Interested in this AI/ML Engineer role at Care Access?
Apply Now →About This Role
About Care Access
Care Access is working to make the future of health better for all. With hundreds of research locations, mobile clinics, and clinicians across the globe, we bring world-class research and health services directly to communities that often face barriers to care. We are dedicated to ensuring that every person has the opportunity to understand their health, access the care they need, and contribute to the medical breakthroughs of tomorrow.
With programs like Future of Medicine, which makes advanced health screenings and research opportunities accessible to communities worldwide, and Difference Makers, which supports local leaders to expand their community health and wellbeing efforts, we put people at the heart of medical progress. Through partnerships, technology, and perseverance, we are reimagining how clinical research and health services reach the world. Together, we are building a future of health that is better and more accessible for all.
To learn more about Care Access, visit www.CareAccess.com.
How This Role Makes a Difference
As the Director of Engineering, you are someone who deeply cares about your team and the success of those around you. You are an experienced leader and manager with a strong background in SaaS-based product organizations, and you were once a developer yourself. You can hold your own in technical conversations without needing to write software daily. You take a servant-leadership approach to management, waking up every day to elevate your team and organization. You shine as a collaborator, are an impeccable communicator, and bring an energetic passion for learning and applying new technical paradigms.
In clinical trials, research site staff spend countless hours on manual, repetitive administrative tasks—chasing phone calls, answering basic questions, coordinating schedules, and running routine prescreening surveys.
You will lead the engineering organization building our AI Patient Engagement Agent—one of our newest and most transformative product initiatives. This team is engineering an intelligent, voice- and messaging-capable AI agent designed to handle standardized patient interactions, drive recruitment, manage appointments, and automate prescreening outreach at global scale.
The mission of this team is to eliminate operational friction and give hours back to clinical research staff so they can focus on high-value human care and medical decisions.
Please note that this position is hybrid role from the following location only: Boston, MA
This role requires one day per week in the Boston office.
How You'll Make An Impact
- Configurable Conversational Workflows: Building dynamic, script-driven conversation engines that can be customized across diverse clinical trials and tailored to distinct patient needs while guiding users toward interaction goals.
- Intelligent Logistics & Orchestration Engine: Engineering a resilient task engine that balances multi-factor variables—patient "safe-call" windows, local time zones, call-center operational hours, outcome tracking, and adaptive retry policies to maximize reach while maintaining strict best practices.
- Delivery & Roadmap Execution: Own the execution of the AI Patient Engagement engineering roadmap. Partner with Product and Design to translate complex trial protocols into intuitive AI interaction workflows and deliver reliable, high-impact software.
- Hands-on Architectural Guidance: Participate in architectural design reviews for real-time voice, messaging, workflow orchestration, and data pipeline engines. Ensure the team builds a resilient, highly available platform that scales without taking on technical debt.
- Sprint & Capacity Management: Balance feature delivery with platform stability, managing team capacity, resource allocation, and technical debt to keep patient outreach timelines on track.
- Agile Process Optimization: Refine agile ceremonies and continuous integration/continuous delivery (CI/CD) practices for your team. Eliminate operational roadblocks to foster a high-velocity shipping cadence.
- Line Management & Mentorship: Directly manage, mentor, and grow Engineering Managers and Tech Leads. Conduct regular 1-on-1s, build career growth trajectories, and recruit top engineering talent to expand the team.
- Team Health & Culture: Foster an inclusive, high-accountability environment where engineers are empowered, aligned, and motivated by the real-world healthcare impact of their work.
- Cross-Functional Collaboration: Serve as the technical point of contact across departments. Translate complex AI, telemetry, and platform concepts into clear operational updates for executive leadership and non-technical partners.
The Expertise Required
- People Leadership: Proven experience as a "manager of managers," directly leading, coaching, and developing Engineering Managers and Technical Leads.
- Technical Breadth: Strong background in scalable cloud architecture, event-driven systems, API-first SaaS platforms, or workflow automation engines.
- Execution Track Record: Demonstrated ability to own and ship complex, data-intensive, or automation-heavy products on time.
- Operational Expertise: Deep proficiency in Agile/Scrum methodologies, sprint planning, and modern CI/CD practices.
- Architectural Oversight: Active engagement in technical design reviews to guarantee platform durability, security, and scalability.
- Communication Skills: Exceptional ability to articulate technical strategies, risks, and trade-offs to non-technical stakeholders and leadership. + Preferred Qualifications:
- Experience or strong interest in conversational systems, telephony infrastructure (voice/SMS integrations), or AI/automation workflows.
- Prior experience working within HealthTech, Biotech, Clinical Trials, or Life Sciences (or another highly regulated domain with strict data privacy requirements).
Certifications/Licenses, Education, and Experience
- Minimum Education + BA or BS degree from college or university.
- Minimum Experience + 7+ years of software engineering experience, with at least 3+ years successfully managing engineering teams.
How We Work Together
- Location: Hybrid within the United States. This role requires work to be performed in a remote office environment.
- Travel: Occasional planned travel may be required as part of the role.
- Physical demands associated with this position Include: The ability to use keyboards and other computer equipment.
The expected pay range for this role is $180,000 - $230,000 USD per year for full time team members.
Benefits & Perks (US Full Time Employees)
- Paid Time Off (PTO) and Company Paid Holidays
- 100% Employer paid medical, dental, and vision insurance plan options
- Health Savings Account and Flexible Spending Accounts
- Bi-weekly HSA employer contribution
- Company paid Short-Term Disability and Long-Term Disability
- 401(k) Retirement Plan, with Company Match
Mandatory Employer Disclosures
Notice to Illinois applicants: Applicants are not obligated to disclose expunged juvenile records or adjudication, arrest, or conviction.
Notice to Connecticut applicants: Care Access may require applicants to submit to a urinalysis drug test in connection with an application for employment.
Notice to Arizona, Georgia, Indiana, and North Dakota applicants: Care Access complies with applicable laws prohibiting smoking in and around places of employment.
Notice to Massachusetts applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Notice to Rhode Island applicants: Care Access complies with Rhode Island law prohibiting smoking in enclosed areas within places of employment. Care Access is also subject to is subject to Chapters 29–38 of Title 28 of the Rhode Island General Laws.
Notice to Maryland applicants: UNDER MARYLAND LAW, AN EMPLOYER MAY NOT REQUIRE OR DEMAND, AS A CONDITION OF EMPLOYMENT, PROSPECTIVE EMPLOYMENT, OR CONTINUED EMPLOYMENT, THAT AN INDIVIDUAL SUBMIT TO OR TAKE A LIE DETECTOR OR SIMILAR TEST. AN EMPLOYER WHO VIOLATES THIS LAW IS GUILTY OF A MISDEMEANOR AND SUBJECT TO A FINE NOT EXCEEDING $100.
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Diversity & Inclusion
We work with and serve people from diverse cultures and communities around the world. We are stronger and better when we build a team representing the communities we support. We maintain an inclusive culture where people from a broad range of backgrounds feel valued and respected as they contribute to our mission.
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to, and will not be discriminated against on the basis of, race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other characteristic protected by law.
Care Access is unable to sponsor work visas at this time.
If you need an accommodation to apply for a role with Care Access, please reach out to: [email protected]
Salary Context
This $180K-$230K 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 Care Access, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. Disclosed range: $180K to $230K.
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.
Care Access AI Hiring
Care Access has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Boston, MA, US. Compensation range: $190K - $230K.
Location Context
AI roles in Boston pay a median of $215,000 across 360 tracked positions.
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
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