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Slam Engineer Jobs in Michigan (NOW HIRING)

WSP

Senior Geomatics Specialist

Detroit, MI · On-site

... engineers, and project managers to ensure accurate field-to-finish workflows. * Provide training and guidance on survey principles, GNSS, dimensional control methods, SLAM scanning, and terrestrial ...

Qualcomm

Senior Localization Systems Engineer

Novi, MI · On-site

$120K - $150K/yr

Applies expertise with Kalman Filters, including EKF & UKF, Particle Filters, and SLAM theory ... Engineering, Computer Engineering, Computer Science or related degree field and six (6) months of ...

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$25.3K

$92K

$147.3K

How much do slam engineer jobs pay per year?

As of Jul 16, 2026, the average yearly pay for slam engineer in Michigan is $92,045.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,800.00 and $110,700.00 per year, depending on experience, location, and employer.

What is a SLAM Engineer job?

A SLAM (Simultaneous Localization and Mapping) Engineer develops algorithms and systems that enable machines, like robots or AR/VR devices, to map their environment while tracking their own position. They work with sensor fusion, computer vision, and machine learning to enhance real-time navigation and spatial awareness. This role is crucial in robotics, autonomous vehicles, and augmented reality applications. A SLAM Engineer typically has expertise in C++, Python, ROS, and technologies like LiDAR and visual odometry.

What engineers make $300,000 a year?

Senior engineers in specialized fields such as software engineering, petroleum engineering, and aerospace engineering can earn $300,000 or more annually, especially with extensive experience, advanced skills, and working in high-demand industries or leadership roles. Compensation often includes base salary, bonuses, and stock options, particularly in tech companies or energy sectors.

What are the main projects and responsibilities I can expect as a SLAM Engineer?

As a SLAM Engineer, you'll typically be responsible for developing, optimizing, and integrating algorithms that enable robots or autonomous devices to navigate and map their environments. This may involve working with sensor data from LiDAR, cameras, or IMUs, implementing real-time data processing pipelines, and testing your solutions in both simulated and real-world scenarios. You’ll often collaborate closely with robotics hardware engineers, software developers, and research scientists to ensure reliable system performance. Additionally, you may participate in field deployments, debugging sessions, and ongoing enhancements to keep up with the latest research and industry trends.

What is a slam engineer?

A slam engineer specializes in designing and implementing systems that process large volumes of data in real-time, often focusing on data ingestion, transformation, and analysis. They typically work with big data tools and programming languages like Python or Java, and may require knowledge of data pipelines, cloud platforms, and performance optimization. The role is common in data engineering and analytics environments.

What are the key skills and qualifications needed to thrive in the Slam Engineer position, and why are they important?

To thrive as a SLAM Engineer, you need expertise in robotics, computer vision, and probabilistic state estimation, usually supported by a degree in computer science, robotics, or electrical engineering. Familiarity with tools like ROS (Robot Operating System), C++/Python programming, and experience with libraries such as OpenCV, PCL, or GTSAM are typically required. Strong problem-solving ability, attention to detail, and effective teamwork are crucial soft skills in this position. These capabilities are vital for developing and maintaining robust simultaneous localization and mapping solutions, ultimately ensuring high-performance autonomous systems.

Which 3 jobs will survive AI?

Slam Engineers, involved in designing and maintaining automated systems, are likely to continue working alongside AI due to the need for specialized technical skills and hands-on problem-solving. Jobs requiring complex judgment, creativity, and emotional intelligence—such as healthcare professionals and skilled tradespeople—are also expected to persist despite AI advancements.

What engineer makes $500,000 a year?

Slam engineers, a specialized role in the construction or industrial sectors, typically do not earn $500,000 annually. High salaries in engineering usually occur in executive, senior management, or highly specialized fields such as petroleum, aerospace, or software engineering, where top professionals with extensive experience and advanced skills can reach or exceed this level. Most engineering roles offer salaries significantly below this threshold, with top-tier executives or consultants earning higher compensation.
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Slam Engineer jobs near you
Infographic showing various Slam Engineer job openings in Michigan as of July 2026, with employment types broken down into 93% Full Time, 4% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $92,045 per year, or $44.3 per hour.
Autonomous Driving Vehicle Perception Engineer

Autonomous Driving Vehicle Perception Engineer

INA Solution Inc

Northville, MI • On-site

$80K - $100K/yr

Full-time

Re-posted 2 days ago

Job description

Job Title: Autonomous Driving Vehicle Perception Engineer

Location: Northville, MI (Onsite)

Experience: 4–10 Years

Salary: $80K-$100K

What You will Do:

  • Design and implement advanced perception algorithms for autonomous vehicles using LiDAR, cameras, radar, and GNSS.
  • Develop and optimize sensor fusion techniques to combine data from multiple sensors, improving the accuracy and reliability of perception systems.
  • Create algorithms for object detection, tracking, semantic segmentation, and classification from 3D point clouds (LiDAR) and camera data.
  • Work on Simultaneous Localization and Mapping (SLAM) algorithms, including Graph SLAM, LIO-SAM, and visual-inertial SLAM.
  • Develop sensor calibration techniques (intrinsic and extrinsic) and coordinate transformations between sensors.
  • Participate in real-time systems design and optimization to meet the high-performance requirements of autonomous driving.
  • Work with ROS2 for integration and deployment of perception algorithms.
  • Develop, test, and deploy machine learning models for perception tasks such as object detection and segmentation.
  • Collaborate with cross-functional teams, including software engineers, data scientists, and hardware teams, to deliver end-to-end solutions.
  • Stay up-to-date with industry trends and emerging technologies to innovate and improve perception systems.

 

What You Will Bring:

  • Minimum 3+ years of experience in sensor calibration, multi-sensor fusion, or related domains.
  • Strong foundation in linear algebra, 3D geometry, coordinate frames, quaternions, probability, Bayesian filtering, and data association.
  • Hands-on experience with intrinsic and extrinsic calibration of LiDAR, cameras, and radar, including geometric calibration, coordinate transforms, and sensor synchronization.
  • Proven experience with perception algorithms for autonomous systems, particularly in the areas of LiDAR, camera, radar, GNSS, or other sensor modalities.
  • Deep understanding of LiDAR technology, point cloud data structures, and processing techniques; experience with PCL or Open3D.
  • Proficiency in sensor fusion for combining data from LiDAR, camera, radar, and GNSS, including handling time synchronization and motion distortion.
  • Solid background in computer vision techniques; experience with OpenCV and object detection models such as YOLO, Faster R-CNN, or SSD.
  • Experience with deep learning frameworks (TensorFlow or PyTorch) for object detection and segmentation tasks.
  • Hands-on experience with multi-object tracking algorithms such as SORT, DeepSORT, Kalman Filters, UKF, IMM, or JPDA.
  • Strong programming skills in C++ and Python; familiarity with geometric optimization libraries.
  • Familiarity with ROS2 for perception-based autonomous systems development.
  • Experience with parallel computing for real-time performance optimization (e.g., CUDA, OpenCL).