A
Archer
Staff Engineer (AI Autonomy)
Skills
PyTorchMachine LearningAI
2 months ago
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Important information
Contract type:
Permanent
Salary:
$195,000 – $235,000 / year
Location:
San Jose, CA, USA
Work mode:
Onsite
Published on:
19 August 2026
What they need
- 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
- 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
- Hands-on experience building, fine-tuning, and/or integrating vision-language or VLA models
- Comfort with ambiguity. If you need a fully scoped program before you can start, this won’t be a fit
- Strong with PyTorch (or comparable frameworks); comfortable owning model-serving integration end to end
- 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)
- Real 0→1 experience — you’ve built the first version of something, not just iterated on a mature product
- Solid grasp of transformer architectures and multimodal foundation models, including the practical mechanics of fine-tuning a pre-trained checkpoint to a new domain
- 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
- Shipped evaluation tooling or benchmarks that enable data-driven decisions
- Edge deployment experience (NVIDIA Jetson-class or comparable) — measured and tuned inference latency and memory footprint
- Experience in or alongside a safety-critical or regulated engineering process (aerospace, automotive, medical devices), even outside an ML-specific role
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