Interested in this AI/ML Engineer role at Talkiatry?
Apply Now →Skills & Technologies
About This Role
Location
------------
New York, NY
Employment Type
-------------------
Full time
Location Type
-----------------
Hybrid
Department
--------------
Corporate \& TechnologyCore Operations
Compensation
----------------
- $170K – $185K • Offers Bonus
We're building the next chapter of Talkiatry – one where AI reimagines how patients access care and how our teams support them through treatment with greater ease, speed, and personalization.
As Director of AI Strategy \& Operations, you'll own how that happens. You'll define where AI gets deployed across the patient journey – intake, scheduling, engagement, follow\-up – and you'll make sure it actually works: performing well, improving over time, and earning the trust of the teams that rely on it.
This role is about taking AI from concept to operating reality – building the roadmap, managing the vendors, running the performance systems, and closing the loop when something breaks. You'll sit at the intersection of Operations, Product, and Engineering, translating business needs into deployable solutions and holding the system accountable to outcomes.
If you've deployed AI into a live operations environment, know the difference between a pilot and an operating model, and can move between strategic roadmap and AI agent\-level QA without losing altitude – this is the role.
About Talkiatry
-------------------
Talkiatry is transforming mental health care by making high\-quality psychiatry more accessible, human, and sustainable for both patients and clinicians. Co\-founded by a patient and a triple\-board\-certified psychiatrist, we were built to solve one of healthcare’s most urgent challenges: millions of people struggle to access mental health care, while clinicians face growing administrative burden and burnout. Through our technology\-powered, in\-network platform, we’re changing that, creating a better experience for patients and empowering clinicians to focus on delivering exceptional care.
Today, Talkiatry is the largest dedicated psychiatry practice in the U.S., with 750\+ psychiatrists, 250\+ therapists, and more than two million patient visits delivered nationwide. At our scale, every innovation has real impact. Every workflow we improve, every technology solution we build, and every patient experience we redesign helps thousands of clinicians provide better care to hundreds of thousands of patients. We’re not just growing quickly — we’re redefining how behavioral healthcare is delivered in America.
Joining Talkiatry means joining a mission\-driven team that is solving complex problems at scale, driving meaningful change in healthcare, and building the future of mental health care with compassion, innovation, and purpose.
You Will:
-------------
Key Responsibilities:
AI Vision \& Strategy
- Define where AI creates durable impact across the patient and clinician journey, and build the multi\-year strategy to get there.
- Translate strategy into concrete roadmaps, success metrics, and business cases that align Product, Engineering, and Operations around shared priorities.
- Champion responsible AI governance standards, especially important in a healthcare context where trust is everything.
AI Product Management
- Own the AI product roadmap end\-to\-end, ensuring systems talk to each other across Genesys, ServiceNow, Snowflake, the native Talkiatry app, and LLM infrastructure.
- Turn patient needs and operational workflows into product requirements that balance automation, safety, and empathy.
- Partner with Engineering and Data on architecture decisions – you don't need to build it, but you need to understand how it connects.
- Stay close to emerging technologies and know when something is ready to move from interesting to investable.
AI Agent Operations \& Quality
- Own the performance of deployed AI agents – containment rate, response accuracy, resolution efficiency, patient and clinician satisfaction.
- When something breaks or drifts, find the root cause and fix it: prompt updates, workflow changes, model retraining, knowledge base gaps.
- Build feedback loops from frontline teams so issues surface before they compound.
- Maintain the knowledge infrastructure (taxonomy, content ingestion, source\-of\-truth integrations) and set governance processes that keep agents accurate and current.
Vendor Strategy \& Program Delivery
- Lead vendor strategy: find the right partners, negotiate the terms, and hold them accountable for outcomes, not just delivery.
- Oversee a portfolio of concurrent AI initiatives from scoping through UAT, launch, optimization, and scale.
- Build internal AI enablement so teams across the org know how to work alongside the tools you're deploying.
You must have:
------------------
- 10\+ years in strategy \& operations, product management, or AI/automation, with real experience deploying AI into live operational environments.
- A track record of defining and executing AI strategy.
- Strong grasp of conversational AI platforms, LLM infrastructure, and how enterprise systems connect (APIs, telephony, CRM, data platforms) – preferred but not required if the operational instincts are strong.
- Analytical enough to build performance frameworks and pressure\-test results. You don't need a perfect model to move, but you don't wing it.
- Strong project and vendor management – you can run a complex multi\-workstream program and keep external partners honest.
- A communicator who can hold a room with engineers and operators alike.
- Committed to ethical AI and data integrity – in healthcare, that's not a checkbox.
Why Talkiatry
-----------------
- Top\-notch team: we're a diverse, experienced group motivated to make a difference in mental health care
- Collaborative environment: be part of building something from the ground up at a fast\-paced startup
- Excellent benefits: medical, dental, vision, effective day 1 of employment, 401K with match, generous PTO plus paid holidays, paid parental leave, and more!
- Grow your career with us: hone your skills and build new ones with our Learning team as Talkiatry expands.
- It all comes back to care: we’re a mental health company, and we put our team’s well\-being first
*Talkiatry participates in E\-Verify and will provide the federal government with your Form I\-9 information to confirm that you are authorized to work in the U.S. only after a job offer is accepted and Form I\-9 is completed. For more information on E\-Verify, please visit the following:* *EVerify Participation* *\&* *IER Right to Work**.*
*At Talkiatry, we are an equal opportunity employer committed to a diverse, inclusive, and equitable workplace and candidate experience. We strive to create an environment where everyone has a sense of belonging and purpose, and where we learn from the unique experiences of those around us.*
*We encourage all qualified candidates to apply regardless of race, color, ancestry, religion, national origin, sexual orientation, age, citizenship, marital or family status, disability, gender, gender identity or expression, pregnancy or caregiver status, veteran status, or any other legally protected status.*
Salary Context
This $170K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Talkiatry, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. Disclosed range: $170K to $185K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Talkiatry AI Hiring
Talkiatry has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, US, Remote, US. Compensation range: $180K - $185K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.