Machine Learning Lead

You’ll work directly with our CTO to build AI systems that scale from pilot deployments to thousands of coordinated deliveries per day, establishing the intelligence layer that makes autonomous logistics commercially viable.

Location :
Remote US (Bay Area, Austin preferred)

Autolane is on a mission to revolutionize last-mile logistics by empowering autonomous vehicle owners to unlock the value of their vehicle. Our flagship product is the industry's first orchestration layer for autonomous deliveries—coordinating heterogeneous autonomous systems (AVs, humanoid robots, delivery bots) to achieve zero-wait handoffs and maximum fleet utilization. We integrate directly with retailers, commercial real-estate operators, and AV fleets, building the AI infrastructure that enables autonomy at scale.

As Machine Learning Lead at Autolane, you’ll architect and build the AI brain that orchestrates autonomous last-mile logistics. You’ll design and deploy the core learning systems—Graph Neural Networks for spatial reasoning, Transformers for temporal prediction, and Multi-Agent Reinforcement Learning for heterogeneous agent coordination—that enable our platform to optimize deliveries across AVs, humanoid robots, and delivery bots in real-time.

You’ll work directly with our CTO to build AI systems that scale from pilot deployments to thousands of coordinated deliveries per day, establishing the intelligence layer that makes autonomous logistics commercially viable.

• Graph Neural Networks: Design and implement 6-layer Graph Attention Networks for modeling spatial relationships between agents, locations, and resources using PyTorch Geometric
• Temporal Prediction: Build Transformer-based architectures for multi-horizon arrival time prediction, task duration forecasting, and optimal scheduling sequences
• Multi-Agent RL: Architect QMIX-based coordination systems with Conservative Q‑Learning for safe exploration across heterogeneous agent types (Teslas, Unitree G1 humanoids, PUDU bots)
• Ensemble Systems: Design robust decision-making through model diversity, weighted voting mechanisms, and uncertainty quantification with confidence‑based fallbacks
• Real‑time Inference: Optimize models for