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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.
Job Description
Summary of Position
We are seeking an AI Engineer – Data \& AI Platform to architect, build, and operationalize scalable generative AI and machine learning solutions across Snowflake, Databricks, and modern MLOps ecosystems. This role will partner with data scientists, ML engineers, and platform teams to design and deploy end\-to\-end AI/ML pipelines, advance AI integration, and ensure production\-grade reliability for AI\-driven products.
The ideal candidate has 8\+ years of experience in AI/ML engineering, with deep expertise in generative AI deployment, API\-based AI services, large\-scale data processing, and AI lifecycle management. The AI Engineer will not only deliver hands\-on technical solutions but also set technical direction, mentor other engineers, and drive innovation across Teladoc's AI platforms.
Essential Duties and Responsibilities
- Operationalize GenAI and LLM applications, leveraging RAG (retrieval\-augmented generation), vector search, prompt engineering, agentic AI, and MCP (Model Context Protocol).
- Lead the design, development, and deployment of production\-grade LLM and ML pipelines, including data transformation, feature engineering, training, tuning, and serving.
- Architect scalable data and AI workflows on Snowflake, Databricks, and Azure ML, integrating AI models with modern data lakehouse platforms.
- Build and maintain API\-based AI services (FastAPI, Flask), enabling secure, performant, and reliable model access at scale.
- Define and implement CI/CD pipelines for GenAI and ML services, using GitHub Actions/Azure DevOps, MLFlow, and container orchestration (Kubernetes, Docker).
- Develop and enforce MLOps/LLMOps best practices, including experiment tracking, model versioning, observability, and governance.
- Mentor ML engineers and data scientists on engineering rigor, scalable design, and production\-readiness.
- Partner with cross\-functional teams to integrate AI services into products, ensuring security, compliance, and resilience in regulated healthcare environments.
- Troubleshoot production AI systems, analyzing inference latency, drift, and performance issues, and implementing preventive solutions.
- Document and communicate architecture patterns, operational standards, and AI development frameworks across the organization.
The time spent on each responsibility reflects an estimate and is subject to change dependent on business needs.
Supervisory Responsibilities
No
Qualifications Expected for Position
- Bachelor's degree in Computer Science, Engineering, Machine Learning, or a related field; equivalent work experience is acceptable.
- 8\+ years of experience in AI/ML engineering roles, with proven success in architecting and scaling production LLM/GenAI and ML systems.
- Experience deploying LLM and GenAI solutions including RAG, vector database integration, and agentic/tool‑augmented LLM systems (LangChain, MCP, or similar frameworks)
- Experience with Snowflake or Databricks, using one or both as core platforms for data processing or AI/ML workloads.
- Proven track record in MLOps/LLMOps, including CI/CD pipeline automation, model serving, monitoring, and governance, using modern AI infrastructure tools such as Docker, Kubernetes, Azure ML, MLflow, and Terraform.
- Proficiency in Python and SQL, with experience processing high‑volume datasets using big‑data tools such as Spark or equivalent distributed systems.
- Ability to collaborate in cross\-disciplinary teams (engineering, product, compliance, security) and deliver impact in regulated industries.
Bonus Qualifications
- Excellent communication skills to articulate complex GenAI/ML concepts to diverse audiences.
- Strong leadership and mentorship abilities, fostering technical growth across teams.
- Understanding of API\-driven AI development, including Python\-based API frameworks (FastAPI, Flask) or equivalent experience working with API development workflows.
- Certifications in Snowflake, Databricks, Azure ML, or AI/ML platforms preferred.
- Experience in healthcare AI applications and regulated AI deployment.
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: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $200K to $230K.
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.
Teladoc Health AI Hiring
Teladoc Health has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span New York, NY, US, Remote, US. Compensation range: $219K - $230K.
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 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 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).
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 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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