Peppermint Robotics

Location: US remote/ on site Pune
Job Overview: We are seeking a Lead Robotics Perception Engineer - SLAM to join our core autonomy team. This role focuses on leading development and optimization of 3D LiDAR-based SLAM solutions, enabling reliable and accurate localization and mapping for autonomous robots deployed across complex, real-world environments. We are looking for engineers who are not only experts in state-of-the-art SLAM solutions but also able and willing to build our senior talent pool through mentoring and collaboration.
 Key Responsibilities:
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SLAM System Development: Develop robust, real-time SLAM systems using 3D LiDARs, 2D LiDARs, cameras, and other onboard sensors.
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Optimization: Design and implement factor graph-based optimization pipelines using GTSAM, Ceres Solver, G2O.
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Sensor Fusion: Integrate SLAM with IMU, wheel odometry, and vision-based systems.
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Testing &amp; Validation: Continuously test and validate SLAM modules across diverse real-world deployments, including crowded airports, large malls, and hospitals.
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Cross-Functional Collaboration: Collaborate closely with perception, controls, and navigation teams to ensure seamless robot autonomy.
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Performance Tuning: Profile and optimize SLAM performance for resource-constrained embedded compute platforms.
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Team Leadership: Serve as a technical leader in SLAM. Mentor junior team members by sharing knowledge and skills gained through your experience.
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Supply Chain Curation: Share ownership of sensor selection with other technical leads.
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Logistics: Able and willing to work with teammates in India, including at least two hours of overlap with IST (India Standard Time) for most days and rare travel to India for team-building.
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 Minimum Qualifications
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Education: Master’s degree in Robotics, Computer Science, Electrical Engineering, or a related field, including published SLAM research in respected venues.
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Experience: History of developing and delivering SLAM systems on multiple shipped robotics products (typically requiring several years of industry experience) that successfully operate in large, dynamic indoor environments.
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Advanced SLAM Expertise: Proven expertise with 3D LiDAR-based SLAM, visual SLAM, loop closure, global mapping, multi-session SLAM, and lifelong mapping.
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Theoretical Knowledge: Strong grasp of Lie algebra, probabilistic estimation, sensor fusion, and graph optimization.
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Programming: Proficient in C++ and Python in a Linux-based development environment.
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Optimization Tools: Experience with GTSAM, iSAM2, Ceres Solver, or G2O.
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Middleware: Familiarity with ROS/ROS2 and standard robotics middleware.
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Preferred Qualifications
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Education: PhD focusing on SOTA SLAM methods.
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Point Cloud Processing: Familiarity with PCL, Open3D, or similar libraries.
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Research &amp; Community: Contributions to open-source SLAM projects or publications in leading conferences (e.g., ICRA, IROS, RSS).
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Embedded Systems: Knowledge of performance optimization on resource-constrained hardware.
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Supply Chain: experience in selecting SLAM-related sensors such as LiDARs, depth cameras, RGB cameras, IMUs.
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AI Proficiency: Demonstrated ability to use LLMs (Claude or Gemini) to accelerate software development and solve complex technical challenges.
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