Interested in this AI/ML Engineer role at Abby Care?
Apply Now →Skills & Technologies
About This Role
About Abby Care: Powering the future of care at home for all of America.
Abby Care is building the leading AI-native platform for family-led care. America is facing a growing care crisis. Millions more people need care at home than ever. Over 50 million family caregivers support loved ones without the tools, training, or recognition they deserve.
We believe families are the largest untapped caregiving workforce in America, and that technology can help them deliver better care while driving stronger outcomes and greater transparency across the healthcare system.
Abby Care combines clinical oversight with an AI-powered platform to train, enable, and support family caregivers in delivering high-quality care at home. Our platform helps health plans and government partners better understand, verify, and improve care in the home. We expand access to care, reduce reliance on higher-cost settings, and help ensure public dollars are spent effectively.
We are proud to partner with leading health plans, providers, and community organizations and are backed by top VCs. We envision a future where family-led care is a core part of the healthcare system. Abby Care is building that future.
Join us in solving one of the most important challenges of our time.
The Opportunity
Abby Care is building an AI-native operating system for home care—one that manages the full lifecycle from caregiver and patient intake through care delivery, billing, and compliance.
As we expand our capacity to support more family caregivers at scale, we’re looking for an Applied AI Engineer to build AI-powered products and workflow automation that help caregivers, clinicians, and operational teams deliver better care.
Reporting to the Director, Applied AI, you will work on practical, high-impact problems such as collecting and interpreting clinical documents, automating intake and prior authorization work, assisting caregivers and nurses, detecting operational or billing risks, and routing exceptions to the right person.
You will contribute across the development lifecycle: understanding the underlying workflow, prototyping solutions, preparing datasets, building evaluations, implementing production systems, and learning from real-world usage.
Curious and highly resourceful, you are excited by both emerging AI capabilities and rigorous software engineering. You want to move beyond demos and build systems that are reliable, observable, and useful in consequential workflows. If you are excited to grow as an Applied AI engineer while helping transform how care is delivered in the home, we’d love to hear from you!
This is a Full-Time Hybrid opportunity based in San Francisco.
What you’ll work on:
Build AI-powered products and automation: Develop agents, copilots, and intelligent workflows that help caregivers, clinicians, and operational teams deliver better care at scale.
Develop production systems: Build the backend services, APIs, data pipelines, integrations, and reusable AI components required to move solutions from prototype to reliable production use.
Work with complex healthcare information: Create systems that extract, retrieve, and reason over clinical documents, conversations, operational records, and other structured and unstructured data.
Evaluate and improve system quality: Build representative datasets, measure performance, analyze failures, and improve models, prompts, retrieval, tools, and workflow logic based on evidence.
Learn directly from users and operations: Partner with Product, Design, Clinical, and Operations to understand real workflows, identify edge cases, and rapidly iterate based on production feedback.
Contribute to strong engineering foundations: Write maintainable, well-tested code and help build shared capabilities for orchestration, tool use, structured outputs, monitoring, tracing, and evaluation.
What success looks like:
- You consistently ship AI-powered capabilities that solve real problems for caregivers, clinicians, and operational teams.
- Your work progresses from prototype to reliable production functionality rather than stopping at demonstration-stage experiments.
- The systems you build are supported by clear evaluations, monitoring, and documented limitations.
- You use production feedback and error analysis to improve system quality over time.
- You collaborate effectively with product engineers, domain experts, and operational users.
- You develop reusable components that accelerate future Applied AI work.
- You grow your ability to independently own increasingly complex projects and technical decisions.
- You build a strong understanding of when to use AI, deterministic software, or human review to produce the best outcome.
What you’ll have:
- 2+ years of professional software engineering, machine learning, or Applied AI experience, or equivalent demonstrated ability.
- Strong programming skills and software engineering fundamentals.
- Experience building applications using language models, machine-learning models, or modern AI APIs.
- Familiarity with backend development, APIs, data processing, testing, and production software systems.
- Ability to structure ambiguous problems, test assumptions, and iterate based on evidence.
- Interest in building evaluations and understanding why AI systems succeed or fail.
- Strong ownership, curiosity, and willingness to engage directly with users and operational workflows.
- Clear written and verbal communication skills.
- Must reside in or be willing to permanently relocate to the San Francisco Bay Area.
- Experience with Python, agent architectures, retrieval-augmented generation, document intelligence, multimodal models, structured generation, model evaluation, monitoring, or prompt optimization is preferred but not required.
- Experience in healthcare, fintech, insurance, another regulated environment, a startup, or an operations-intensive business is preferred but not required.
Benefits:
- Competitive compensation packages that reflect the value you bring. We reward our team for the impact of their work.
- Comprehensive health coverage that works for you. Choose from high-quality medical dental and vision options, including a $0 deductible PPO and a company-funded HSA, alongside employer-paid life and disability insurance.
- Generous paid time off. We provide policies that allow you to recharge along with 10 paid company holidays.
- Financial savings benefits to support your future. We support your financial well-being with HSA contributions, optional FSA and commuter benefits, and full coverage of all 401(k) account fees (employer match not currently offered).
- Paid parental leave to support your growing family. We provide paid leave, so you can focus on bonding and adjusting to life as your family grows.
Our Values
- Families First
Redefining healthcare starts with how we treat the parents and children we serve. We go above and beyond for every family, building strong, lasting relationships. We continually ask ourselves, “Would we want this for our own families?”
- Urgency with Precision
Millions of families are waiting for care, and they cannot wait, therefore this is not your typical 9 to 5 job. We match their urgency with our own, delivering exceptional care without compromise. Here, speed and excellence go hand in hand.
- Relentlessly Resourceful
As an ambitious startup, we adapt quickly and make the most of limited time and resources. We solve challenges with creativity to deliver results without unnecessary complexity.
- Purpose with Positivity
We take our mission seriously while never losing sight of the people behind the work. Respect, kindness, memes, and coffee make us stronger as a team and better for the families we serve.
- Driven to Redefine What’s Possible
We are here to make healthcare better, which means asking hard questions, challenging outdated systems, and finding smarter, more compassionate ways to deliver care.
Equal Opportunity Employer
Abby Care is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, national origin, religion, sex, gender identity, sexual orientation, age, disability, veteran status, or any other legally protected basis. If you have a disability and require reasonable accommodation during any portion of the application or hiring process, contact [email protected]*.*
Abby Care considers qualified applicants with arrest or conviction records for employment and conducts background checks consistent with applicable law, including the California, Los Angeles County, San Francisco, Philadelphia, and New York City Fair Chance laws. We are an E-Verify participating company.
Automated Decision Tools
Abby Care may use automated decision tools to help match and rank candidate work experience, education and skills found in online profiles, resumes and job applications against job requirements. We believe that these tools help ensure objective, data-based decisions during the hiring process, however, all Abby Care hiring decisions involve final human review and input. If you have questions or would like to request an alternative process, contact [email protected]*.*
Compensation Range: $175K - $235K
Salary Context
This $175K-$235K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Abby Care, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $175K to $235K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Abby Care AI Hiring
Abby Care has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, San Francisco, CA. Compensation range: $235K - $235K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.