Senior AI Engineer

At Talkspace, we are committed to fostering a diverse, equitable, inclusive, and belonging-centered workplace where everyone can thrive while making a difference in mental health. Want to help over two million people receive quality mental healthcare? Come join our mission of getting therapy in the hands of everyone! We are looking for an experienced Senior AI Engineer to join our team. The Senior AI Engineer will be a pivotal technical leader, responsible for designing, building, and scaling the autonomous AI agents that form the core of our behavioral health platform. This role requires deep expertise in developing complex, multi-agent systems, leveraging Large Language Models (LLMs) for reasoning, planning, and goal setting, and applying Reinforcement Learning (RL) techniques to model and influence human behavior safely and ethically. You will drive the entire lifecycle of our agents—from developing cognitive architectures and interaction models to ensuring their robust, high-availability deployment and continuous learning in a production environment. Given the sensitivity of behavioral health, this role demands an exceptional focus on safety, ethical autonomy, transparency, and data privacy. The ideal candidate is a seasoned engineer who can bridge the gap between theoretical AI (specifically RL and planning) and real-world, scalable, and impactful user interactions. To work at Talkspace, you need to be as passionate as we are about our work, and excited to partner with us on delivering quality mental healthcare. Talkspace HQ is in NYC; this position is based in Eastern Standard Time. What You’ll Do Autonomous System Architecture: Design and implement the technical architecture for Tee's core AI agents, including the development of planning modules, memory/retrieval systems, goal-setting algorithms, and tool-use orchestration. Reinforcement Learning (RL) for Behavior: Apply advanced RL, Inverse RL, or related control theory methods to develop agents capable of adaptive, long-term intervention strategies that maximize positive user outcomes while minimizing risk (e.g., optimizing interaction sequencing, timing, and content). LLM Integration and Fine-tuning: S elect, fine-tune, and deploy foundation models (LLMs) to power agent reasoning, natural language understanding, and empathetic, context-aware communication with users.