Interested in this AI/ML Engineer role at The Hershey Company?
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Why Hershey:
At The Hershey Company, our culture is rooted in purpose and inspired by Milton Hershey’s enduring vision that business should create opportunity for others. Through our commitment to corporate philanthropy, we create positive impact by supporting communities, helping children thrive, and operating as a business for good. This includes our deep and long standing partnership with Milton Hershey School, where we invest in education, workforce readiness, and hands on engagement to help students succeed beyond the classroom. Supported by strategic grantmaking, product donations, cross brand collaboration, and strong employee volunteerism, Hershey offers a place to work where making a difference—locally and globally—is part of how we do business every day.
About The Hershey Company
The Hershey Company is an industry-leading snacks company known for making more moments of goodness through its iconic brands. With a strong portfolio across chocolate, confectionery, and salty snacks, Hershey delights consumers every day. Our beloved brands include Hershey’s, Reese’s, KitKat®, Dots Pretzels, Lesser Evil, Jolly Rancher, Twizzlers, Ice Breakers, and more.
At Hershey, we are committed to making a meaningful difference in the communities where we live, work, and do business. This commitment originated with our founder, Milton Hershey, and continues to shape who we are today.
Guided by our employee value proposition, Lead Today. Create Tomorrow, we empower our people to drive impact now while shaping what comes next. Our One Hershey mindset brings teams together across functions, geographies, and businesses, enabling us to operate with speed, collaboration, and shared purpose.
The Hershey Company is proud to be recognized as a top employer in the United States, reflecting our commitment to our people, culture, and purpose. How will you fit in the big picture?
The Sr. Manager, AI Business Modernization will accelerate the U.S. business AI agenda by translating strategic priorities into a focused portfolio of AI products, agentic solutions, and digitally enabled capabilities that drive productivity, improve decision-making, and create measurable business value. Reporting to the Chief of Staff to the President, U.S., this role serves as the critical bridge between business strategy and enterprise AI capabilities, helping shape how AI transforms ways of working across the organization.What will you do?
- 1. Own the U.S. functional AI portfolio and roadmap, ensuring priorities align with business strategy, enterprise AI standards, and measurable value creation.
- 2. Serve as the business lead for AI priorities, balancing opportunity, feasibility, risk, and business impact.Identify, prioritize, and scale AI products, agentic solutions, and AI-enabled capabilities that improve commercial performance and productivity.
- 3. Partner with enterprise AI, IS, data, security, legal, and governance teams to deliver solutions aligned with enterprise standards.
- 4. Establish success metrics, adoption measures, and value targets for AI initiatives, tracking performance and business outcomes.
- 5. Partner with Finance and business leaders to quantify productivity improvements, financial impact, and realized value.
- 6. Evaluate portfolio performance and recommend opportunities to improve, accelerate, retire, or redesign solutions based on adoption and value.
- 7. Drive change management and AI adoption by helping leaders translate AI into new ways of working and business decision-making.
- 8. Build AI fluency through communications, training resources, practical use cases, and responsible AI education.
- 9. Create mechanisms to gather employee feedback, identify new opportunities, and surface adoption barriers and AI risks.
- 10. Develop repeatable playbooks, standards, and practices that enable scalable AI adoption across the U.S. business.
Minimum Education & Requirements
Experience:
- 7+ years of progressive experience in strategy, business transformation, product management, digital, analytics, technology-enabled growth, or enterprise capability building.
Education:
- Bachelor's degree required.
- Advanced degree in Business, Strategy, Analytics, Technology, or a related field preferred.
Benefits and Perks
At Hershey, we offer competitive pay and a comprehensive total rewards package designed to support your wellbeing, financial security, and life outside of work.
- Health & Wellbeing: Medical, dental, and vision coverage, plus wellness programs that support your physical and mental health
- Financial Security: Competitive pay, annual incentive opportunities, and a 401(k) with company match
- Time Off & Flexibility: Paid time off, company holidays, and flexible ways of working where applicable
- Growth & Development: Career development programs, learning opportunities, and internal mobility
Benefits may vary based on role, location, and eligibility.
The anticipated base salary range for this role is $143,120 to $178,900 per year. The actual base salary offered will depend on a variety of job‑related factors, including, but not limited to, experience, skill set, education, and training. In addition to a market-competitive base salary, our Total Rewards package includes performance‑based incentives and a comprehensive, competitive benefits package designed to support overall physical, mental, and financial well‑being (as applicable). Learn more about Hershey’s benefits and all the resources Hershey provides to support our employees’ physical, emotional, and financial well‑being by visiting the Hershey Digital Employee Experience at www.askHRbenefits.hersheys.com
The Hershey Company is an Equal Opportunity Employer. The policy of The Hershey Company is to extend opportunities to qualified applicants and employees on an equal basis regardless of an individual's race, color, gender, age, national origin, religion, citizenship status, marital status, sexual orientation, gender identity, transgender status, physical or mental disability, protected veteran status, genetic information, pregnancy, or any other categories protected by applicable federal, state or local laws.
The Hershey Company is an Equal Opportunity Employer - Minority/Female/Disabled/Protected Veterans. You may request a reasonable accommodation if you are unable or limited in your ability to use or access our online application process as a result of a disability.
You can request an accommodation via phone or email.To request an accommodation via phone, please call +1 877-804-1794 and leave a voicemail with your contact information. You may also email a request for accommodation to [email protected]. Please be sure to include “Accommodation Needed” in the subject line. This will ensure that your email is routed to the appropriate contact who will handle your request.
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Salary Context
This $143K-$178K range is below 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 The Hershey Company, 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($161K) sits 24% below the category median. Disclosed range: $143K to $178K.
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.
The Hershey Company AI Hiring
The Hershey Company has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Hershey, PA, US, New York, NY. Compensation range: $134K - $178K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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