Job Description: Autonomous Navigation Specialist {robotics engineer} Role Overview We are seeking an experienced Autonomous Navigation Specialist to design, develop, and optimize advanced navigation systems for autonomous vehicles, drones, and robotic platforms. The ideal candidate will have a proven track record of building robust systems that can operate independently and safely in GPS-denied and complex environments. This role requires deep expertise in algorithm development, multi-sensor fusion, and real-world deployment of navigation stacks. Key Responsibilities Lead the design and implementation of Simultaneous Localization and Mapping (SLAM) pipelines for autonomous platforms. Develop and optimize path planning and motion control algorithms to enable safe and efficient navigation. Integrate and calibrate diverse sensors, including IMUs, LiDARs, cameras, GNSS, and radar , into a cohesive navigation framework. Implement and maintain sensor fusion algorithms (e.g., EKF, UKF, graph-based optimization). Conduct extensive simulation, testing, and validation using tools like ROS/ROS2, Gazebo, CARLA, or similar platforms. Collaborate with hardware, perception, and AI teams to deliver end-to-end autonomous navigation solutions. Troubleshoot system performance in field tests and iteratively improve reliability under real-world conditions. Mentor junior engineers and contribute to technical roadmaps and research directions. Required Skills & Experience Strong background in robotics, computer vision, or control systems (Master’s or PhD preferred). 2+ years of hands-on experience developing and deploying navigation systems for autonomous drones, robots, or vehicles. Deep expertise in SLAM (visual, LiDAR, or visual-inertial) , path planning , and multi-sensor fusion . Proficiency in C++ and Python , with extensive experience in ROS/ROS2 . Strong understanding of control theory, state estimation, and probabilistic robotics . Experience with simulation tools (Gazebo, CARLA, AirSim, or equivalent). Familiarity with GPU/embedded platforms (NVIDIA Jetson, FPGA, or similar). Demonstrated ability to solve complex problems and deliver production-ready systems. Preferred Qualifications Experience with autonomous drones (UAVs) and ground robots (UGVs) . Publications or patents in navigation, robotics, or related domains. Hands-on experience with field testing of autonomous navigation systems. Knowledge of safety standards and regulatory requirements for autonomous systems. What We Offer Opportunity to work on cutting-edge autonomous systems with global impact. A highly collaborative environment with hardware and AI experts. Growth opportunities in a fast-scaling deep-tech startup.
About the Role Phoqtek Labs is seeking an exceptional Drone Systems Engineer who possesses deep expertise across both aerial hardware design and autonomous navigation software . The candidate will be responsible for the design, integration, and deployment of next-generation drones equipped with Visual Navigation Systems (VNS) , capable of GPS-denied operation and AI-assisted autonomy . This is not a plug-and-play assembly role you will architect the entire system stack , from sensor fusion and flight control integration to real-time perception and control systems optimized on edge devices like NVIDIA Jetson Orin Nano/Xavier . Key Responsibilities Hardware Design & Integration Design, assemble, and optimize multi-rotor and fixed-wing drones , including frame selection, motor & ESC configuration, propeller dynamics, and payload balancing. Integrate onboard compute modules (Jetson Orin, Raspberry Pi, etc.) with PX4 or ArduPilot flight controllers (CubePilot Orange+, Pixhawk 6X, etc.). Develop and test power distribution systems , LiPo battery configurations , and telemetry setups (SiK, 433/915 MHz, etc.). Work on thermal management , vibration isolation , and EMI mitigation to ensure high sensor accuracy. Software & Autonomy Stack Develop and tune Visual Navigation Systems (VNS) including: Visual-Inertial Odometry (VIO) Visual SLAM (Kimera, ORB-SLAM3, VINS-Fusion) Depth-based obstacle avoidance Implement and optimize sensor fusion pipelines (IMU + Vision + GPS + LiDAR) using ROS/ROS2 . Work on autonomous path planning , mission control , and failsafe behaviors . Deploy real-time inference pipelines for onboard AI (object detection, terrain mapping, human/vehicle tracking). Integrate MAVLink protocols for communication between companion computers and flight controllers. Systems Engineering & Testing Calibrate and validate sensors (IMU, magnetometer, barometer, GPS, and camera extrinsics). Conduct ground and flight tests to evaluate stability, control loops, and navigation accuracy. Analyze flight logs using QGroundControl , Mission Planner , or MAVLink tools for debugging and optimization. Manage version-controlled firmware and ROS packages using Git and CI/CD pipelines. Edge & Cloud Deployment Implement real-time video streaming , telemetry dashboards , and remote monitoring over 4G/5G or Wi-Fi. Optimize low-latency data pipelines using CUDA , TensorRT , and GStreamer . Collaborate with AI/ML engineers for data collection, annotation, and retraining pipelines for perception and mapping models. Required Skills & Experience 1–2 years of hands-on experience in UAV system development (hardware + software). Proven integration experience with PX4 or ArduPilot flight stacks. Strong programming skills in C++ and Python for robotics and embedded control. Deep understanding of ROS/ROS2 , MAVROS , and sensor fusion algorithms . Hands-on experience with NVIDIA Jetson devices (Jetson Orin/Xavier/Nano). Experience with stereo/depth cameras such as Intel RealSense D455 , ZED , or Luxonis OAK-D . Knowledge of SLAM/VIO algorithms , sensor calibration , and 3D reconstruction . Familiarity with PCB design , power electronics , and embedded microcontrollers (STM32, ESP32). Strong background in control systems , flight dynamics , and PID tuning . DGCA-certified drone pilot license (preferred) — ideal for candidates capable of manual flight testing and maneuver validation. Preferred Skills Experience with Isaac ROS Visual SLAM , Kimera-VIO , or RTAB-Map . Exposure to LiDAR integration and point cloud processing (PCL, Open3D). Proficiency in Docker for containerized robotics deployments. Understanding of computer vision (OpenCV) and deep learning frameworks (PyTorch, TensorFlow). Knowledge of geospatial datasets , DGCA/IN-SPACe regulations , and BVLOS operational standards . Education Bachelor’s or Master’s in Robotics , Mechatronics , Aerospace Engineering or Embedded Systems . Publications, research projects, or open-source contributions in drone autonomy , SLAM , or VIO are highly desirable. What We Offer Work at the intersection of AI, photonics, and autonomous UAVs . End-to-end ownership in drone R&D , from prototype to field deployment. Access to Phoqtek Labs’ GPU and simulation infrastructure for testing and AI workloads. Opportunity to contribute to cutting-edge aerial autonomy research . Competitive startup compensation with growth.
About the Role We are seeking a highly skilled IT Solutions & GPU Infrastructure Lead to take complete ownership of our GPU-based server infrastructure. This role focuses on next-generation GPU systems used for AI/ML workloads, covering every aspect from data center colocation and setup to GPU slicing, MIG management, resource allocation, optimization, and compliance. You will lead the end-to-end lifecycle of GPU infrastructure — ensuring all servers are optimized, secure, and production-ready for both internal and customer use. Key Responsibilities Colocation & Infrastructure Setup GPU colocation and end-to-end infrastructure setup will be entirely under your ownership and responsibility. Coordinate with data centers for rack installation, power, and cooling. Deploy and configure GPU-based servers for production readiness. 2. GPU & AI/ML Infrastructure Manage GPU slicing and MIG (Multi-Instance GPU) for multi-tenant workloads. Install and maintain the NVIDIA software stack — CUDA, cuDNN, NCCL, and DCGM. Optimize GPU infrastructure for AI/ML workloads (TensorFlow, PyTorch, RAPIDS). Support multi-GPU scaling using NVLink and PCIe passthrough. 3. Systems & Virtualization Administer Linux-based environments (Ubuntu, CentOS, Rocky) along with other environments. Manage virtualization platforms such as VMware, KVM, or Proxmox with GPU passthrough. Handle container orchestration with Docker and Kubernetes GPU Operators. Integrate high-performance storage (NFS, Ceph, SAN/NAS) for large-scale datasets. 4. Monitoring & Performance Optimization Monitor GPU and system performance using Prometheus, Grafana, NVIDIA DCGM, and nvidia-smi. Proactively detect, analyze, and resolve GPU or system bottlenecks. Optimize GPU nodes for training and inference performance. Implement structured logging, alerts, and usage reporting. one should have to administer, manage, monitor and maintain GPU infrastructure for AI workloads. 5. Security & Compliance Harden GPU servers for multi-tenant workloads. Manage driver, firmware, and software license compliance. Ensure infrastructure security and audit readiness with periodic patching and updates. 6. Networking & High-Performance I/O Configure and maintain high-speed network fabrics (InfiniBand, RDMA, RoCE). Optimize low-latency interconnects for distributed GPU workloads. Troubleshoot and enhance data transfer performance. 7. Customer & Infrastructure Ownership Serve as the primary contact for GPU resource allocation. Provision GPU slices or MIG instances for internal and external teams. Troubleshoot, document, and optimize workload performance. Qualifications Proven experience in data center server setup and colocation. Deep expertise in GPU server administration (NVIDIA A100/H100 or equivalent). Strong working knowledge of GPU slicing, MIG, CUDA, NCCL, and NVIDIA drivers. Experience with Linux administration, virtualization (VMware/KVM/Proxmox), and containers (Docker/Kubernetes). Hands-on experience with AI/ML frameworks such as TensorFlow and PyTorch. Familiarity with monitoring tools (Prometheus, Grafana, DCGM). Knowledge of storage systems (NFS, Ceph) and high-performance networking. Strong vendor coordination and infrastructure management skills. Why This Role Matters This position owns the entire lifecycle of GPU-based infrastructure — from colocation to slicing, monitoring, and optimization. You will build and maintain the backbone of our AI/ML infrastructure, ensuring that all systems are efficient, scalable, and production-grade.