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About This Role
Overview
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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
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The Opportunity
MassMutual’s AI \& Data Science team is seeking an impact\-driven Lead AI Engineer to join our high\-performing, cross\-functional team. In this role, you will lead the design, deployment, and production scaling of advanced AI solutions that solve complex, high\-value problems across the enterprise. You’ll architect and deliver generative AI, agentic AI, and LLM\-based systems by applying rigorous scientific methods, writing high\-quality production code, and communicating results to senior leadership.
The Team
This is a unique opportunity to work alongside experts in applied AI, statistics, and computer science. The team operates at the intersection of cutting\-edge research and enterprise delivery, building AI solutions that shape the future of MassMutual and the life insurance industry at large. We partner closely with technology and business stakeholders across the organization, and we invest in growth through a culture of peer learning, candid feedback, and shared technical standards. This team is defined by a shared commitment to scientific and engineering excellence, meaningful work, and the kind of collaboration that makes challenging problems tractable.
The Impact
- Architect, build, and lead end\-to\-end AI solutions supporting a range of enterprise use cases—from ideation through production deployment and monitoring—using LLMs, agentic AI, machine learning, and probabilistic modeling, with accountability for reliability, performance, and maintainability.
- Design and conduct rigorous evaluations of AI system performance, including experimentation, benchmarking across foundation models, and quantitative analysis, to validate approaches and inform technical decisions.
- Drive innovation by identifying emerging technologies, translating cutting\-edge research into practical applications, and establishing team\-wide best practices in AI development and responsible AI deployment.
- Build rapid prototypes to test and validate AI approaches and deliver production\-grade AI\-powered applications (e.g., intelligent interfaces, dashboards, automated workflows) when solutions prove viable.
- Collaborate with engineering teams to build robust, production\-grade AI pipelines and APIs that integrate into the broader enterprise technology ecosystem.
- Influence senior leadership by aligning AI initiatives with enterprise strategy and communicating complex technical concepts and findings in clear, actionable terms.
- Mentor and develop junior talent, fostering a culture of technical excellence, scientific rigor, and continuous learning across the team.
The Minimum Qualifications
- 7\+ years of experience in data science, machine learning, or AI engineering, with a track record of delivering impactful AI/ML solutions at scale.
- Deep expertise in machine learning, statistics, NLP, and LLMs, including generative AI, agentic architectures, prompt engineering, and LLM evaluation across a variety of foundation models and benchmarks.
- Demonstrated ability to build, deploy, and scale production AI systems from architecture planning through orchestration, monitoring, and end\-user delivery.
- Strong programming skills in Python, with the ability to write clean, well\-tested, production\-quality code, including familiarity with Docker, Kubernetes, and other orchestration and deployment frameworks.
- M.S. or Ph.D. in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or a related quantitative field.
The Ideal Qualifications
- Familiarity with agentic AI tooling ecosystems, such as Bedrock AgentCore, AWS Strands, Azure, and MCP/A2A protocols.
- Experience developing and evaluating AI systems in a regulated industry, with a strong understanding of compliance and privacy standards.
- Breadth across AI and data science methods—including classical ML, causal inference, optimization, and Bayesian approaches—with comfort moving across techniques as problems demand.
- Proficiency in SQL and database design; familiarity with cloud\-native data platforms, vector databases, and semantic search.
- Exceptional ability to translate complex AI concepts and quantitative findings into clear insights for non\-technical stakeholders and senior leadership.
- Exceptional research credentials, such as published work, significant open\-source contributions, or a strong record of scientific rigor applied in industry.
What to Expect as Part of MassMutual and the Team
- Regular meetings with the AI \& Data Science 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\-MC
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: $172,000\-$225,700
Award\-Winning Culture
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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
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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)
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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 $172K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($198K) sits 10% above the category median. Disclosed range: $172K to $225K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
MassMutual AI Hiring
MassMutual has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Springfield, MA, US. Compensation range: $225K - $225K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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