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About This Role
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 Senior Engineering Manager of the AI & ML Platform team, this Senior Machine Learning Engineer will play a key part in building and scaling a centralized AI platform (services, tools, best practices, and more) that powers our care capabilities. This role is part of the AI & ML Platform team and is instrumental in stewardship of a shared foundation of AI and ML development at Spring Health.
Please note this is a hybrid role based in San Francisco with an expectation to be in the office 2-3 days per week at our 44 Montgomery location. Candidates must be based in the San Francisco area or able to relocate independently within 90 days of their start date. Occasional travel will be required for team on-sites.
What you'll do
- Collaborate to build and scale our AI platform, tooling, and best practices, enabling the rapid deployment of GenAI capabilities across the organization.
- Monitor and maintain the uptime of critical AI and ML tools to ensure high availability for key platform features.
- Contribute to backlog prioritization by identifying high-impact engineering opportunities that drive internal adoption of our centralized AI platform.
- Act as a technical advocate for the team by participating in on-call rotations, hosting internal office hours, and contributing to cross-functional AI working groups.
- Lead refactoring initiatives for key platform services to establish and enforce centralized coding standards for engineering contributors.
- Partner with machine learning teams to consult on and periodically modernize MLOps best practices.
- Drive cross-team collaboration to accelerate the adoption of AI and ML platform offerings, serving as a technical partner and consultant to feature teams.
- Troubleshoot and debug cloud (AWS and Azure) and Kubernetes (K8s) infrastructure issues to ensure the reliability and availability of platform services.
- Identify and recommend process optimizations to help the team effectively balance feature development, operational support, and internal consultations.
What success looks like in this role
- Maintain internal support service level agreements (SLAs) by achieving a 24-hour initial response time and ensuring active follow-up or resolution within 72 hours for cross-functional engineering inquiries.
- Reduce feature time-to-production for GenAI capabilities to meet established internal velocity targets.
- Improve internal Net Promoter Score (NPS) among engineering teams by reducing implementation friction and establishing centralized adoption standards for GenAI features.
What you'll bring
- Degree in Computer Science, Data Science, or a related field with a focus on Artificial Intelligence and Machine Learning.
- 4-6 years of Python development experience with GenAI and ML libraries and frameworks (such as LangChain, Pydantic, Scikit-Learn, and more).
- Experience maintaining and configuring engineering tools (third-party and in-house built), including deployment within a Kubernetes stack.
- Experience driving the adoption of generalized platforms with a continuous focus on improving internal Developer Experience (DX).
- Demonstrated ability to architect solutions for a full project as a lead prior to implementing a new feature, software, or tool in an enterprise environment while keeping project and organization constraints in mind.
- Demonstrated ability to mentor junior engineers and communicate complex technical ideas effectively to both technical and non-technical audiences to create buy-in and alignment.
Nice to have:
- Experience with MLOps and best practices for creating and deploying machine learning models.
- Background in a DevOps-style operational environment, including on-call rotations and cloud infrastructure management.
- Experience debugging and resolving issues within cloud environments (such as AWS or Azure) and Kubernetes clusters.
- 1-2 years of Ruby on Rails experience
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.
Compensation Range: $183K - $205K
Salary Context
This $183K-$205K range is above the median for MLOps Engineer roles in our dataset (median: $157K across 20 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 9,653 AI roles we're tracking, MLOps Engineer positions make up 2% of the market. At Spring Health, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $205,000 based on 151 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($194K) sits 5% below the category median. Disclosed range: $183K to $205K.
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 San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% above the national median.
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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.
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