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About This Role
Staff Software Engineer (Artificial Intelligence)
Remote
Engineering
Remote
Full\-time
About Fabric Health
At Fabric Health, we are powering boundless care by solving healthcare’s biggest challenge: clinical capacity. We aren’t here to disrupt healthcare; we’re here to fix it. We unify the care journey from intake to treatment, using intelligent automation to remove administrative burdens and make care delivery 2\-10x more efficient. Our technology empowers clinicians to move faster and focus on what matters most: the patient.
We are a mission\-driven team of brilliant minds trusted by leading organizations including Intermountain Health, OSF HealthCare, SSM Health, and MUSC Health. Our vision is backed by premier investors such as Thrive Capital, GV (Google Ventures), General Catalyst, and Salesforce Ventures. We move quickly for good reason, listen deeply to solve big challenges, and build products with the same care and quality we’d want for our own loved ones. Learn more: About Us \| News \& Press \| LinkedIn \| Careers
About the Role
We are looking for a Staff Software Engineer, AI with deep expertise to set the technical direction and define the architecture for advanced language and voice technologies that transform how patients and providers interact. In this strategic role, you will be a key contributor, mentoring senior engineers and driving Fabric’s most complex work across a range of AI and ML applications and techniques. This is a high\-leverage, technical leadership position at the core of Fabric’s production engineering vision. You will work cross\-functionally to pioneer novel and impactful applications of machine learning, agentic AI, and other modern technologies to meet Fabric’s existing and future business needs.
What You'll Do
As a Staff Software Engineer, AI, you will be instrumental in defining the technical roadmap and elevating the team’s capabilities in advanced AI concepts. Your primary responsibilities will include:
- Defining the end\-to\-end architecture for mission\-critical ML/AI applications and owning the entire SDLC of those applications.
- Pioneering and driving the productionization of ML and AI features in Python, integrating them seamlessly with core backend services.
- Setting technical standards and providing mentorship to the engineering team, raising the overall technical bar and driving best practices.
- Partnering with product and medical teams to architect appropriate, responsible safeguards and business constraints for all AI outputs at a system level.
- Collaborating with engineering leadership to design and evolve robust interfaces for the Data Science team’s applications, so they can be used by a wide array of products across the organization.
- Leading the way in designing and implementing automated evaluation frameworks to rigorously measure the accuracy, fairness, and performance of our systems.
- Serving as the technical owner for existing NLP and AI diagnosis production components, overseeing their maintenance and strategic improvement.
- Developing and driving adoption of comprehensive analytics to monitor system performance, identify systemic bottlenecks, and strategically prioritize improvements.
- Leading the organization's strategy for getting the most out of AWS Bedrock, focusing on resilience and cost\-efficiency.
- Maintaining a technical vision by rapidly researching, prototyping, and introducing new AI tools, APIs, and architectures that align with company needs.
- Shaping Fabric’s long\-term AI strategy and contributing significantly to the future of healthcare AI.
Why You Might Be a Good Fit* You care deeply about the mission: You are passionate about deploying technology that empowers patients to engage with healthcare quickly, easily, and effectively.
- You're an autonomous problem solver: You excel at breaking down complex problems in the AI space and finding effective solutions, operating with autonomy and ownership.
- You’re a leader in your field: You stay current on machine learning, foundation models, and algorithms related to text and text\-to\-speech technologies.
- You value robust and responsible AI: You're committed to building robust testing and monitoring systems that provide insight into real\-time performance, and you are dedicated to developing safeguards for responsible AI use.
- You thrive on collaboration: You enjoy working cross\-functionally across teams, integrating AI use cases into products by understanding their APIs and data systems.
- You're a clear communicator: You're skilled at communicating complex ideas clearly to both technical and non\-technical audiences.
This Might Not Be The Right Fit If...* You prefer a role that is focused purely on research rather than hands\-on, production\-level engineering and deployment.
- You are not comfortable with the ambiguity and fast\-paced nature of developing cutting\-edge AI systems from the ground up.
- You prefer to work in a silo rather than collaborating closely with product, medical, and other engineering teams.
- You are not passionate about the specific application of AI to solve real\-world problems in the healthcare industry.
Your Qualifications* You have a combination of the following experience and credentials appropriate to a Staff\-level engineering leader:
+ A masters' degree in a related field, or;
+ 8\+ years of experience in software engineering or applied machine learning, with a strong focus on building real\-world AI/ML systems, or;
+ Strong experience in developing healthcare\-specific AI/ML solutions; or
+ Demonstrable experience developing novel, highly impactful AI/ML solutions that handle sensitive data
- Proficiency in backend software engineering using Python.
- Solid understanding of embeddings and embedding databases.
- Familiarity with modern AI/ML frameworks and tools, with constant attention to new tools, trends, and technologies.
- Experience building and deploying cloud\-native applications on AWS.
- Demonstrated ability to bring models from research to production, solving for latency, scale, and reliability.
- Effective communication skills and the ability to work across disciplines in a fast\-paced, agile environment.
- Strong technical leadership skills, with a particular focus on growing and supporting a skilled, senior\-level team.
Bonus Points* Prior work on multimodal AI interfaces or agent\-based dialogue systems.
- Experience hosting, scaling, and fine\-tuning open\-source models.
- A passionate interest in improving healthcare access and outcomes through applied AI.
*The national pay range for this role is $165,000\.00 \- $210,000\.00 per year. Actual compensation will be determined by factors such as the candidate's geographic market, experience, skills, and qualifications. Certain roles may also be eligible for additional compensation, including a comprehensive benefits package such as medical, dental, vision, unlimited PTO, and a 401(k) plan, stock options and bonuses. If your compensation requirement is greater than our posted range, please still consider applying; a determination can be made based on unique qualifications. Expected compensation ranges for this role may change over time.At Fabric, we believe that a diverse workforce is essential to our success. We are an equal opportunity employer and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, or any other legally protected characteristic. We actively encourage individuals from all backgrounds to apply.* Recruitment Fraud Alert: Protect Yourself**
Fabric Health is aware of scammers attempting to impersonate employers. To ensure that any recruiting contact you receive is legitimate, please adhere to the following:* Verify the Domain: Official recruitment emails will only come from addresses ending in *@fabrichealth.com* or *@gem.com*. No other domain names are legitimate.
- Official Interview Tools: We use Gem for our recruitment process and Google Meet for all video interviews. Google Meet is always the platform used for your first interview; you will never be sent a Zoom link to set up or conduct an initial interview. All interviews are conducted via video unless specifically stated by our team as an audio call. We never conduct interviews via chat, social media, Skype, or WhatsApp.
- Zoom Usage: Zoom is utilized only for specific meetings set directly by our team for purposes outside of the standard interview process (e.g., coordination or onboarding discussions). It is never the first link you will receive from us.
- Authorized Contact \& Texting: Fabric will only contact you if you have submitted an application or if you are connected to a current employee who shared your information with us. We will only send text messages if you have provided explicit authorization and consent, either through your application or while communicating directly with our team. If you have not explicitly authorized us to reach out, treat any SMS or unsolicited outreach as fraudulent and do not respond.
- Sensitive Data: We will never ask you for sensitive personal or financial documents (ID, banking info, SSN) during the application, interview, or candidacy stages. All sensitive data is handled through secure internal systems post\-offer.
- Verify the Team: You can reference LinkedIn to verify members of our recruiting team; however, please remain vigilant as scammers may create fraudulent profiles. Always cross\-reference the sender's email domain with our official @fabrichealth.com address.
If you question the validity of a contact or receive a suspicious message, do not click any links. Report the issue immediately to careers\[email protected]. Please note: The security inbox is for reporting fraudulent activity only. Do not email this address for application status updates or to share application materials, as these will not be reviewed. Applications are only accepted and reviewed if submitted through our official application portal, and no application status information will be provided via the security email.
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Req ID: ENG2601
Salary Context
This $165K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Fabric 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($187K) sits 5% above the category median. Disclosed range: $165K to $210K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Fabric Health AI Hiring
Fabric Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $210K - $210K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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.
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