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About This Role
The Massachusetts Medical Society (MMS) is the statewide professional association for physicians and medical students, supporting 25,000 members. We are dedicated to educating and advocating for the physicians of Massachusetts and patients locally and nationally. A leadership voice in health care, the MMS contributes physician and patient perspectives to influence health\-related legislation at the state and federal levels, works in support of public health, provides expert advice on physician practice management, and addresses issues of physician well\-being. Under the auspices of NEJM Group, the MMS extends our mission globally by advancing medical knowledge from research to patient care through the *New England Journal of Medicine*, *NEJM Evidence*, *NEJM AI*, NEJM Catalyst, NEJM Clinician, and through our accredited and comprehensive continuing medical education programs.
The world has changed, and so has the way we work. The MMS has adopted a flexible work model that allows most employees to choose where they work – at home, onsite in our Waltham office, or a combination of the two – based on their preferences and our business needs. Because what matters is the work we do, not where we do it.
The AI Licensing Support Specialist is responsible for providing administrative and operational support to the AI Licensing team and other internal and external stakeholders. This position is responsible for tracking all AI licensing opportunities using current software systems such as Salesforce and Jira. Coordinates pipeline reporting and assists with other reporting. Duties include executing MNDAs, preparing term sheets, and assisting with onboarding of licensees including invoicing and coordinating data delivery.
Responsibilities:
- Responsible for monitoring and tracking all AI licensing opportunities from qualification through execution of an agreement.
- Organizes and maintains all documentation related to AI licensing in a centrally shared location.
- Works closely with Managing Director, New Business \& Strategic Partnerships, and General Manager, AI Product and Business Development, to ensure accurate and timely creation of internal and external deliverables including MNDAs and term sheets.
- Serves as primary onboarding and support liaison for new customers ensuring that customers get timely access to licensed information.
- Serves as primary internal liaison for onboarding of new customers ensuring timely and accurate invoicing and coordinating publicity and marketing copy review.
- Assists with managing reporting from licensees.
- Assists with project management of AI product development and business development initiatives.
- Assists with scheduling and planning of business meetings and attends meetings with prospects and customers to take notes and capture follow\-up tasks.
- Responsible for maintaining biweekly pipeline reports and other reporting as required.
- Other duties as required.
Qualifications:
- A Bachelor’s degree, or equivalent relevant experience, is required.
- At least 3 years of related administrative experience (preferably in sales or licensing).
- Proven ability to be proactive, work independently, prioritize workload, deliver high quality results and meet deadlines in a multi\-project, fast paced sales environment with minimal supervision.
- Meticulous attention to detail, resourcefulness, flexibility, reliability and willingness to learn are essential.
- Proficiency with MS Office applications and Salesforce.
- Strong experience with reporting is essential as is an ability and willingness to learn new software.
- Strong organizational, problem\-solving, analytical, follow\-up and time management skills.
- Excellent verbal and written communication skills, including demonstrated ability to draft clear and concise correspondence.
- Excellent interpersonal and customer skills. Ability to speak articulately and credibly with a professional demeanor to successfully relate to a wide diversity of staff and business partners.
- Must be able to work effectively and productively, in a team\-based environment, across all levels of staff.
- Aptitude for handling sensitive and confidential information with discretion and good judgment.
Compensation:
The target base pay for this role is $60,000\-$70,000\. Salary is commensurate with skills, qualifications, and experience.
Benefits:
Our generous benefits offerings include: 2 weeks of paid vacation, 6 personal days, 12 sick days, 13 paid holidays, medical and dental plans, 401(k) plans with company match, backup childcare assistance, tuition assistance and more!
The MMS has earned praise as one of the Top Places to Work in Massachusetts by *The Boston Globe* for the past 15 years in a row! The Globe surveys employees regarding their opinions about company leadership, benefits, ethics, values and culture, and recognizes those companies who receive high marks from their employees.
*The MMS is an Equal Opportunity Employer, committed to providing opportunities to veterans and people with disabilities and a work environment that is welcoming to all.*
Salary Context
This $60K-$70K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Massachusetts Medical Society, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($65K) sits 65% below the category median. Disclosed range: $60K to $70K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Massachusetts Medical Society AI Hiring
Massachusetts Medical Society has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Waltham, MA, US. Compensation range: $70K - $70K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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