AI Engineer | Autonomous Systems & Foundation Models

Compensation: Up to $400,000 Base Salary + Bonus + Meaningful Equity We're partnering with a well-funded startup building next-generation AI systems that enable intelligent machines to reason, plan, and operate autonomously in complex real-world environments. This is an opportunity to join a small, high-caliber engineering team working at the intersection of foundation models, agentic AI, robotics, reinforcement learning, and large-scale machine learning systems. You'll help develop the core intelligence layer that powers autonomous systems, contributing across model training, evaluation, simulation, reasoning architectures, memory systems, multi-agent coordination, and deployment. What You'll Work On Design, train, and evaluate multimodal AI models for autonomous decision-making Develop agent architectures capable of planning, memory, tool use, and long‑horizon reasoning Build large‑scale training and evaluation pipelines using synthetic and real‑world data Develop reinforcement learning environments and simulation frameworks for rapid experimentation Create benchmarking and evaluation systems that drive model improvement Improve model performance through data curation, training strategy, and rigorous experimentation Collaborate closely with engineers and researchers to deploy AI systems into real‑world environments Take ownership of projects from research through production deployment What We're Looking For 2+ years of experience building and deploying machine learning systems Strong Python and PyTorch experience Experience training, fine‑tuning, or evaluating modern deep learning models Familiarity with foundation models, multimodal AI, agentic systems, or reinforcement learning Experience working with distributed training or large‑scale ML infrastructure Strong experimental mindset with a focus on evaluation and measurable performance improvements Ability to thrive in fast‑paced startup environments Nice to Have Experience with RLHF, DPO, reinforcement learning, or model alignment techniques Simulation, synthetic data generation, or digital twin environments Robotics, autonomous systems, computer vision, or physical AI experience #J-18808-Ljbffr Harrison Clarke