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Archer Technologies is an aerospace company building all-electric vertical takeoff and landing (eVTOL) aircraft designed to carry four passengers with minimal noise, advancing sustainable air mobility.
As a Staff AI Research Engineer, you will lead the development of cutting-edge machine learning and deep learning solutions that power the company's autonomous systems. Your responsibilities include:
• Design, implement, and evaluate novel machine learning and deep learning algorithms from first principles
• Own the full ML development lifecycle: data requirements, architecture design, input/output representations, evaluation metrics, and production deployment
• Collaborate with research engineers to prototype and validate complex solutions from academic literature
• Conduct rigorous experiments to benchmark new techniques and characterize model behavior under real-world conditions
• Develop scalable, reproducible research and development tools and frameworks
• Communicate research findings and technical insights to leadership and cross-functional teams
• Stay current with the latest AI/ML research and identify innovations applicable to aerospace challenges
• Transition research prototypes into production-ready systems for aircraft autonomy
You will work on problems at the intersection of AI and aerospace engineering, including autonomous flight control, perception systems, and decision-making algorithms for electric aircraft.
Required qualifications: M.S. or PhD in Computer Science or Computer Engineering with strong AI/ML focus. Expert-level proficiency with PyTorch or TensorFlow. Deep knowledge of Transformer architectures, attention mechanisms, multi-modal foundation models, diffusion policies, fine-tuning, model distillation, and mixture-of-experts approaches. Strong debugging and requirements analysis skills for AI models. Current with research literature and modern ML methodologies.
Bonus qualifications: Experience with reinforcement learning, self-supervised learning, imitation learning, Vision-Language-Action (VLA) models for robotics, production AI systems, or publications at top-tier conferences.
