Interested in this AI/ML Engineer role at HealthRIGHT 360?
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About This Role
HealthRIGHT 360 gives hope, builds health, and changes lives for people in need by providing comprehensive, integrated, compassionate care that includes primary medical care, mental health services, and substance use disorder treatment.
Benefits and perks:
- HR360 offers a robust benefits package, including PTO, 15 paid holidays, commuter benefits, retirement plans, and more!
- Employees qualify for public loan forgiveness programs
- Training and professional development opportunities
- Work with mission driven, compassionate colleagues and make a difference every day in the work that you do.The required work schedule for this position is: Monday-Friday 9AM - PM
JOB SUMMARY
HealthRIGHT 360 provides compassionate, integrated care—including primary medical, mental health, and substance use disorder treatment—to some of California’s most vulnerable communities. We are dedicated to fostering a diverse, equitable workforce and creating an inclusive, empowering environment for all employees.
The AI & Automation Solutions Developer reports to the Chief Quality & Analytics Officer, who oversees Data, EHR Training & Support, Billing, Quality Improvement, Behavioral Health Medical Services and Modernization Initiatives. The AI & Automation Solutions Developer will independently architect, prototype, develop, test, deploy, document, monitor, and continuously improve production-ready AI applications, agents, integrations, and automated workflows. The successful candidate will have an extensive track record of using modern AI-assisted development tools to rapidly turn ambiguous operational problems into reliable, secure, scalable solutions.
We are seeking an experienced practitioner who has personally built and deployed complex AI and automation solutions used in real-world operating environments integrating with legacy and modern systems. Your core mission is to champion the our agency’s AI and automation transformation and elevate our operational excellence to new heights. This role will serve as HealthRIGHT 360's lead hands-on technical practitioner for AI and automation – partnering with Data, EHR, Quality Improvement, IT, operations, clinical teams, and external vendors to build valuable systems.
The ideal candidate:
- Architects, builds and deploys efficient, effective and sustainable AI tools and automated workflows.
- Understands the tools developed are part of the overall solution architecture, not the solution itself.
- Works independently and proactively, maintaining accountability and timely follow-through on all projects.
- Takes pride in delivering high-quality work and is meticulous about details.
- Thinks critically to identify root problems, asks thoughtful questions, and proposes effective solutions.
- Collaborates well with others while demonstrating strong ownership of individual responsibilities.
Key Responsibilities
- Architect, develop, deploy, and maintain end-to-end AI applications and automated workflows, from prototype through production.
- Build AI agents, copilots, workflow automations, APIs, integrations, and internal applications that address high-value operational problems.
- Rapidly prototype solutions using AI-assisted software development and agentic development tools, then harden successful prototypes for reliable production use.
- Design and implement integrations across enterprise systems using APIs, databases, Power Platform, scripting, and other integration technologies.
- Write and maintain production-quality code, automated tests, documentation, source control, logging, monitoring, and error handling.
- Evaluate build-versus-buy decisions and rapidly assess emerging AI platforms and tools through hands-on prototypes.
- Establish technical patterns and reusable components that allow successful solutions to scale across programs and departments.
- Implement appropriate security, privacy, access controls, auditability, human-in-the-loop controls, and AI governance, particularly for healthcare and sensitive data.
- Measure the operational impact of solutions through metrics such as hours saved, cycle-time reduction, error reduction, adoption, reliability, and return on investment.
- Stay updated on the latest trends and advancements in automation and AI relevant to healthcare
- Provide regular updates on project status, outcomes, and potential risks to leadership and key stakeholders.
- Work across departments to align systems improvements with organizational goals and deliver scalable, sustainable solutions.
Position Requirements
Education and Experience
Required
- 5–7+ years of progressive experience building software, workflow automation, systems integrations, data applications, or related technical solutions, including substantial recent experience building and deploying AI-enabled solutions.
- Demonstrated experience and expertise using advanced AI tools, examples include CODEX, Claude, etc.
- Demonstrated portfolio of independently built solutions that have moved beyond proof-of-concept into operational or production use.
- Advanced proficiency with Microsoft Power Platform, including Power Automate and Power Apps
- Experience designing solutions that interact securely with enterprise data and applications.
Desired
- Bachelor's degree in Computer Science, Information Technology, AI, Healthcare Informatics, or a related field.
- Demonstrated proficiency in at least one programming/scripting language, such as Python, JavaScript/TypeScript, PowerShell, or C#.
- Advanced experience with REST APIs, JSON, authentication/OAuth, webhooks, and systems integration.
- Hands-on experience building applications using LLM APIs and modern generative AI development frameworks.
- Hands-on experience building locally hosted, tuned, and trained models.
- Experience working in community health setting
Knowledge
Required
- Strong familiarity with AI tools and technologies, such as machine learning algorithms, natural language processing, and robotic process automation.
- Well versed in Microsoft Power Platform suite
- Culturally competent and able to work with a diverse population.
Desired
- Knowledge of clinical documentation and healthcare processes (BH and Primary Care specific--ICD-10, BH progress notes, treatment plans, etc.)
Skills and Abilities
- Ability to independently troubleshoot complex technical problems across multiple systems.
- Strong interpersonal communication and experience working with cross-functional teams
- Excellent written and verbal communication skills
- Excellent problem-solving and analytical skills
- Skilled in emotional intelligence
- Ability to thrive and adapt in a fast-paced and rapidly growing environment
- Comfortable working with people from a broad range of backgrounds from highly technical to healthcare professionals and front-line staff
- Demonstrated problem-solving skills and ability to think critically.
- Demonstrated experience taking projects from conception to completion
- History of contributing to a team culture of operational excellence, customer service, and growth mindset
- Ability to prioritize based on business impact
Physical requirements
The physical requirements section describes the physical demands and environment of the job and lists the basic physical conditions needed to perform the job. This section should also list specific physical requirements such as lifting heavy objects and standing for long periods of time.
In an eight-hour workday employee is required to perform work:
Sitting: 7.75 hours
Standing: 0.00 hours
Walking: 0.25 hour
Driving: occasionally
In an average month job holder must travel out of town:
Often (over twice a month) Sometimes (less than twice a month) Never
X
In an eight-hour workday, employee must:
Not at all Occasionally-(1/4-2½ hrs.) Frequently-(2½-5½ hrs.) Continuously-(5½-8 hrs.)
Bend/stoop X
Climb X
Reach above shoulder level X
Kneel X
Balance X
Push/pull X
Squat X
Crawl X
Crouch X
HealthRIGHT 360 is committed to fair and equitable compensation practices. Placement within the compensation range is based on factors such as relevant experience, education, credentials, internal equity, and organizational compensation practices. In addition to salary, HealthRIGHT 360 offers a comprehensive benefits package, subject to eligibility requirements
The compensation range listed reflects the expected pay for this position based on the primary work location. Actual compensation may vary depending on geographic location and applicable state or local pay transparency requirements.
HealthRIGHT 360 is an equal opportunity employer committed to fostering a diverse, equitable, and inclusive workplace. We prohibit discrimination, harassment, and retaliation based on any characteristic protected by applicable federal, state, or local laws, including race, color, ancestry, national origin, religion, sex, pregnancy, gender identity, gender expression, sexual orientation, age, disability, medical condition, genetic information, veteran status, marital status, and any other protected characteristic. We are committed to providing equal employment opportunities through fair and equitable recruitment, hiring, compensation, development, and advancement practices.
We will consider for employment qualified applicants with arrest and conviction records.
Must complete a background check and Live Scan.
Salary Context
This $130K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At HealthRIGHT 360, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $130K to $160K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
HealthRIGHT 360 AI Hiring
HealthRIGHT 360 has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $160K - $160K.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% above the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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