Interested in this AI/ML Engineer role at One Senior Care?
Apply Now →About This Role
Are you passionate about using artificial intelligence to transform the future of healthcare?
Are you a strategic thinker who can inspire others, solve complex challenges, and lead innovation from vision to execution?
If so, you may be the perfect fit for our One Senior Care family of businesses — including LIFE\-NWPA, Mountain View PACE, and Buckeye PACE.
Job Summary:
As the AI Strategy \& Innovation Lead, you'll help shape the future of how One Senior Care uses artificial intelligence to improve care, operations, and business processes. You'll develop our AI strategy, evaluate technology solutions, and build a roadmap that helps our organization adopt AI safely, responsibly, and effectively.
This is a player\-coach role, ideal for someone who enjoys both strategic planning and hands\-on technical work. You'll partner with leaders across the organization, guide internal teams, evaluate vendors, and help build a strong AI foundation that supports long\-term success.
Engagement Type:
- Near full\-time embedded 1099 contractor.
- Initial 9–12 month engagement with the opportunity to renew or transition into a permanent position based on mutual agreement.
Schedule:
First\-shift, full\-time hours in a hybrid work environment. Specific work hours will be established in consultation with your supervisor.
Core Responsibilities:
- Develop and maintain One Senior Care's AI strategy and implementation roadmap.
- Prioritize AI initiatives based on organizational needs, business value, and the organization's EMR transition.
- Establish guidelines for when to build custom AI solutions versus purchasing vendor products.
- Lead AI governance, including responsible AI practices, PHI\-safe policies, and review processes for new AI technologies.
- Evaluate AI vendors, platforms, and healthcare technology solutions.
- Support vendor contract negotiations, including healthcare\-specific privacy and security requirements.
- Design the organization's long\-term AI technical architecture, including data access, integrations, and infrastructure.
- Recommend technology stacks and development standards for internally built AI solutions.
- Build prototypes, review technical designs, and provide hands\-on guidance when needed.
- Partner with IT, Business Intelligence, clinical leaders, and executive leadership to drive AI initiatives.
- Provide regular updates on project progress, priorities, risks, and recommendations.
- Support knowledge transfer so internal teams can successfully maintain solutions after implementation.
- Participate in organizational planning and continuous improvement initiatives.
Technology is always evolving. Even if every project looks different, your ability to learn quickly, solve complex problems, and help others navigate change is what matters most. You'll have the opportunity to build something meaningful while helping shape the future of healthcare technology at One Senior Care.
What Makes You a Great Fit:
- Curious, innovative, and excited about emerging technologies.
- Comfortable balancing big\-picture strategy with detailed technical work.
- Strong communicator who can explain technical concepts to both technical and non\-technical audiences.
- Collaborative leader who builds trust and influences others without direct authority.
- Confident evaluating competing solutions and making thoughtful, data\-driven recommendations.
- Adaptable and comfortable working through changing priorities during large organizational initiatives.
- Organized, self\-motivated, and able to manage multiple projects simultaneously.
- Values teamwork, continuous learning, and practical problem solving.
Education and Experience:
- Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field preferred.
- Demonstrated experience leading AI strategy and implementation for a complex organization.
- Experience evaluating AI vendors, technologies, and enterprise platforms.
- Hands\-on experience with AI technologies, software development, system architecture, or machine learning.
- Experience working within healthcare or another highly regulated industry is strongly preferred.
- Experience supporting major technology transitions such as EMR or ERP implementations is a plus.
- Experience successfully transferring projects to permanent internal teams is preferred.
Requirements:
- Ability to evaluate technical solutions and write or review code and system designs.
- Knowledge of AI governance, security, privacy, and responsible AI practices.
- Understanding of healthcare technology, PHI protection, and data integration principles preferred.
- Strong project management, communication, and leadership skills.
- Successful completion of required background screening.
Physical Requirements:
- Must be able to move intermittently throughout the workday.
- Must be able to sit, stand, and work at a computer for extended periods.
- Must be able to travel occasionally to One Senior Care locations as needed.
- Must be able to communicate effectively with employees, vendors, and leadership both in person and virtually.
Join Us!
At One Senior Care, you'll help shape how technology supports better care for older adults while empowering employees with smarter tools and innovative solutions. Your work will have a lasting impact across our family of organizations as we continue building the future of healthcare.
One Senior Care is an Equal Opportunity Employer. Engagement is contingent upon successful completion of required background checks and any screenings or clearances required for healthcare programs.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At One Senior 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
One Senior Care AI Hiring
One Senior Care has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pittsburgh, PA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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