Senior AI Autonomy Engineer — Edge & Vision-Language

AlleyCorp

San Jose (CA)

On-site

USD 195,000 - 235,000

Full time

14 days+

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Job summary

Archer, an aerospace company based in San Jose, CA, seeks a Staff Engineer, AI Autonomy to advance intelligent, learned behavior for eVTOL systems. You will develop multi-model systems, fine-tune foundation models, and validate performance across simulation, hardware, and flight tests.

You will lead edge deployments, work with Safety and Simulation teams, and push models from prototype to deployed components under real latency and power limits.

Qualifications

  • 8+ years of relevant experience.
  • MS/PhD or equivalent in CS/Robotics; exceptional BS candidates considered.
  • Hands-on experience with vision-language/VLA models.
  • Strong grasp of transformer-based multimodal models and fine-tuning.
  • Experience with model-serving integration end-to-end.
  • Real 0→1 project experience and comfort with ambiguity.

Responsibilities

  • Develop and integrate multi-model systems (VLN/VLA, perception, language).
  • Design benchmarks, report comparisons to baselines and results.
  • Fine-tune foundation models; weigh tradeoffs clearly.
  • Validate with data-driven tests in simulation, hardware, and flight.
  • Deploy and monitor models on edge hardware under real constraints.

Skills

Vision-language models
Multimodal transformer architectures
Model fine-tuning and integration
Edge deployment / latency constraints
Ambiguity handling / startup mindset
RL/Imitation/Self-supervised learning

Education

MS/PhD in CS/Robotics or related

Tools

PyTorch

Job description

Archer, an aerospace company based in San Jose, CA, seeks a Staff Engineer, AI Autonomy to advance intelligent, learned behavior for eVTOL systems. You will develop multi-model systems, fine-tune foundation models, and validate performance across simulation, hardware, and flight tests.

You will lead edge deployments, work with Safety and Simulation teams, and push models from prototype to deployed components under real latency and power limits.

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