R&D AI Engineer Austin, TX​/Remote Office

About Us

We are establishing the first distributed AI infrastructure dedicated to personalized AI. The evolving needs of a data‑driven society are demanding scalability and flexibility. We believe that the future of AI is distributed and enables real‑time data processing at the edge, closer to where data is generated. We are building a future where a company's data and IP remains private and it is possible to bring large models directly to consumer hardware without removing information from the model.

As a Staff R&D AI Engineer, you will lead the development of cutting‑edge AI systems that bridge computer vision, natural language understanding, and action learning. You'll architect and implement Vision‑Language‑Action (VLA) models, advance reinforcement learning applications, and push the boundaries of multimodal AI integration. This role combines deep expertise in both computer vision and large language models with hands‑on experience in reinforcement learning to create intelligent systems that can understand, reason about, and interact with complex environments.

You'll drive research initiatives, mentor technical teams, and translate breakthrough AI research into practical applications across diverse domains.

• Design and develop Vision‑Language‑Action (VLA) models that integrate visual perception, natural language understanding, and action prediction
• Architect and implement reinforcement learning systems for sequential decision‑making, including policy learning and skill acquisition
• Build and optimize computer vision pipelines for perception tasks, including object detection, segmentation, tracking, and scene understanding
• Develop and fine‑tune large language models for instruction following, reasoning, and task planning applications
• Implement RLHF (Reinforcement Learning from Human Feedback) systems to improve model alignment and safety
• Create multimodal training pipelines that leverage synthetic and real‑world data for robust model performance
• Research and prototype novel AI architectures that combine vision, language, and action learning
• Collaborate with engineering teams to integrate AI models into applications and validate performance across domains
• Optimize model inference performance for real‑time applications across edge and cloud deployments
• Lead technical initiatives, mentor junior AI engineers, and establish best practices for AI model development
• Stay current with latest research in VLA models, multimodal AI, and robotics to drive innovation roadmap
• Present findings at conferences and publish research to advance the field

• 7+ years of experience in AI/ML engineering with 4+ years focusing on deep learning and neural network development
• Strong understanding of reinforcement learning algorithms and their applications (PPO, SAC, TD3, etc.)
• Strong expertise in both computer vision and natural language processing with hands‑on model development experience
• Proficiency in PyTorch and/or Tensor Flow with experience training and deploying large‑scale models
• Experience with transformer architectures, attention mechanisms, and large language model fine‑tuning
• Hands‑on experience with computer vision tasks including object detection, semantic segmentation, and visual tracking
• Strong programming skills in Python with experience in distributed training and model optimization
• Understanding of sequential decision‑making and control systems fundamentals
• Experience with MLOps practices including model versioning, monitoring, and deployment pipelines
• Proven ability to work independently on complex research problems and deliver practical solutions
• Strong communication skills and experience collaborating with cross‑functional engineering teams

• PhD in Computer Science, Robotics, AI/ML, or related field with focus on multimodal learning or robotics
• Direct experience developing or working with Vision‑Language‑Action (VLA) models or similar multimodal architectures
• Experience with RLHF implementation and human feedback integration for model alignment
• Background in imitation learning, inverse reinforcement learning, or learning from demonstrations
• Ex…