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About This Role
Tebra only initiates contact with candidates via email from an official Tebra email address (@tebra.com, @patientpop.com, or @kareo.com) or through our applicant tracking system, Greenhouse. We will only ask you to provide sensitive personal information through our official application portal — not via social media or text message. We do not conduct interviews via instant messaging.
About the Role
==================
We are seeking a hands\-on AI \& Automation Engineer to design, build, and optimize integrations, automations, and AI\-driven workflows that power our enterprise data and business operations. You'll own the Workato automation stack, develop resilient data pipelines between core platforms (Salesforce, NetSuite, Snowflake, Slack), and implement AI\-enabled capabilities that reduce manual work and accelerate insights.
This role requires deep technical execution skills in integration development, data modeling, and programming, combined with a practical understanding of AI workflows. You will collaborate directly with stakeholders to translate business needs into scalable, automated solutions — delivering measurable improvements in data accessibility, system efficiency, and operational performance.
Your Area of Focus
======================
Integration \& Automation Development
- + Design, build, and maintain Workato recipes, connectors, and orchestrations for Salesforce, NetSuite, Slack, and Snowflake.
+ Implement error handling, observability, and reusable design patterns to ensure reliability and scalability.
Agentic System Design
- + Architect multi\-agent systems: tool selection, planning loops, state management, human\-in\-the\-loop Checkpoints.
+ Partner with stakeholders to design corporate systems (Salesforce, NetSuite, Snowflake, Slack, etc) as agent\-callable tools, not just data sources.
+ Build guardrails, fallbacks and error recovery for non\-deterministic workflows.
Data/RAG Pipeline Design \& Management
- + Develop and automate ELT/ETL processes to support both BI Analytics and AI retrieval across similar datasets with different access patterns.
+ Build evaluation into every pipeline to ensure high quality results considering precision, answer faithfulness, latency and cost.
+ Own retrieval end\-to\-end including ingestion, chunking strategy, embeddings, vector storage and reranking as one connected system.
+ Implement data enrichment, quality checks, and architectural safeguards to maintain trusted datasets.
- AI Operations
- + Production observability: prompt/response tracing, cost monitoring, eval dashboards.
+ Document agent behavior, decision logic and failure modes.
+ A/B testing agent versions, model comparisons.
Programming for Automation
- + Write modular, reusable scripts in Python, Ruby, SQL, or JavaScript to support integration, data transformation, and automation tasks.
Governance \& Documentation
- + Apply data governance practices for lineage, security, and retention.
+ Maintain technical documentation, diagrams, and version control via GitHub, Confluence, and Jira.
Your Professional Qualifications
====================================
- 2\-5 years of experience in integration, automation, AI, software or data engineering, with 1\+ year hands\-on in Workato.
- Proven expertise with Enterprise iPaaS required (Workato, Mulesoft, or similar).
- Proficiency in designing and implementing integrations across Salesforce, NetSuite, Slack, and Snowflake.
- Hands\-on technical background in ETL/ELT, Reverse\-ETL, data modeling, SQL/T\-SQL, and programming (Python, Ruby, or similar).
- Experience using vector databases such as Pinecone, Snowflake Cortex Search and pgvector.
- Experience with AI/ML concepts and implementing AI workflows in enterprise environments.
- Hands\-on experience leveraging APIs and programming languages to build and maintain scalable enterprise integrations.
- Experience with Cloud Platforms such as GCP or AWS preferred.
- Hands\-on experience using APIs and programming languages to build and maintain scalable enterprise integrations.
- Experience with GitHub for version control, Jira for work tracking, and Confluence/Lucid for documentation.
- Strong problem\-solving, critical thinking, and ability to execute under minimal supervision.
- Proficiency with Snowflake, Salesforce, and NetSuite, with strong architectural and automation design skills preferred.
- Motivated to learn new technologies and develop new skills.
- Track record of introducing innovation in automation and AI while maintaining governance and system reliability.
- Workato Automation Pro or SnowPro certifications preferred.
*(For Recruiter use only)* \#LI\-SS1 \#LI\-Remote
About Tebra
===============
Tebra is the only all\-in\-one EHR\+ platform built exclusively for independent healthcare practices. Designed to replace the clunky, fragmented tools built for corporate systems, Tebra connects EHR software, billing, automation, telehealth solution, and marketing — so providers can spend less time on admin and more time with patients. More than 42,000 private practices trust Tebra to streamline operations, increase revenue, and reduce burnout — helping clinicians leave work on time and rediscover their purpose. Learn more at www.tebra.com.
Our Values
==============
Start with the Customer
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We get to know our customers \- and their patients \- and look at the world through their lens.
Keep It Simple
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Healthcare is too complex. We aim to simplify it for everyone.
Stay Entrepreneurial
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We reject the status quo and solve problems with creativity, perseverance, and a bias to action.
Better Together
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We are diverse, humble, and collaborative. We put the team first and win together.
Celebrate Success
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Life is short and joy is underrated. We take time to have fun and celebrate success.
Perks \& Benefits
=====================
United States: In addition to our healthcare benefits, we also offer amazing perks! Need work from home basics? We offer a discount through Dell! We also offer a number of resources to help you keep your mind and body healthy. Check out Gympass for a great workout, or TelusEmployee Assistance Program to find mental health resources, along with other resources for everyday occurrences.
Costa Rica: To assist with all of life's needs, Tebra also offers a wellness and childcare subsidy and a University/Education discount! We also offer a number of resources to help you keep your mind and body healthy. Check out Gympass for access to health and fitness apps, or Telus Employee Assistance Program to find mental health resources, along with other resources for everyday occurrences.
Compliance \& Privacy Disclosures
=====================================
*NOTE: Tebra is an equal opportunity employer. All applicants will be considered for employment without attention to age, race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.*
*California residents who apply or are recruited for a job with us: please carefully review our California\-specific Privacy Notice under the California Consumer Protection Act here:* *https://www.tebra.com/privacy\-policy/california\-supplemental\-notice/*
*If you would like to report a fraudulent Tebra job posting, please contact us at* *[email protected]* *and consider reporting your experience to the FBI's Internet Crime Complaint Center or the Better Business Bureau to help keep others safe online, too.*
*As part of our commitment to a fair and efficient hiring process, Tebra utilizes BrightHire, an interview intelligence platform, for our phone and video screenings.* *This technology records and transcribes interviews to help us ensure consistency, reduce bias, and make more informed hiring decisions.* *By applying for this position, you acknowledge that your interview may be recorded.*
Salary Context
This $101K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Tebra, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($108K) sits 40% below the category median. Disclosed range: $101K to $115K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Tebra AI Hiring
Tebra has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Corona del Mar, CA, US. Compensation range: $115K - $145K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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