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Job Description:
Title: Brand Insights AI Innovation Manager
Location: Chicago, IL or London
Workstyle: Onsite
Mars does not sponsor visas for this role. This position is not eligible for relocation benefits
The Mars corporate brand is on a journey to elevate its reputation by growing brand awareness and building trust through storytelling. Global Corporate Affairs is seeking to step-change reputational impact through the addition of stronger insights, data, and analytics foundations, that will be further accelerated through the application and adoption of AI. This assignment is the first dedicated internal role with this priority and focus.
This role will be part of the central Corporate Affairs global team. While it will report to the Corporate Brand and Purpose VP, the capabilities and deliverables of this role will serve the needs across CA pillars- brand, external communications, internal communications, and public affairs.
Additionally, the AI capabilities aspect of this role will serve as a CoE within the Corporate Affairs global function, strongly collaborating with the Corporate Affairs capabilities sponsor and Data Technology
What will be your responsibilities?
- Target Audience Definition: Develop target audience profiles across the Mars corporate brand stakeholder ecosystem- including Consumers, Associates, Government/Policy Makers, and Media/Industry Influencers; Create audience definitions and illuminate insights, needs, and opportunities
- Trust Reputation Performance: Establish a quantitative framework for evaluating trust across the Mars corporate brand stakeholder ecosystem (Consumers, Associate, Government/Policy Makers, Customer, etc.); Establish a model to evaluate reputational impact attribution; Provide ongoing support for brand reputation updates with senior leadership
- Consumer Learning Plan: Partner with CA team to define and execute annual learning plan for foundational knowledge and key campaigns / initiatives; Manage learning plan budget$500K
- AI Data Analytics: Identify opportunities to accelerate and adopt AI tools and workflows to drive data and analytics, including AI enabled solutions / innovation and opportunities for efficiencies and cost savings
- AI Capability Center of Excellence (CoE): Serve as the AI CoE for global Corporate Affairs.
o Identify AI opportunities across the global CA function
o Build AI capability/upskilling across the CA community
o Drive adoption of AI tools and workflows within everyday communications activities
o Identify opportunities to improve operational efficiency
o Advise on AI-enabled solutions that support CA teams and strategic priorities
What are we looking for?
- BS/BA degree
- 5-10 years of experience in consumer insights
- Experience across a range of FMCG categories/brands
- Corporate brand experience (preferred)
What can you expect from Mars?
- Work with over 130,000 diverse and talented Associates, all guided by the Five Principles.
- Join a purpose driven company, where we’re striving to build the world we want tomorrow, today.
- Best-in-class learning and development support from day one, including access to our in-house Mars University.
- An industry competitive salary and benefits package, including company bonus.
#RW-Onsite
#LI-RW1
The base pay range for this position at commencement of employment is between the range listed below, however, base pay offered may vary depending on multiple individualized factors, including but not limited to job-related knowledge, skills, and experience. The total compensation package for this position will include variable pay, medical and dental benefits, participation in 401k plan, and paid time off benefits. Details of participation in these benefit plans will be provided if an applicant receives an offer of employment.
The pay range shown is applicable to all US locations listed within the job posting: USD 108,160.00 - USD 148,720.00
Salary Context
This $108K-$148K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Mars, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($128K) sits 39% below the category median. Disclosed range: $108K to $148K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Mars AI Hiring
Mars has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $148K - $148K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% below the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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