Interested in this AI/ML Engineer role at MedBridge?
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About This Role
Join the team shaping the future of healthcare! Medbridge is a dynamic software as a service company working with the country’s largest healthcare providers to build technology solutions helping patients get better faster, while decreasing the overall cost of care. Our Operations team is growing and looking for Product Engineer (AI)\- Operations to join us!
We hire in the following states: AZ, CO, FL, ID, GA, IL, NH, KS, MA, MI, MN, NC, NY, OH, OR, PA, SC, TN, TX, UT, VA, WA, WI.
Medbridge is establishing an AI Operations team to lead the integration of AI across our company and workflows. As a Product Engineer, you will own features end\-to\-end; rather than receiving pre\-specified tickets, you will partner directly with our Head of Operations to scope problems, design solutions, and execute at speed. Operating at the intersection of AI, product development, and full\-stack engineering, you will build and ship impactful tools in a high\-ownership, high\-judgment environment. This role requires moving quickly, making decisions with incomplete information, and transforming ambiguous problems into finished products.
We are embedding AI into the core of our development process and product capabilities. Our engineers utilize AI throughout their workflows to reach real patients and clinicians. If you want to build alongside a team that treats AI as a first\-class tool rather than a novelty, this is the place.
### In this role you will:
- Own stakeholder relationship across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
- Design, build, and ship AI\-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
- Build full\-stack applications using AI\-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
- Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production\-ready deployments.
- Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
- Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
- Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
- Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
- Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.
What you will need to succeed:
- 2\+ years of professional experience in software engineering, product engineering, or full\-stack application development.
- Hands\-on experience delivering real AI\-powered applications in production using tools such as Claude, Cursor, Codex, OpenAI, Anthropic, or similar AI development platforms.
- Practical experience integrating LLMs: prompt engineering, AI agents, generative AI workflows, or RAG architectures.
- Strong full\-stack fundamentals with proficiency in a modern language such as Python, TypeScript, or JavaScript, and a track record of building and deploying production\-grade applications.
- Product instinct: you ask who the user is and what they actually need before writing code, and you push back on solutions that miss the point.
- A bias toward shipping and learning over endless planning, balanced with the judgment to know when something needs more care.
- Genuine enthusiasm for using AI to work better and faster, and the discernment to know where it helps and where it does not.
- Clear written and verbal communication. You can explain a tradeoff to an engineer and the same tradeoff to a non\-engineer.
- Comfort with ambiguity and a track record of owning outcomes, not just tasks.
Nice to Have
- Experience building evals or quality harnesses for LLM\-based systems, and designing agent loops, tool integrations, or guardrails.
- Familiarity with vector databases, embeddings, semantic search, or orchestration and AI workflow automation frameworks.
- Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization and deployment technologies such as Docker and Kubernetes.
- Strong product design instincts and the ability to produce a clean, usable interface without a dedicated designer.
- Experience in healthcare, healthtech, or another regulated or high\-trust domain.
- A history of raising the bar on engineering practices: testing, review, observability, or team tooling.
Our Values:
Excellence: A job worth doing is a job worth doing RIGHT.
- We believe that excellence is never an accident. It’s the result of high intention, sincere effort, and intelligent execution.
- We are change\-makers who push the boundaries of what is possible, continually reimagining patient care.
- We realize that our work impacts people’s health and demands that we hold ourselves to the highest possible standards.
- We know a good thing when we see it; when exceptional talent comes our way, we hire them and help them grow.
- We understand the value of time, and we give our best effort everyday because we have a day in which to give it.
Fortitude: Our goals are big. Our dedication is bigger.
- We embrace ambitious and challenging projects with confidence in our ability to achieve them together.
- We are courageous as we venture into the unknown.
- We persevere in the face of difficulties.
- We do not let perfection be the enemy of progress; we focus on taking the next best step forward.
- We take ownership of our mistakes and of our successes, and we learn from both.
Service: We work for something more important than ourselves.
- We care deeply about our colleagues, customers, and patients, ensuring our work at Medbridge has a lasting impact.
- We take a multi\-disciplinary, data\-oriented approach to solving problems.
- We lead with confidence and humility, embracing a servant\-leadership mindset as we support and challenge one another to reach our shared goals.
- We know that if we do great work, we help people live healthier lives.
Salary Range: $150K\- $160K
*At Medbridge, salary ranges are assigned to a job based on 3rd party salary benchmark surveys. Individual pay within this range is informed by the candidate's skills, capabilities and experience.*
We embrace diversity and are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. No matter your background, your orientation, your identity expression, or whatever else makes you unique, if you want to raise the bar and join an amazing team of passionate people, then we'd love to work alongside you at Medbridge.
Take the Next Step
If you’re excited about this opportunity and think Medbridge is the right fit for your career, apply now! Our Talent Acquisition team will follow up with you shortly.
Please note: Due to reports of phishing, we're requesting that all Medbridge applicants apply through our official careers page at https://www.medbridge.com/careers. All official communication from Medbridge will come from email addresses ending with @medbridge.com.
Salary Context
This $150K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At MedBridge, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($155K) sits 29% below the category median. Disclosed range: $150K to $160K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
MedBridge AI Hiring
MedBridge has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bellevue, WA, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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