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About This Role
### ORGANIZATION OVERVIEW
Solely funded by philanthropists Gary and Mary West, West Health includes the nonprofit and nonpartisan Gary and Mary West Health Institute and Gary and Mary West Foundation in San Diego, and the Gary and Mary West Health Policy Center in Washington, D.C. These organizations are working together toward a shared mission dedicated to enabling seniors to successfully age in place with access to high\-quality, affordable health and support services that preserve and protect their dignity, quality of life and independence. Through a combination of medical research and policy initiatives, collaborations, education, and advocacy, West Health is committed to supporting and creating healthcare delivery models that improve care and access for our fast\-growing, diverse population of seniors. Data/Data Science is an important engine that powers our work. For more information, visit westhealth.org and follow @westhealth.
POSITION SUMMARY
West Health is seeking a Senior Applied AI Engineer to join our Data Science \& Analytics team. This is a hands\-on, delivery\-oriented role for a builder who can rapidly turn ideas into fully working applications and tools that create immediate, measurable value for our organization and the communities we serve. The ideal candidate pairs deep, practical expertise in applied AI with the speed and follow\-through to ship. You will partner closely with the Director of Data Science \& Analytics to design, build, and deploy intelligent systems—including multi\-agent architectures and automated workflows—that amplify the impact of our team and advance our mission of improving healthcare for seniors. Beyond building, you will bring a sharp eye for where AI can add the most value, helping the team spot the highest\-impact opportunities and apply the right tool to each one. This role is focused on applied use of existing AI and LLM tools, understanding what each is best suited for and combining them into well\-designed, reliable solutions. The right candidate can take a business problem or loosely defined concept and independently develop it into a polished, well\-documented tool with a thoughtful user experience. Above all, we are looking for someone genuinely energized by this technology—an engineer who is eager to grow alongside a fast\-moving field and is at their best when turning a rough idea into something real and working.
GENERAL DUTIES AND RESPONSIBILITIES* Applied AI Solution Development: Design, build, and deploy production\-quality AI\-powered tools and workflows using large language models, AI APIs, and orchestration platforms. Architect and implement multi\-agent systems, retrieval\-augmented generation (RAG) pipelines, and automated analytical workflows that address real organizational needs and are built for sustained daily use.
- AI Workflow Orchestration \& Integration: Build and maintain intelligent automation pipelines using platforms such as n8n, LangChain/LangGraph, Microsoft Copilot Studio, CrewAI, and similar orchestration tools. Leverage APIs and emerging integration standards such as Model Context Protocol (MCP) to connect AI capabilities with existing data infrastructure (Snowflake, Python, internal systems) and create seamless, reliable workflows that operate with minimal manual intervention.
- Data Analysis \& Strategic Insight: Conduct independent data analysis and contribute analytical thinking to team projects. Proactively identify opportunities where AI can unlock insights, surface patterns in large datasets, and recommend relevant analyses. Translate findings into clear, compelling data stories that inform leadership decisions.
- Solution Delivery \& Follow\-Through: Move effectively from concept to deployed solution. Take loosely defined ideas from leadership and independently scope, design, build, and deliver polished tools with intuitive interfaces, proper documentation, and sustainable architecture. Demonstrate consistent follow\-through from ideation to completion.
- Prompt Engineering \& LLM Optimization: Develop and refine sophisticated prompt architectures, system instructions, and evaluation frameworks that ensure consistent, high\-quality outputs from LLMs. Stay current on model capabilities across providers (OpenAI, Anthropic, Google, open\-source models) and recommend the appropriate tool for each use case.
- Knowledge Management \& Documentation: Build and maintain organizational knowledge bases, vector stores, and retrieval systems that enable AI tools to work effectively with proprietary data. Create clear technical documentation so that solutions are maintainable, transferable, and not dependent on any single individual.
- Collaboration \& Communication: Work cross\-functionally with data scientists, analysts, visualization developers, and organizational stakeholders. Communicate technical approaches in accessible language and participate in strategic conversations about where AI can drive the greatest organizational impact.
- Commitment to West Health’s values and mission.
- This role requires a regular in\-office presence from Tuesday through Thursday to support collaboration and business needs during core hours of 9 AM to 5 PM. Mondays and Fridays may be worked remotely, provided availability aligns with standard working hours. The primary focus is on fulfilling responsibilities, delivering results, and collaborating effectively with others.
QUALIFICATIONS AND EDUCATION* Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field; a Master’s degree is a plus.
- Minimum 5 years of professional experience in software development, data engineering, or data science, with at least 2 years of hands\-on applied AI/LLM experience in a professional setting.
- Demonstrated experience building and deploying production\-ready AI solutions used by real stakeholders. A portfolio or live demonstrations of past work is strongly preferred and will carry significant weight in the evaluation process.
- Proficiency with AI orchestration and agent frameworks such as LangChain, LangGraph, CrewAI, AutoGen, n8n, or similar tools for building multi\-step, multi\-agent AI workflows.
- Working knowledge of retrieval\-augmented generation (RAG) patterns, vector databases (e.g., Pinecone, ChromaDB, Weaviate, pgvector), and embedding models.
- Experience with LLM APIs and platforms including OpenAI, Anthropic Claude, Google Gemini, and/or open\-source models via Hugging Face or Ollama.
- Proficiency in Python and SQL; familiarity with cloud data platforms such as Snowflake is a strong plus.
- Experience with low\-code/no\-code AI platforms such as Microsoft Copilot Studio, Power Automate, or similar enterprise AI tooling.
- Familiarity with emerging AI integration standards such as Model Context Protocol (MCP) and tool\-use patterns for connecting LLMs with external systems is a plus.
- Solid foundation in data analysis, with the ability to independently explore datasets, identify patterns, and communicate findings through clear data narratives.
- Strong business acumen with the ability to connect technical capabilities to organizational goals and articulate the value behind both the data and the tools.
- Excellent communication skills with the ability to explain complex AI concepts to non\-technical audiences and collaborate effectively across teams.
- Self\-directed, highly motivated, and able to manage projects from ideation through delivery with minimal supervision.
- Must believe in public health and science
COMPENSATION AND BENEFITS* The estimated salary range for this position is $140,000 \- $180,000
We gladly offer:
+ Up to 10% Annual Performance Bonus – rewarding your hard work and success.
+ Hybrid Work Schedule (Must be located in San Diego) \- offering flexibility to balance your work and personal life.
+ Comprehensive Benefits Package – including Medical, Dental, Vision, Short\-Term Disability, Long\-Term Disability, Life Insurance, and a Flexible Spending Account to support your health and well\-being.
- 100% Premium Coverage for Employee Medical, Dental, Vision, Short\-Term Disability, Long\-Term Disability, and Life Insurance, plus 70% coverage for dependents for medical, dental and vision – ensuring both you and your family are well cared for.
+ Generous 5% Retirement Plan Match – helping you build a secure financial future.
+ Professional Development Reimbursements – investing in your growth and career advancement.
+ 15 Days of Paid Time Off plus 16 Paid Holidays – promoting a healthy work\-life balance and time to recharge
*West Health Institute is an Equal Opportunity Employer and does not discriminate against persons on the basis of race, color, religion, national origin, sexual orientation, gender, marital status, age, disability, or veteran's status.*
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Salary Context
This $140K-$180K range is below 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 West 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. This role's midpoint ($160K) sits 26% below the category median. Disclosed range: $140K to $180K.
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.
West Health AI Hiring
West Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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