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About This Role
The ML Ops Engineer II at Boston Children’s Hospital is an integral part of the Cardio Engineering Team within the Department of Cardiac Surgery (https://www.cardioengineering.org). This role is pivotal in developing and scaling advanced AI and machine learning projects, enhancing data frameworks, and optimizing data flows to support the hospital’s strategic initiatives. The ML Ops Engineer II works in close collaboration with data scientists and various stakeholders across the hospital to develop solutions that improve patient care outcomes and operational efficiency. Focused on innovation and technological advancement, the ML Ops Engineer II ensures the robust integration of data science into clinical and administrative processes.
Key Responsibilities
Leads the development, implementation, and scaling of machine learning models and other AI\-driven projects within the Data \& Analytics team.
Collaborates with data scientists and other stakeholders to translate complex model requirements into operational systems.
Ensures robust and scalable infrastructure to support AI deployments and continuous learning cycles.
Utilizes SQL, Python, and dbt to build and optimize data models that support complex queries and analyses essential for AI projects.
Interprets data analysis results and communicates findings to both technical and non\-technical stakeholders, ensuring actionable insights.
Works closely with healthcare professionals, researchers, and administrators to define and refine data requirements for AI applications.
Provides expert guidance and mentorship to junior engineers and other team members on data science and engineering best practices.
Manages multiple AI and data science projects, ensuring effective communication, adherence to timelines, and delivery within budget.
Acts as a project lead, coordinating efforts across different teams and ensuring project milestones are met.
Develops project plans, tracks progress, and adjusts resources and timelines as needed to ensure successful project delivery.
Partner with Innovation and Digital Health Accelerator to operationalize AI within BCH.
Actively seeks and integrates new technologies and methodologies to enhance the capabilities of AI projects and data processes.
Stays informed of the latest industry trends and technologies, advocating for the adoption of innovative solutions that can provide competitive advantages.
Encourages and leads initiatives to explore and adopt innovative solutions that provide competitive advantages.
Ensures the integrity and reliability of data used in AI and machine learning projects, adhering to quality standards and compliance requirements.
Maintains comprehensive documentation of all data processes, models, and code to ensure reproducibility and adherence to internal and external regulations.
Conducts regular code reviews and quality checks to ensure best practices are followed and standards are met.
Minimum Qualifications
Education:
A bachelor's degree in computer science, engineering or a related field; master's degree is preferred.
Experience:
3\-5 years of relevant experience in data engineering, including project leadership responsibilities and advanced technical contributions to AI or machine learning projects.
Proficient in SQL and an understanding of database management systems.
Familiarity with ETL tools; experience with debt is highly advantageous.
Familiarity with nnU\-Net and other CNN and deep learning approaches for image segmentation and processing
Strong capabilities in Python or another advanced scripting language, essential for AI and machine learning model development.
Experience with cloud\-based data platforms and tools such as AWS, Azure, or GCP is a plus.
Demonstrated experience in managing multiple projects, including developing project plans, tracking progress, and adjusting resources and timelines.
Status
Full\-Time
Regular, Temporary, Per Diem
Regular
Standard Hours per Week
40
Pay Range
$93537\.60\-$149656\.00 Annual
Office/Site Location
Boston
Job Posting Category
Research
Remote Eligibility
Part Remote/Hybrid
Salary Context
This $93K-$149K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Boston Children's Hospital, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($121K) sits 43% below the category median. Disclosed range: $93K to $149K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Boston Children's Hospital AI Hiring
Boston Children's Hospital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $149K - $149K.
Location Context
AI roles in Boston pay a median of $210,000 across 166 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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