Staff Engineer, AI Autonomy

Archer is an aerospace company based in San Jose, California building an all-electric vertical takeoff and landing aircraft with a mission to advance the benefits of sustainable air mobility. We are designing, manufacturing, and operating an all-electric aircraft that can carry four passengers while producing minimal noise. As the Staff Engineer, AI Autonomy, you'll be at the forefront of bringing intelligent, learned behavior to real aircraft — building and deploying the vision-language and VLA models that let Archer's eVTOL aircraft, including Midnight, perceive, reason, and navigate in the real world.

Responsibilities

Develop and integrate multi-model systems; VLN/VLA, perception, and language models; Design benchmarks, measure success and failure modes against internal baselines and published results, and report comparisons transparently; Fine-tune and adapt foundation models, weighing tradeoffs clearly at each step; Validate through data-driven testing in simulation, on hardware, and in flight test, partnering closely with the Safety and Simulation Engineers who check your model's output; Take models from prototype to deployed, monitored components running on edge hardware under real latency and power constraints.

Requirements

8+ years of experience related to position minimum; M.S., PhD, or equivalent experience in Computer Science, Robotics, or a related field, with strong applied machine learning depth (exceptional BS candidates also considered); Hands-on experience building, fine-tuning, and/or integrating vision-language or VLA models; Solid grasp of transformer architectures and multimodal foundation models, including the practical mechanics of fine-tuning a pre-trained checkpoint to a new domain; Strong with PyTorch (or comparable frameworks); comfortable owning model-serving integration end to end; Real 0→1 experience — you've built the first version of something, not just iterated on a mature product; Comfort with ambiguity.

Nice to have

VLN or embodied-agent navigation experience (aerial, ground, or indoor robotics); Experience with reinforcement learning, imitation learning, or self-supervised training for navigation or control; Edge deployment experience (NVIDIA Jetson-class or comparable) — measured and tuned inference latency and memory footprint; Shipped evaluation tooling or benchmarks that enable data-driven decisions; Experience in or alongside a safety-critical or regulated engineering process (aerospace, automotive, medical devices), even outside an ML-specific role.