Integration & Costmap Consumption: Build the high-performance C++ pipelines that ingest the AI team's neural network outputs (semantic maps, learned costmaps) in real-time and convert them into deterministic mathematical constraints for the planner.
  • Center of Mass (CoM) Aware Navigation: Ensure local planners dynamically adjust to real-time changes in the robot's Center of Mass caused by the lifting column and dual-arm payloads. Your trajectories must guarantee stability and prevent tipping on slopes or rough ground.
  • Deterministic Safety Architectures: AI models hallucinate. You will build the deterministic C++ safety layer and fallback behaviors that override AI inputs if a commanded path violates physical safety constraints or if off-the-shelf SLAM tracking drops.
  • WBC Integration: Architect the local planning interfaces to seamlessly pass base trajectories, velocity limits, and dynamic constraints to our upcoming Whole-Body Control (WBC) architecture, bridging the gap between base mobility and upper-body manipulation.
Requirements
  • Education: Ph.D. in Robotics, Mechanical Engineering, Computer Science, or a related field, OR an M.S. degree with 4+ years of relevant industry experience.
  • Motion Planning Expertise: Deep hands-on experience developing and tuning advanced local planners and trajectory optimization algorithms for mobile robots in complex, unstructured environments.
  • Rigid Body Dynamics: Strong understanding of kinematics, vehicle dynamics, and center-of-mass constraints for top-heavy mobile robots operating on uneven outdoor terrain.
  • C++ Performance Engineering: Expertise in modern C++ (C++17/20), with strict attention to real-time performance, memory management, and low-latency execution. You must be comfortable bridging high-latency AI outputs with hard real-time control loops.
  • Field Deployment: Proven track record of building and deploying autonomous navigation stacks on real, physical robotic platforms in the wild.
  • Requires 5 days/week in-office collaboration with the teams.
Bonus Qualifications
  • WBC / Manipulation: Familiarity with Whole-Body Control frameworks, operational space control, and how mobile bases synchronize with high-DOF manipulators.
  • Applied SLAM / State Estimation: Experience tuning, debugging, or filtering outputs from commercial SLAM/VIO systems when they fail in visually degraded environments.
  • Slip & Traction Estimation: Background in detecting slip or loss of traction from proprioceptive sensor signals and adapting planning constraints accordingly.
  • GPU Acceleration: Experience using CUDA to accelerate grid processing or trajectory sampling pipelines.

Benefits
  • Competitive stock options/equity programs.
  • Health, dental, and vision insurance, 401(k) plan.
  • Visa sponsorship and green card support for qualified candidates.
  • Lunches and dinners, a fully stocked kitchen, and regular team-building events.

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