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About This Role
Join the team leading the next evolution of virtual care.
At Teladoc Health, you are empowered to bring your true self to work while helping millions of people live their healthiest lives.
Here you will be part of a high-performance culture where colleagues embrace challenges, drive transformative solutions, and create opportunities for growth. Together, we’re transforming how better health happens.
Summary of Position
As a Principal Data & AI Platform Engineer, you will architect, build, and operationalize AI-augmented capabilities that make our data platform more resilient, observable, scalable, and intelligent. This role focuses on DataOps - the systems that operate and support data pipelines rather than the pipelines themselves - including AI-driven monitoring, failure detection and recovery, data quality, and operational decisioning.
The ideal candidate combines deep data and software engineering experience with hands-on experience productionizing AI and agentic capabilities in engineering workflows. You will set technical direction, build solutions, lead cross-functional architecture and implementation efforts, and mentor engineers across the organization.
Essential Duties and Responsibilities
- Architect and implement AI and agentic capabilities that improve the reliability, scalability, and operational maturity of data processing systems.
- Build intelligent pipeline failure detection and recovery, including automated retry, root-cause assessment, escalation to on-call teams, and downstream user notification.
- Develop AI-driven data quality and heuristic monitoring to detect anomalies, assess impact, and recommend or automate the next best action.
- Design and evolve large-scale DataOps and platform capabilities that support enterprise data consumers and hundreds or more users.
- Productionize AI within data engineering operations with appropriate evaluation, observability, human review, and fallback paths.
- Partner with data engineering, platform, cloud, security, and business/data consumer teams to define requirements and drive technical solutions.
- Lead architecture and design reviews and establish reusable engineering patterns and standards for resilient data operations.
- Contribute hands-on to implementation, troubleshooting, and performance and scalability improvements across modern data platforms.
- Evaluate and apply modern data and AI technologies based on fit, scalability, reliability, and operational value.
Mentor engineers and provide technical review and guidance on complex data platform and AI-enabled engineering problems. *
Supervisory Responsibilities
No
Qualifications Expected for Position
- Bachelor's degree in Computer Science, Engineering, or a related field; equivalent work experience is acceptable.
- 10+ years of experience across data engineering, software engineering, or platform engineering, including senior-level architecture and large-scale system design.
- 3+ years of hands-on experience applying AI, GenAI, or agentic capabilities to data engineering, platform, or operational workflows.
- Proven experience architecting or engineering large-scale data processing systems serving a broad population of users or downstream consumers.
- Hands-on experience productionizing AI-augmented or agentic solutions, including evaluation, monitoring, failure handling, and operational support.
- Strong experience with modern data platforms such as Databricks or Snowflake and large-scale/distributed data processing patterns.
- Demonstrated ability to lead technical design and execution across teams, communicate with technical and business stakeholders, and influence without direct authority.
- Travel : Up to 10% Travel expectations are approximate and subject to change based on business needs.
Bonus Qualifications
- Experience with agentic AI and engineering frameworks such as Databricks Agent Bricks, LangChain, Hugging Face, or similar technologies.
- Experience with context layers, semantic layers, model evaluation, or other patterns that make enterprise data more usable by AI systems.
- Deep experience with data quality, data observability, lineage, fault tolerance, or automated remediation at scale.
Experience in healthcare or another regulated industry. *
The base salary range for this position is $200,000 - $230,000. In addition to a base salary, this position is eligible for a performance bonus and benefits (subject to eligibility requirements) listed here: Teladoc Health Benefits 2026 . Total compensation is based on several factors including, but not limited to, type of position, location, education level, work experience, and certifications. This information is applicable for all full-time positions.
#LI-SS2 #LI-Remote
We follow a Flexible Vacation Policy, intended for rest, relaxation, and personal time. All time off must be approved by your manager prior to use. You will also receive 80 hours of Paid Sick, Safe, and Caregiver Leave annually. This applies to full-time positions only. If you are applying for a part-time role, your recruiter can provide additional details.
As part of our hiring process, we verify identity and credentials, conduct interviews (live or video), and screen for fraud or misrepresentation. Applicants who falsify information will be disqualified.
Teladoc Health will not sponsor or transfer employment work visas for this position. Applicants must be currently authorized to work in the United States without the need for visa sponsorship now or in the future.
Why join Teladoc Health?
- Teladoc Health is transforming how better health happens. Learn how when you join us in pursuit of our impactful mission .
- Chart your career path with meaningful opportunities that empower you to grow, lead, and make a difference.
- Join a multi-faceted community that celebrates each colleague’s unique perspective and is focused on continually improving, each and every day.
- Contribute to an innovative culture where fresh ideas are valued as we increase access to care in new ways.
- Enjoy an inclusive benefits program centered around you and your family, with tailored programs that address your unique needs.
- Explore candidate resources with tips and tricks from Teladoc Health recruiters and learn more about our company culture by exploring #TeamTeladocHealth on LinkedIn .
As an Equal Opportunity Employer, we never have and never will discriminate against any job candidate or employee due to age, race, religion, color, ethnicity, national origin, gender, gender identity/expression, sexual orientation, membership in an employee organization, medical condition, family history, genetic information, veteran status, marital status, parental status, or pregnancy). In our innovative and inclusive workplace, we prohibit discrimination and harassment of any kind.
Teladoc Health respects your privacy and is committed to maintaining the confidentiality and security of your personal information. In furtherance of your employment relationship with Teladoc Health, we collect personal information responsibly and in accordance with applicable data privacy laws, including but not limited to, the California Consumer Privacy Act (CCPA). Personal information is defined as: Any information or set of information relating to you, including (a) all information that identifies you or could reasonably be used to identify you, and (b) all information that any applicable law treats as personal information. Teladoc Health’s Notice of Privacy Practices for U.S. Employees’ Personal information is available at this link .
Salary Context
This $200K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Teladoc 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 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $200K to $230K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Teladoc Health AI Hiring
Teladoc Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $230K - $230K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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