Interested in this MLOps Engineer role at Lyra Health?
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About This Role
About Lyra Health
Lyra Health is a leading provider of evidence\-based mental health care, serving more than 20 million people globally in partnership with employers and more than 100 million through health plan and partner relationships. The company has delivered more than 15 million sessions of mental health care, published more than 35 peer\-reviewed studies, and delivered unmatched outcomes in terms of access, clinical effectiveness, and cost efficiency. Extensive peer\-reviewed research confirms Lyra’s transformative care model helps people recover twice as fast and results in a 26% annual reduction in overall healthcare claims costs. Lyra is transforming access to life\-changing mental health care through Lyra Empower, the only fully integrated, AI\-powered platform combining the highest\-quality care and technology solutions.
About the Role:
We are looking for an experienced Senior ML Engineer who is eager to build the tooling and services necessary to deliver ML and generative AI based products that make a significant impact within the organization. The ideal candidate will be enthusiastic about taking ownership of their work, spearheading cross\-functional projects, and providing guidance and mentorship to other team members.
Lyra is for you if you:
- Thrive on working with brilliant teammates to solve complex, meaningful problems
- Are passionate about making a social impact and supporting people at their most challenging moments
- Enjoy cross\-functional collaboration with physicians, therapists, data scientists, data analysts and product managers
### Responsibilities
- Build tooling and services that power the machine learning and generative AI powered solutions in production
+ Training and inference platform services
+ The model evaluation metrics and testing framework
- Build services that expose machine learning and AI based products
- Deploy and manage various applications that power various components of the the AI/ML SDLC process
- And of course, you will be coding every day!
### Qualifications
- 6\+ years of experience deploying ML/AI solutions in production environments
- Ability to write high\-quality code in Python
- Experience building RESTful APIs
- Experience defining and using Protobuf messages
- Experience working with Docker and deploying applications to Kubernetes
- Experience with relational and low\-latency databases
- Experience working with Celery
- A strong desire to work on ML/AI based products
- A desire to learn new technologies quickly
- A love of building systems from scratch
- A thoughtful approach to balancing quality and deadlines in fast\-paced settings
- Excellent communication skills with a talent for building consensus and alignment
- Strong organizational skills and the ability to distill complex problems into clear priorities that move the team and business forward
### Preferred Qualifications
- Experience building RAG (retrieval\-augmented generation) based solutions
- Experience setting up and maintaining vector databases
- Experience writing production code in Java/Kotlin
- Experience building solutions on cloud infrastructure, particularly AWS
- Experience working with highly sensitive data in a healthcare environment
As a full\-time Sr. ML Engineer (MLOps), you will be employed by Lyra Health, Inc. The anticipated annual base salary range for this full\-time position is $143,000 to $197,000\. The base range is determined by role and level, and placement within the range will depend on a number of job\-related factors, including but not limited to your skills, qualifications, experience and location. This role may also be eligible for discretionary bonuses.
Annual salary is only one part of an employee’s total compensation package at Lyra. We also offer generous benefits that include:
- Comprehensive healthcare coverage (including medical, dental, vision, FSA/HSA, life and disability insurances)
- Lyra for Lyrians; coaching and therapy services
- Equity in the company through discretionary restricted stock units
- Competitive time off with pay policies including vacation, sick days, and company holidays
- Paid parental leave
- 401K with up to 3% matching
- Monthly tech allowance
- We like to spread joy throughout the year with well\-being perks and activities, surprise swag, regular community celebration…and more!
We can’t wait to meet you.
"We are an Equal Opportunity Employer. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, age, disability, genetic information or any other category protected by law.
By applying for this position, you acknowledge that your personal information will be processed as per the Lyra Health Workforce Privacy Notice. Through this application, to the extent permitted by law, we will collect personal information from you including, but not limited to, your name, email address, gender identity, employment information, and phone number for the purposes of recruiting and assessing suitability, aptitude, skills, qualifications, and interests for employment with Lyra. We may also collect information about your race, ethnicity, and sexual orientation, which is considered sensitive personal information under the California Privacy Rights Act (CPRA) and special category data under the UK and EU GDPR. Providing this information is optional and completely voluntary, and if you provide it you consent to Lyra processing it for the purposes as described at the point of collection, for example for diversity and inclusion initiatives. If you are a California resident and would like to limit how we use this information, please use the Limit the Use of My Sensitive Personal Information form. This information will only be retained for as long as needed to fulfill the purposes for which it was collected, as described above. Please note that Lyra does not “sell” or “share” personal information as defined by the CPRA. Outside of the United States, for example in the EU, Switzerland and the UK, you may have the right to request access to, or a copy of, your personal information, including in a portable format; request that we delete your information from our systems; object to or restrict processing of your information; or correct inaccurate or outdated personal information in our systems. These rights may be subject to legal limitations. To exercise your data privacy rights outside of the United States, please contact [email protected]. For more information about how we use and retain your information, please see our Workforce Privacy Notice."
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, summarizing interviews, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $143K-$197K range is above the median for MLOps Engineer roles in our dataset (median: $168K across 34 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 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Lyra 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 $203,000 based on 85 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($170K) sits 16% below the category median. Disclosed range: $143K to $197K.
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.
Lyra Health AI Hiring
Lyra Health has 5 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer, Data Scientist. Based in Remote, US. Compensation range: $197K - $221K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 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).
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 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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