Staff R&D AI Engineer

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.

Responsibilities

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

Requirements

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 TensorFlow 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

Nice to have

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; Experience with real-world system deployment and sim-to-real transfer techniques; Knowledge of 3D computer vision, spatial reasoning, or multi-modal perception systems; Experience with distributed training frameworks (DeepSpeed, FairScale, Horovod) and large-scale model training; Familiarity with edge AI deployment and model optimization techniques (quantization, pruning, distillation); Experience with embodied AI research or projects involving agent-environment interaction; Published research in top-tier AI/ML conferences (NeurIPS, ICML, ICLR, CoRL, etc.); Open-source contributions to major AI/ML frameworks or robotics projects; Startup experience with ability to rapidly prototype and iterate on AI solutions; Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies; Background in safety-critical AI systems or AI alignment research

Benefits

Competitive salary; Comprehensive health, dental, and vision benefits package; 401(k) match; Equity options; $200/month Health & Wellness stipend; Continuing Education support; $500/year Function Health subscription; Free parking for in-office employees; Flexible Time Off (FTO); Parental leave for eligible employees; Supplemental life insurance