Staff Engineer, AI Autonomy
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About the Role
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.
Our sights are set high and our problems are hard, and we believe that diversity in the workplace is what makes us smarter, drives better insights, and will ultimately lift us all to success. We are dedicated to cultivating an equitable and inclusive environment that embraces our differences, and supports and celebrates all of our team members.
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.
What You’ll Do:
- 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.
- 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. If you need a fully scoped program before you can start, this won't be a fit.
Bonus Qualifications:
- 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.
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