Interested in this AI/ML Engineer role at Spring Health?
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Our mission: eliminating every barrier to mental health.
Spring Health is a global mental health company on a mission to eliminate every barrier to mental health. We're building a world where getting support is simple, personal, and built around the person, so care can continue through every job, move, health plan, and life stage.
Our AI-native platform helps us deliver personalized support across self-guided tools, coaching, therapy, medication management, and specialty care. With outcomes independently validated by JAMA Network Open and the Validation Institute, Spring Health reaches more than 170 million people worldwide through leading employers, health plans, and partners.
As an AI-native company, we believe technology should expand the reach, quality, and humanity of care. Every Spring Health team member is expected to use AI tools thoughtfully, apply human judgment to AI outputs, and keep building AI fluency in ways that support their role and our mission.
Reporting to the Director of AI Operations, the Senior AI Care Operations Manager will own the hands-on development, performance, and continuous improvement of AI-powered member and provider support experiences. Working within the strategy and priorities established by AI Operations leadership, this person will build effective support workflows, investigate complex issues, and translate conversations into validated insights, AI improvements, and well-supported recommendations for partner teams.
This is a full-time, individual-contributor position with no direct reports. This is a hybrid role based in New York City, with an expectation to be in the office two to three days per week. Our office is located at 60 Madison Avenue. Occasional travel may be required for company or team events.
What you’ll do:
- Build, test, launch, and continuously improve AI-powered support workflows that resolve member and provider needs accurately, safely, and efficiently.
- Translate support policies, processes, and real-world scenarios into clear decision logic and reliable automated experiences.
- Monitor AI performance and investigate incorrect answers, failed resolutions, routing problems, unnecessary escalations, and information gaps.
- Own the investigation of suspected AI issues submitted by Care Support and cross-functional partners, from problem reproduction and root-cause analysis through resolution and follow-up.
- Distinguish between AI, knowledge, process, product, and platform issues; implement fixes within AI Operations and route other issues to the appropriate owner with a clear problem definition.
- Analyze support conversations to identify recurring member and provider friction, emerging trends, knowledge gaps, and opportunities to reduce avoidable support demand.
- Validate conversation signals using available evidence and translate meaningful patterns into AI improvements or well-supported recommendations for partner teams.
- Collaborate with Care Support, Product, Engineering, Enablement and Training, and analytics partners to troubleshoot issues, share evidence, and support solutions within established priorities.
- Monitor the results of AI Operations changes and provide clear performance insights, risks, and recommendations to AI Operations leadership.
- Maintain documentation, testing standards, and monitoring practices that enable AI Operations to deliver reliable changes at speed.
What success looks like in this role:
- AI-powered support workflows consistently meet agreed targets for resolution quality, routing accuracy, member experience, and operational performance.
- New and updated workflows are launched with effective testing and monitoring, resulting in fewer defects, regressions, and repeat issues.
- High-priority AI-related issues are diagnosed, communicated, and resolved within agreed service expectations, with recurring root causes addressed.
- Meaningful patterns from member and provider conversations are consistently converted into validated AI improvements or actionable recommendations for the appropriate partner teams.
- Improvements owned by AI Operations produce measurable gains in outcomes such as successful resolution, escalation rates, repeat contacts, or member effort.
- AI Operations leadership and cross-functional partners receive clear, evidence-based analysis that supports timely prioritization and decision-making.
What you’ll bring:
- 4-6 years in an operations function — service or care operations, product/program operations, or a comparable role where you owned how work actually got done, not just how it was planned.
- 2+ years at a high-growth or scale-stage company, in a role that was both strategic and hands-on: you set direction and then built the thing yourself.
- Demonstrated hands-on experience building, configuring, testing, or improving automated or AI-powered workflows, decision trees, or similarly complex operational systems.
- Deep understanding of operational workflows and workflow development — how they're designed, where they break, what makes them scale, and how to translate messy policy and edge cases into clear, reliable decision logic.
- Strong analytical capabilities and a data-oriented instinct. You're comfortable getting into the data yourself, and you can move from a pattern in the numbers to a specific, sequenced plan of action.
- A track record of partnering directly with subject-matter experts to learn their workflows in detail, then working alongside them to automate, streamline, or apply AI — earning their trust rather than handing down requirements.
- Rigorous root-cause problem-solving. You can take an ambiguous symptom, reproduce it, isolate whether it's an AI, knowledge, process, product, or platform issue, and drive it to resolution.
- Clear written and verbal communication, with the ability to explain complex issues and influence partners without formal authority.
Nice to have:
- Background in Strategy & Operations, Business Operations, or management consulting.
- Experience in Care Support, customer support, or another high-volume support environment.
- Experience building or managing conversational AI or AI-powered support experiences.
- Experience in healthcare, benefits, mental health, or another regulated, high-trust environment.
- Familiarity with prompts, APIs, system integrations, logs, evaluation tools, experimentation, or SQL and basic data querying.
Benefits provided by Spring Health:
Note: We have even more benefits than listed here and below, your recruiter will provide more in-depth information as you continue in the interview process. Benefits are subject to individual plan requirements and eligibility criteria.
- Health, Dental, Vision benefits start on your first day at Spring. You and your dependents also receive access to One Medicalaccounts HSA and FSA plans are also available, with Spring contributing up to $1K for HSAs, depending on your plan type.
- Employer sponsored 401(k) match of up to 2% for retirement planning
- A yearly allotment of no cost visits to the Spring Health network of therapists, coaches, and medication management providers for you and your dependents.
- We offer competitive paid time off policies including vacation, sick leave and company holidays.
- At 6 months tenure with Spring, we offer parental leave of 18 weeks for birthing parents and 16 weeks for non-birthing parents.
- Access to Noom, a weight management program—based in psychology, that’s tailored to your unique needs and goals.
- Access to fertility care support through Carrot, in addition to $4,000 reimbursement for related fertility expenses.
- Access toWellhub, which connects employees to the best options for fitness, mindfulness, nutrition, and sleep in one subscription
- Access to BrightHorizons, which provides sponsored child care, back-up care, and elder care
- Up to $1,000 Professional Development Reimbursement a year.
- $200 per year donation matching to support your favorite causes.
Not sure if you meet every requirement? Research shows that women and people from historically underrepresented communities often hesitate to apply for roles unless they meet every qualification compared to other similarly-qualified candidates. At Spring Health, we are committed to fostering a workplace where everyone feels valued, empowered, and supported to Thrive. If this role excites you, we encourage you to apply.
Our privacy policy: https://springhealth.com/privacy-policy/
Spring Health is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex, marital status, ancestry, disability, genetic information, veteran status, gender identity or expression, sexual orientation, pregnancy, or other applicable legally protected characteristic. We also consider qualified applicants regardless of criminal histories, consistent with applicable legal requirements. Spring Health is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans. If you have a disability or special need that requires accommodation, please let us know.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Spring Health, 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 in Demand for This Role
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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Spring Health AI Hiring
Spring Health has 5 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, MLOps Engineer. Positions span Seattle, WA, US, New York, NY, San Francisco, CA, US. Compensation range: $205K - $242K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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