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: Select, fine-tune, and deploy foundation models (LLMs) to power agent reasoning, natural language understanding, and empathetic, context-aware communication with users.

Complex Interaction Modeling: Develop models for human-agent interaction (HAI), incorporating principles from cognitive science and behavioral economics to ensure agents are effective, trustworthy, and aligned with therapeutic protocols.

Simulated Environments: Construct robust simulation environments for pre-training and testing agent policies, ensuring system stability and safety across a wide range of psychological and behavioral scenarios.

Performance and Resilience: Optimize the deployment environment to manage the computational demands of multi-agent orchestration, ensuring low-latency decision-making and high system resilience.

Technical Strategy: Lead the evaluation and adoption of new agentic frameworks, reasoning technologies, and system design patterns that position Tee as a leader in autonomous behavioral health technology.

Agent Evaluation & Observability: Design and implement comprehensive evaluation pipelines for multi-agent orchestration—including visualisations, trace-level analysis of LLM calls and tool invocations, offline evaluation against golden datasets, real-time production monitoring for behavioral drift and outcome correlation, guardrails, and human-in-the-loop annotation workflows. Establish metrics frameworks to assess reasoning quality, task completion, safety compliance, and task alignment across the full agent lifecycle.

About You

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a highly quantitative field.

Minimum of 5+ years of experience in a production software engineering environment, with at least 3+ years specifically focused on designing, implementing, and deploying complex machine learning or autonomous systems.

Exceptional Python Programming Skills: Mastery of Python and its scientific libraries.

Production MLOps Expertise: Demonstrated experience building and managing CI/CD pipelines for ML models and integrating them into service-oriented architectures (SOA).

Autonomous/Agentic Systems: Strong theoretical understanding and practical application of techniques central to agentic AI, such as Reinforcement Learning (RL), planning algorithms, or complex decision-making systems.

LLM Deployment: Hands-on experience fine-tuning, deploying, and managing Large Language Models (LLMs) in a production environment, including knowledge of prompt engineering, retrieval-augmented generation (RAG), and cost optimization.

Software Engineering Rigor: Proven ability to write high-quality, maintainable, scalable, and well-tested production code.

Proficiency with cloud infrastructure (AWS, GCP) and containerization technologies (Docker, Kubernetes).

Benefits

Comprehensive Medical, Dental and Vision plans coverage since day one

Pre-tax benefits: HSA/ FSA