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About This Role
AI Solutions Engineer \& Full\-Stack Developer — Founding Technical Hire
About Us
Affinity CHC is a fast\-growing, concierge\-level home health care agency. We care for everyone from next\-door neighbors to high\-net\-worth families, and we hold ourselves to a white\-glove, elite standard in everything we do. We're at the point where our growth is outpacing our manual processes — and we're investing in technology to scale without ever losing our high\-touch quality of care.
The Role
We're hiring our first dedicated technical builder to design and build the systems that will run our company as we scale. You'll create our internal "AI brain" (a centralized knowledge base our team can ask questions of), build custom internal apps and tools, and automate the workflows that currently eat hours of manual work every week. This is a build\-it\-once\-and\-keep\-expanding role: you'll own the technical foundation and grow it with us over time.
You'll work directly with the founder, turn real operational problems into working software, and have genuine ownership of what you build.
What You'll Build
- An AI\-powered internal knowledge base ("brain") that answers staff questions ("What do we do when…?"), surfaces gaps in our processes, and turns our recorded trainings and manuals into self\-serve answers (using LLMs, retrieval/RAG, prompt engineering).
- Custom internal apps, dashboards, and tools that streamline staffing, HR/onboarding, billing/operations, and client care.
- Automations and integrations that connect our existing systems (scheduling/EVV, billing \& payroll, CRM, onboarding/compliance platforms) so our team stops manually importing/exporting data between programs.
- Workflow automation (e.g., automated client welcome emails, recurring care\-management tasks, caregiver follow\-ups) so nothing falls through the cracks as we grow.
- Over time: client\-facing tools, analytics/capacity modeling, and new products as we expand.
Must\-Haves (non\-negotiable)
- Strong coding ability — full\-stack. Comfortable building real applications end to end (e.g., Python and/or JavaScript/TypeScript, React, Node, REST APIs, databases). Coding is a must.
- Hands\-on AI / LLM experience. You've built with large language model APIs (Claude, OpenAI, or similar), and understand retrieval/RAG, prompt engineering, and how to build reliable AI\-powered apps. AI experience is a must.
- API \& automation skills. Experience integrating third\-party APIs, webhooks, and automation tools (Zapier, Make, n8n, or custom).
- Self\-starter who owns problems. You can take a vague business need, ask the right questions, and deliver a working solution without hand\-holding.
- Clear communicator. You can work closely with non\-technical people and explain technical choices in plain language.
Nice\-to\-Haves
- Experience in healthcare, home care, or other regulated industries.
- Understanding of HIPAA and best practices for handling sensitive/private data (you will work with confidential information).
- Experience building chatbots or AI assistants.
- Familiarity with no\-code/low\-code tools (Airtable, Retool) for shipping internal tools quickly.
- Cloud (AWS/GCP), authentication, and security fundamentals.
Who You Are
- A versatile builder/generalist who genuinely enjoys owning projects from idea to launch.
- Comfortable being the first (and for now, only) technical person — you like building from the ground up.
- Excited by the idea of building a foundation and watching it grow into something much bigger.
How to Apply
Submit your resume and a short note (a few sentences) about something you've personally built — ideally an AI, automation, or internal\-tool project. Links to a portfolio, GitHub, or live projects are strongly encouraged and will move you to the top of the pile.
We're moving quickly and will review applications as they come in.
*Affinity CHC is an equal opportunity employer. All qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, disability, age, or any other protected status.*
Pay: $55,000\.00 \- $65,000\.00 per year
Benefits:
- Flexible schedule
Experience:
- CPT coding: 1 year (Required)
- GitHub: 1 year (Required)
- Claude: 1 year (Required)
- Asana: 1 year (Required)
Work Location: Remote
Salary Context
This $55K-$65K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Home Care of SF, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($60K) sits 68% below the category median. Disclosed range: $55K to $65K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Home Care of SF AI Hiring
Home Care of SF has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $65K - $65K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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