This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The Principal FPGA Engineer will participate in all aspects of the development and test of avionics FPGA RTL designs for Astrolab rovers. This position will report to the Director of Avionics Engineering and will be based in our Hawthorne, CA office. - Own FPGA RTL development from blank sheet to flight - Develop FPGA logical architecture and requirements in collaboration with other engineering disciplines including defining HW/SW interfaces between fabric logic and processing subsystems - Develop RTL code for Microsemi, Xilinx, or other FPGA or SoC devices in either SystemVerilog or VHDL - Perform design verification including simulations, test benches, timing analysis, and hardware testing - Lead design reviews, demonstrate requirement traceability, and create documentation for FPGA operation and interfacing with other SW and FPGA systems - Develop and participate in test campaigns ranging from unit level manual tests, unit automated tests, and system level tests Qualifications - Bachelor of Science degree in Computer Engineering, Electrical Engineering, or equivalent - 15+ years of experience in FPGA RTL architecture, development, verification, deployment, and maintenance - Skilled at SystemVerilog or VHDL - Skilled in at least 1 scripting language (TCL, shell, Python, etc) - Skilled in verification techniques, and timing/stability analysis - Proficiency in understanding PCB designs and reading schematics - Proficiency in understanding embedded software - Proficiency with AXI protocols (at least AXI4 family) - Knowledge of common bus and memory interfaces (DDR4, SPI, I2C, UART, etc) - Demonstrated success working in a dynamic environment Requirements - Design for space environments including radiation impacts and mitigations Benefits - Join a team of best-in-class engineers building the foundation of planetary surface exploration - Equity ownership in the company - Comprehensive health benefits, including medical, dental, vision, and mental health support - 401(k) plan with company match - Flexible PTO and parental leave - Home office set up reimbursement - Fully flexible and remote friendly work environment - Weekly lunch stipend, plus complimentary snacks and beverages on-site - Once a month social hour on-site with food and drinks

• Advance core computer vision model performance (object detection, segmentation, OCR) for warehouse inventory scanning across drone and MHE Vision platforms • Own the full ML lifecycle from research and experiment design through production deployment and monitoring — applying rigorous ablation studies and SOTA methodology • Collaborate with the ML infrastructure team on model optimization and deployment across cloud and edge inference targets (ONNX, TensorRT, quantization) • Work with Operations and Product to understand customer needs and translate them into ML improvements with measurable business impact • Provide technical leadership and mentorship to the ML team, raising standards for experiment design, model evaluation, and production readiness • Explore next-generation perception capabilities, including embedded and on-prem inference optimization for new deployment targets

• Develop learning-based manipulation models for end to end sensor-driven interaction (e.g., reaching, motion generation, and execution in dynamic environments). • Build and maintain manipulation training pipelines: dataset creation from robot logs/teleop, action representations, augmentation, and distributed training. • Design evaluation metrics and regression tests that quantify manipulation reliability, recovery behavior, and safety in real environments. • Develop sim-to-real workflows for manipulation learning, including simulation environments, domain randomization, and failure-mode testing. • Optimize and distill models for edge deployment; benchmark latency, memory use, and stability on target hardware. • Partner with the AI platform team to integrate policies with control and safety systems, and validate end-to-end performance on robots. • Analyze field performance, identify dominant failure modes, and drive iterative improvements through data collection and targeted retraining.

• Develop learning-based navigation models that predict safe, smooth trajectories from sensor inputs and/or perception representations. • Build imitation learning pipelines from fleet logs (trajectory extraction, filtering, scenario balancing, evaluation). • Implement simulation-based refinement (RL, reward shaping, domain randomization) to improve robustness. • Define navigation success metrics aligned to product outcomes. • Collaborate with the AI Platform team to integrate learned policies behavior/safety systems and validate on-robot. • Build regression tests and scenario replay suites for challenging scenarios. • Analyze field behavior, identify failure modes, and close the loop through data curation and retraining.