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About This Role
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
- 401k Retirement Savings Program with matching up to 4%
- Voluntary benefits including disability, basic life or pet insurance, etc.
- Monthly Wellness Stipend to promote mental and physical self\-care
- Flexible PTO and Remote First Environment
- Regular team events, including Wellness Workshops and Team Building Events
- Free access to Talkspace products for you and one household member, as well as access to a friends and family discount!
Compensation
At Talkspace, we believe that pay transparency during the interview process is a critical part of diversity, equity, and inclusion. Our salary bands are based on internal and external compensation benchmarks, which we regularly evaluate to ensure we pay competitively.
The base salary range for this role is between $169,000 and $200,000\. Within the salary bands, leveling corresponds to each candidate's relevant experience, skills as assessed during the interview process, education, and applicable certifications.
Why Talkspace?
Talkspace is the world's leading online therapy company, serving over 2 million users looking to begin their wellness journey through tele\-health. According to the World Health Organization, close to 1 billion people worldwide live with a mental disorder, and on average more than 75% with mental, neurological, and substance use disorders receive no treatment for their condition at all. Additionally, one\-third of the world's population – 2 billion people – live in countries that spend less than 1% of their health budgets on mental health. Therapy is an universal need and it's our mission here to change the world by cultivating an intentional space for people to feel supported through quality care that is simple and accessible.
Combining our passion for innovation along with our desire to help others overcome the stigma behind "getting help," we are transforming the way patients find the right care provider, making an otherwise impossible feat easily conquerable. Our network of licensed, accredited, and board\-certified clinicians are increasing access to mental health for our members through a myriad of high quality therapy services: anytime and for a fraction of the price. Dedicated to our mission, we are looking for candidates that want to bring their talents into a diverse "for purpose" space. If you're equally as passionate about making quality mental healthcare accessible to all then Talkspace is the right place for you!
### EQUAL OPPORTUNITY EMPLOYER
Talkspace welcomes and celebrates talent from all backgrounds, perspectives, and walks of life to foster an innovative and diverse workforce. We encourage you to apply, even if you don't meet every qualification or if your path has been nontraditional — such as not completing a formal degree program, taking a career break, or having a prior criminal record — if you believe you could make a great addition to this team. Come as you are and learn about the exciting opportunities on our team.
Individuals seeking employment at Talkspace are considered without regard to race, color, religious creed, sex, national origin, citizenship status, age, physical or mental disability, sexual orientation, marital, parental, veteran or military status, unfavorable military discharge, or any other status protected by applicable federal, state or local law.
How do we define Diversity, Equity, Inclusion, and Belonging at Talkspace?
Diversity
Diversity encompasses the unique attributes of our employees as individuals. We value and embrace the richness arising from their varied backgrounds, perspectives, and experiences, which include, but are not limited to, age, ability, ethnicity, gender, race, and cultural background.
Equity
Equity refers to a fair and impartial workplace, aiming to ensure equal growth and advancement opportunities for all employees. This involves amplifying underrepresented voices, addressing unconscious biases, and providing inclusive, culturally competent mental health care.
Inclusion
Inclusion signifies the practice of granting equal access to opportunities and resources for all employees, particularly those who might otherwise be excluded or marginalized. It ensures that everyone feels a sense of belonging, value, support, and respect as an individual.
Belonging
Belonging reflects the affinity and positive relationships that develop among employees from diverse backgrounds when businesses actively promote diversity, equity, and inclusion in the workplace.
Salary Context
This $169K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Talkspace, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($184K) sits 14% below the category median. Disclosed range: $169K to $200K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Talkspace AI Hiring
Talkspace has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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