Title
SLAM Engineer
Quick Summary
TopoSense Labs is hiring a SLAM Engineer to design, optimize, and productionize visual–inertial and lidar-based localization and mapping pipelines for robots operating in GPS-denied, dynamic environments. The role blends modern C++ and Python development with ROS 2 integration, sensor fusion, and performance tuning from simulation to field deployment. We welcome strong graduates and early-career engineers who have shipped projects or published code, while experienced candidates will be considered for broader technical scope.
Project Category or Industry
Robotics, computer vision, and autonomous systems for logistics, manufacturing, inspection, and smart infrastructure.
Type
Full-time employment.
Experience Level
Entry to mid-level with structured mentorship and clear growth paths; senior candidates are encouraged to apply and may lead workstreams.
Duration
Permanent role.
Location
Remote-first with optional hybrid hubs in Zürich, Switzerland and Toronto, Canada. Maintain at least four hours of collaboration overlap with teams operating between UTC−5 and UTC+2. Occasional travel for data collection, on-site pilots, and customer reviews.
Salary
USD 106,000–158,000 base depending on location and experience, plus benefits, equity eligibility, and an annual performance bonus.
Payment Mode
Monthly payroll for employees; compliant contractor arrangements available in select countries.
Hiring Company Name
TopoSense Labs
Required Skills or Tools
Candidates should demonstrate strong C++ (C++17 or newer) and practical Python, fluency with ROS 2 and DDS, and a solid grasp of sensor fusion, visual odometry, and mapping. Familiarity with time synchronization, calibration, pose graph optimization, and reproducible testing is important.
Project Description
TopoSense Labs builds localization and mapping systems that allow robots to navigate reliably where GPS is unavailable or unreliable. As a SLAM Engineer, you will craft robust algorithms and software that transform noisy camera, lidar, and inertial data into stable, drift-bounded state estimates and consistent maps. Your work will span simulation, bag replay, and real-robot testing, with a focus on predictable behavior, failure awareness, and clear diagnostics that operators can trust.
Core Responsibilities and Expected Deliverables
Design, implement, and benchmark visual–inertial odometry, lidar odometry, and multi-sensor fusion pipelines with well-defined interfaces and QoS in ROS 2.
Build loop-closure, place-recognition, and pose-graph optimization modules; deliver tools for map lifecycle management, anchoring, and serialization.
Establish timing-safe data paths with accurate time bases and extrinsic calibration; document assumptions, latencies, and known limitations.
Create scenario libraries and regression tests from rosbag2 logs and simulation; publish reliable metrics and dashboards to track drift, robustness, and CPU/GPU budgets.
Support field deployments, triage issues using logs and telemetry, and feed learnings back into algorithms and code.
Required Experience and Preferred Qualifications
Proven skill in modern C++ and Python; strong foundations in linear algebra, optimization, and probabilistic estimation.
Hands-on experience with at least one of VIO, lidar odometry, or multi-modal fusion (EKF/UKF, factor graphs).
Preferred exposure to GPU acceleration (CUDA), learned features for place recognition, and large-scale mapping.
Familiarity with embedded Linux, real-time considerations, and resource-aware design for edge compute.
Evidence of impact via internships, open-source contributions, competition teams, or shipped prototypes.
Tools or Platforms to Be Used
Middleware: ROS 2 (rclcpp/rclpy), Fast DDS or Cyclone DDS; rosbag2 for replay.
Perception & math: OpenCV, PCL, Eigen, Ceres/Sophus; optional TensorRT for accelerated inference.
Simulation & evaluation: Isaac Sim, Gazebo/Ignition, CARLA; custom scenario generators; Dockerized batch pipelines.
Build & CI: CMake or Bazel, GitHub Actions, clang-tidy, sanitizers; Grafana/Prometheus for metrics.
Interfaces: SocketCAN, serial, gRPC for telemetry and control bridges.
Language Requirement
Professional English is required. German or French is a plus for Zürich-based work; additional languages are welcome.
Communication Style
Written-first collaboration through design docs and pull requests; Slack for daily coordination; Zoom for stand-ups, design reviews, and field debriefs. All modules include concise docs and change logs.
Time Commitment or Working Window
Standard 40 hours per week with flexible scheduling. Maintain a predictable daily block with at least four hours overlapping the core team between 09:00 and 17:00 in your local time. Field tests may occasionally require short early-morning or evening windows, planned in advance.
Payment Terms
Salary paid monthly via payroll. For contractors, invoices are processed on net-30 terms upon acceptance of deliverables and timesheets.
Evaluation Criteria
Portfolio or code samples showing SLAM depth, algorithmic rigor, and clean interfaces.
Practical exercise: implement a ROS 2 package that fuses IMU and camera or lidar for odometry with loop closure and a reproducible evaluation harness.
System design interview on timing, failure modes, and map lifecycle; discussion of metrics and guardrails.
Collaboration and communication interview emphasizing documentation and cross-team alignment.
References may be requested.
Other Requirements
New hires sign a confidentiality agreement and follow security and data-handling policies. Light time-tracking may be used for distributed coordination. Export control compliance may apply based on nationality and project assignment.
About TopoSense Labs
TopoSense Labs is a privately held robotics company focused on reliable localization and mapping for autonomous systems. With a distributed team centered in Zürich and Toronto, the company partners with manufacturers and integrators to turn research-grade algorithms into dependable products. Learn more at htt