We are seeking a Robotics Perception Engineer to build and integrate real-time vision systems and perception infrastructure for unmanned aerial platforms. You will develop camera pipelines, implement sensor fusion algorithms, and create robust testing frameworks to ensure reliable perception performance across diverse operating conditions. 

Responsibilities: 

Vision System Integration 

• Develop and maintain camera drivers and sensor interfaces for multi-camera systems on embedded platforms
• Integrate off-the-shelf and custom perception algorithms into production autonomy stacks
• Build efficient image processing pipelines using GStreamer, V4L2, and Jetson multimedia APIs
• Implement camera calibration workflows including intrinsic, extrinsic, and temporal synchronization
• Design data logging and telemetry systems for perception subsystem monitoring and debugging 

Perception Infrastructure & ROS2 Development 

• Build ROS2 nodes for camera capture, sensor fusion, and perception output distribution 

• Develop standardized message interfaces and APIs between perception, planning, and control systems 

• Create visualization and diagnostic tools for real-time perception system health monitoring 

• Implement efficient data serialization and transport for high-bandwidth sensor streams 

• Maintain perception software repositories with proper documentation and version control 

Algorithm Implementation & Tuning 

• Integrate visual-inertial odometry (VIO) and SLAM libraries for state estimation 

• Tune EKF/UKF filters for multi-sensor fusion from IMU, cameras, barometers, and rangefinders 

• Implement outlier rejection and sensor health monitoring for robust operation 

• Optimize algorithm parameters based on flight test data and performance metrics 

Testing & Validation 

• Design comprehensive test scenarios in simulation environments 

• Develop automated regression test suites for continuous integration of perception updates 

• Analyze flight logs and sensor data to identify failure modes and performance degradation 

• Create hardware-in-the-loop test setups for pre-flight validation 
• Support flight test operations and conduct post-flight performance analysis 

Qualifications:

• 2-4+ years of experience in robotics perception, computer vision, or sensor integration 
• Bachelor's in Robotics, Aerospace Engineering, Computer Science, Electrical Engineering, or related field – equivalent industry experience also welcome
• Proficient in C++14/17 with understanding of modern software development practices 

• Strong hands-on experience with ROS2 architecture, packages, and development workflows 

• Solid understanding of camera systems, image sensors, and computer vision fundamentals 

• Experience with sensor calibration procedures and multi-sensor time synchronization 

• Familiarity with Linux development environments and embedded systems 

• Strong debugging skills and experience with system-level integration 

Preferred: 

• Experience with camera drivers, GStreamer, V4L2, or similar multimedia frameworks 

• Hands-on experience with NVIDIA Jetson platforms (Orin, Xavier, AGX) 

• Prior work with small UAVs, drones, or mobile robotics platforms 

• Familiarity with PX4 or ArduPilot autopilot systems and MAVLink protocol 

• Experience with state estimation techniques (EKF, UKF, particle filters) 

• Knowledge of VIO/SLAM frameworks (OpenVINS, VINS-Fusion, ORB-SLAM) 

• Python skills for data analysis, visualization, and tooling development 

• Understanding of real-time systems and computational performance optimization 

• Experience with CI/CD pipelines for robotics software 

• • Background in sensor fusion, optical flow, or feature-based tracking 

#LI-BC1

We are seeking an Embedded Software Engineer to develop and maintain custom PX4 autopilot firmware for autonomous aerial platforms. You will modify core flight control algorithms, integrate custom sensors and payloads, build simulation environments for hardware-in-the-loop testing, and ensure reliable operation in challenging mission profiles.

Responsibilities:

PX4 Firmware Development & Customization

• Develop, modify, and maintain custom PX4 firmware modules for mission-specific requirements

• Implement custom flight modes, navigation algorithms, and control strategies

• Integrate sensors, actuators, and payloads through PX4 driver and uORB message framework

• Debug firmware issues using GDB, serial consoles, and flight log analysis

• Patch and backport fixes across PX4 versions while maintaining stability

Build Systems & Toolchains

• Maintain build infrastructure using CMake, Make, and embedded toolchains

• Configure cross-compilation environments for ARM Cortex-M target architectures

• Manage firmware dependencies, submodules, and third-party libraries

• Create reproducible build configurations and automate firmware generation pipelines

Simulation & Hardware-in-the-Loop Testing

• Develop and maintain SITL (Software-in-the-Loop) and HITL (Hardware-in-the-Loop) test environments

• Integrate PX4 with physics-based simulation platforms for validation and regression testing

• Create automated test scenarios to validate firmware changes before flight testing

• Configure MAVLink communication interfaces for ground control and companion computer integration

System Integration & Testing

• Integrate PX4 with companion computers running ROS2-based autonomy stacks

• Develop MAVLink and UART communication protocols between flight controller and edge compute modules

• Support flight test operations including pre-flight checks, log collection, and post-flight analysis

• Collaborate with hardware, autonomy, and systems engineers to diagnose and resolve integration issues

Qualifications:

• Bachelor's in Computer Science, Computer Engineering, Electrical Engineering, Aerospace Engineering, or related field – equivalent industry experience also welcome

• 4-6+ years of experience in embedded software development with focus on real-time systems

• Strong proficiency in C and C++ for embedded systems programming

• Hands-on experience with PX4 autopilot firmware architecture and development

• Deep understanding of build systems including CMake, Make, and embedded toolchains

• Experience with ARM Cortex-M microcontrollers and embedded debugging tools

• Solid grasp of control theory, state estimation, and sensor fusion fundamentals

• Understanding of MAVLink protocol and communication with ground control stations

• Experience developing and maintaining simulation environments for embedded systems testing

Preferred:

• Experience with multi-rotor or fixed-wing UAV flight dynamics and control

• Familiarity with ROS2 and companion computer integration architectures

• Knowledge of Kalman filtering (EKF/UKF) and inertial navigation systems

• Understanding of safety-critical software development practices

• Experience with UART, SPI, I2C, and CAN bus protocols

• Background in flight testing and airworthiness procedures

• Knowledge of RTOS concepts and real-time scheduling

• Experience with version control workflows for firmware development

• Familiarity with Python for scripting, automation, and data analysis

• Understanding of power systems, motor control, and electronic speed controllers

• Experience with continuous integration systems for embedded firmware

We are seeking a Platform Engineer to architect and operate scalable compute infrastructure that powers our autonomous vehicle simulation and testing framework. You will build elastic compute systems across AWS and on-premises clusters, enabling engineering teams to rapidly iterate on autonomy algorithms through massive parallel simulation workloads.

Responsibilities:

Elastic Compute Architecture

• Design and implement auto-scaling compute infrastructure for simulation workloads using cloud platforms

• Build and maintain on-premises GPU and CPU clusters for simulation and machine learning training

• Architect hybrid cloud solutions that optimize cost and performance across cloud and local compute resources

• Implement job scheduling and orchestration systems using Kubernetes for thousands of concurrent simulations

• Design storage solutions for large-scale simulation data, logs, and artifacts using cloud and local storage systems

Simulation Platform Development

• Deploy and maintain robotics simulation environments at scale

• Build CI/CD pipelines for automated simulation testing of autonomy software

• Create infrastructure for distributed parameter sweeps, Monte Carlo testing, and regression suites

• Develop monitoring and observability systems for simulation fleet health and resource utilization

• Implement data pipelines for simulation results ingestion, analysis, and visualization

Infrastructure as Code & Automation

• Write and maintain infrastructure as code for reproducible infrastructure deployment

• Build automation tools and CLI utilities to simplify developer access to compute resources

• Implement GitOps workflows for infrastructure changes and configuration management

• Create self-service interfaces for engineers to launch and manage simulation jobs

• Develop cost monitoring and optimization strategies for cloud and on-prem resources

System Operations & Reliability

• Monitor and optimize infrastructure performance, reliability, and cost efficiency

• Troubleshoot complex distributed systems issues across networking, storage, and compute layers

• Implement backup, disaster recovery, and business continuity strategies

• Maintain security best practices including IAM, secrets management, and network isolation

• Collaborate with autonomy, ML, and robotics teams to understand compute requirements and optimize workflows

Network Design & Infrastructure

• Design and implement network architectures for distributed simulation workloads across AWS and on-premises environments

• Configure VPCs, subnets, security groups, and routing for secure, high-performance compute clusters

• Establish hybrid cloud connectivity (VPN, Direct Connect, site-to-site tunnels) between on-premises and cloud resources

• Optimize network performance for large data transfers, multi-node communication, and distributed workloads

• Support internal infrastructure network design and provide technical guidance to engineering programs

• Troubleshoot network issues including latency, packet loss, and connectivity problems across distributed systems

Qualifications:

• Bachelor's in Computer Science, Software Engineering, or related technical field – equivalent industry experience also welcome

• 2-5+ years of experience in platform engineering, DevOps, SRE, or cloud infrastructure roles

• Strong hands-on experience with Kubernetes for container orchestration and workload management

• Experience with cloud computing platforms and services (compute, storage, networking)

• Deep understanding of Linux system administration and troubleshooting

• Strong networking fundamentals including TCP/IP, routing, DNS, VPNs, and security

• Understanding of infrastructure as code principles and configuration management

• Proficiency in scripting and automation (Python, Bash, or similar)

• Experience building and maintaining CI/CD pipelines

• Solid grasp of distributed systems concepts, job scheduling, and resource management

• Ability to design infrastructure from first principles and make architectural decisions

Preferred:

• Experience building infrastructure for simulation, robotics, or autonomous systems workloads

• Understanding of GPU computing and accelerated workload management

• Knowledge of job scheduling systems for batch and parallel workloads

• Experience managing on-premises clusters and hybrid cloud architectures

• Familiarity with robotics middleware (ROS/ROS2) or simulation platforms

• Understanding of cost optimization for compute-intensive workloads

• Experience with monitoring, logging, and observability systems

• Knowledge of containerization technologies and image management

• Background in data engineering, MLOps, or machine learning infrastructure

• Experience with network performance analysis and troubleshooting

• Understanding of software-defined networking and network automation

• Familiarity with security compliance requirements in aerospace/defense environments

Compensation:

Level II - $113,000 - $141,000

Level III - $142,000 - $172,000

We are seeking a Machine Learning Engineer to build production-grade computer vision pipelines and manage end-to-end ML data infrastructure for autonomous aerial systems. You will own the full lifecycle from data curation through model deployment on edge hardware. 

Responsibilities: 

ML Model Development & Deployment 

• Design, train, and optimize object detection and tracking models (YOLO, RT-DETR, ViT) for aerial platforms 

• Deploy models on NVIDIA Jetson hardware using TensorRT, achieving real-time inference 

• Integrate vision models into ROS2 autonomy stacks with efficient message passing and synchronized sensor data 

• Profile and optimize model performance to meet compute, power, and latency constraints on embedded systems 

Data Infrastructure & MLOps 

• Build and maintain data pipelines for ingestion, labeling, versioning, and quality control of large-scale aerial imagery datasets 

• Design active learning loops and data selection strategies to maximize model performance with minimal labeling effort 

• Implement automated model evaluation frameworks and performance monitoring for deployed systems 

• Create synthetic data generation pipelines and data augmentation strategies to improve model robustness 

System Integration 

• Develop ROS2 nodes for real-time sensor processing, model inference, and downstream autonomy integration 

• Work with GStreamer and Jetson multimedia APIs for efficient camera pipeline management 

• Collaborate with perception and autonomy teams to ensure vision outputs meet system requirements 

Qualifications: 

• Bachelor's in Computer Science, Electrical/Computer Engineering, Robotics, or related field (Master's preferred) – equivalent industry experience also welcome 

• 4-6+ years of experience in machine learning with focus on computer vision applications 

• Strong proficiency in PyTorch and modern deep learning frameworks 

• Hands-on experience deploying models on NVIDIA Jetson platforms (Orin, Xavier, or similar) 

• Solid understanding of model optimization techniques: quantization, pruning, TensorRT, ONNX 

• Experience with ROS2 for robotics applications, including sensor integration and message handling 

• Proven track record building and managing ML data pipelines and infrastructure 

• Strong Python skills with experience in data processing libraries (NumPy, Pandas, OpenCV) 

• Familiarity with MLOps tools and practices (versioning, experiment tracking, CI/CD for models) 

Preferred: 

• Experience with C++ for performance-critical vision processing 

• Knowledge of CUDA programming or GPU optimization 

• Familiarity with data labeling tools and annotation management systems 

• Experience with synthetic data generation or domain adaptation techniques 

• Understanding of aerial imagery characteristics and challenges 

• Background in object tracking algorithms (Kalman filters, Hungarian assignment, multi-object tracking) 

• Experience with GStreamer, Jetson BSP, or embedded Linux development 

• Knowledge of model monitoring and performance debugging in production systems 

• Familiarity with DVC, MLflow, Weights & Biases, or similar MLOps platforms 

#LI-BC1