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About This Role
Overview
A career with us means you’ll work alongside exceptional people and be empowered to reach your professional and personal goals. Our employees are at the foundation of what enables MassMutual to deliver on our purpose to help people secure their futures and protect the ones they love.
We embrace the idea that we all are stronger and better through our support for one another. We strive to create a culture where employees feel valued and are celebrated for who they are.
Job Description
The Opportunity
MassMutual’s AI Platform Engineering team is seeking an impact\-driven Engineering professional to lead and grow the team that builds the infrastructure powering AI development across the company. You will own the people leadership, hiring, and day\-to\-day execution of the AI Platform team, partnering closely with technical leadership to translate roadmap priorities into delivery while building a high\-performing, high\-trust engineering culture.
The Team
This is a unique opportunity to lead a team at the center of how MassMutual builds and operates the platform powering its AI initiatives. The team operates at the intersection of cloud infrastructure, AI/ML systems, and developer experience—delivering foundational capabilities that shape how the entire organization builds and deploys AI. As the engineering director, you will partner closely with the AI engineering, product, and cloud engineering teams across the enterprise, and you will invest in your team’s growth through a culture of peer learning, candid feedback, and shared technical standards. This team is defined by a shared commitment to engineering excellence, clear documentation, and the kind of people leadership that helps engineers do their best work.
The Impact
Own the people leadership of the AI Platform development team—hiring, onboarding, performance management, and career development—for a team of AI platform engineers.
Drive execution against the AI platform roadmap—partner with key stakeholders to break strategic priorities into achievable sprints and milestones and hold the team accountable to delivery.
Stay technical enough to lead—participate in architecture discussions and design reviews for systems like agent runtime infrastructure, tool/plugin registries, memory and context management layers, and agent observability pipelines, even when you're not writing production code day to day.
Champion engineering excellence: set and uphold standards for code quality, reliability, on\-call practices, and operational maturity across the platform.
Manage up and out: provide clear, honest reporting on team progress, risks, and resourcing needs, and communicate technical trade\-offs to senior stakeholders.
Be the primary advocate for your team’s wellbeing and growth—removing blockers, building a high\-trust culture, and raising the team’s technical craft through design reviews and documentation habits.
The Minimum Qualifications
3\+ years of experience as a people manager of software, platform, or infrastructure engineers, plus 5\+ years of overall software engineering experience.
Demonstrated track record of hiring, coaching, and retaining strong engineers, with experience leading teams through ambiguous, fast\-moving priorities.
Working knowledge of cloud\-native architecture: Kubernetes, managed cloud services, networking, and multi\-tenancy patterns across AWS, GCP, or Azure, sufficient to engage credibly in technical discussions.
Proven ability to lead a team through complex, multi\-month platform initiatives—driven from whiteboard to production, managing ambiguity and risk throughout.
Exposure to AI/ML infrastructure: model serving, inference pipelines, agentic solution or LLM integration patterns.
Experience setting and upholding engineering standards (code quality, on\-call practices, operational excellence) across a team.
Strong communication skills: able to translate between technical depth and business priorities for different audiences, with clear written documentation habits.
Bachelors degree in a computer science, engineering or similar discipline.
Must be able to work in the US without sponsorship now or in the future.
The Ideal Qualifications
Experience leading a team that built an internal developer platform (IDP) from scratch, with a product mindset that obsesses over internal developer experience.
Familiarity with AI safety, model evaluation, and governance frameworks.
Ability to lead with both people and technical credibility—influencing design decisions, driving alignment, and raising quality bar across the team.
Open\-source contributions to platform or ML infrastructure tooling.
Comfort with ambiguity: energized by undefined problem spaces, with a habit of building clarity and shared context where there isn’t any.
Advanced educational degree preferred.
What to Expect as Part of MassMutual and the Team
Regular meetings with the AI Platform Engineering team
Focused one\-on\-one meetings with your manager
Networking opportunities including access to Asian, Hispanic/Latinx, African American, women, LGBTQIA\+, veteran and disability\-focused Business Resource Groups
Access to learning content on Degreed and other informational platforms
Your ethics and integrity will be valued by a company with a strong and stable ethical business with industry leading pay and benefits
\#LI\-MC1
MassMutual is an equal employment opportunity employer. We welcome all persons to apply.
If you need an accommodation to complete the application process, please contact us and share the specifics of the assistance you need.
California residents: For detailed information about your rights under the California Consumer Privacy Act (CCPA), please visit our California Consumer Privacy Act Disclosures page.
MassMutual will accept applications on an ongoing basis until such time as a candidate has been offered employment. The job description includes the main duties of this position, which may evolve over time. You may be required to perform other duties not listed.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment.
Salary Range: $148,300\-$194,600
Award\-Winning Culture
MassMutual is guided by a single purpose: We help people secure their future and protect the ones they love. As a company operated for the benefit of our members and participating policyowners, we are defined by mutuality and our vision to provide financial well\-being for all Americans. It’s more than our company structure — it’s our way of life. We are a company of people protecting people. Our company exists because people are willing to share risk and resources and rely on each other when it counts.
We strive to build a thriving community where everyone is valued, included, and feels that they belong.
At MassMutual, we Live Mutual.
How We Work
MassMutual’s flexible workplace approach combines the importance of connecting in person and the flexibility of working remotely. Our hybrid model puts collaboration first with employees coming in at least three days per week to our spectacular campus settings and also enjoying the flexibility of remote Fridays, company\-wide remote weeks, and a bank of flexible remote weeks to use throughout the year.
Benefits for the whole you (and your loved ones)
There’s more to your life than your job and there’s more to your aspirations than a paycheck. We take a holistic view of compensation and benefits that provides the flexibility to create a healthy balance in your life for work, family, and community. We offer the benefits you’d expect, like medical, dental, 401(k), and generous vacation time, but we also offer ones you might not expect, like three paid days for volunteering, a $1,250 annual Well\-Being Wallet, and up to 320 hours of caregiver leave.
Explore some of our offerings below.
Paid Time Off
In addition to generous vacation time, paid holidays, and flexible holidays, MassMutual offers 'take care' time to care for yourself or someone you love—whether for physical illness or mental health.
Health \& Well\-Being
In addition to top\-line medical and dental coverage, personalized mental health solutions, on\-site and virtual health coaching, and much more, MassMutual reimburses employees up to $1,250 per year for eligible expenses supporting mental, physical, and financial well\-being.
Financial Well\-Being
In addition to competitive salaries and bonuses, educational assistance programs, and much more, MassMutual offers up to a 10% total 401(k) benefit, consisting of a 5% company match and a 5% annual contribution.
Taking Care
MassMutual offers generous maternity and parental leaves, as well as bereavement leave to mourn the loss of a loved one (and the employee defines 'loved one'). In addition, we offer up to 320 hours of caregiver leave to help employees support loved ones in times of need.
Giving Back
MassMutual offers three paid days for employees to volunteer with eligible nonprofits of their choice, and the MassMutual Foundation matches employee donation dollars to eligible nonprofit organizations up to $5,000 annually.
Commuter Benefits
MassMutual offers a Qualified Commuter Program through which eligible employees can pay qualified workplace commuting expenses with before\-tax dollars, as well as a commuter wallet option for employees based at Boston and NYC campuses.
Salary Context
This $148K-$194K range is below the median 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 MassMutual, 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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($171K) sits 20% below the category median. Disclosed range: $148K to $194K.
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.
MassMutual AI Hiring
MassMutual has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $194K - $194K.
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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