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About the job
The Sales AI Products Director leads the vision, strategy, and delivery of AI-powered products that transform Sodexo’s Sales operations. This role is accountable for building scalable, high-impact AI solutions that drive measurable business value, achieve strong product-market fit, and contribute directly to Sodexo’s P&L. As a key leader in Sodexo’s AI transformation, the role bridges business needs, data, and technology to accelerate innovation and embed AI at scale across the organization.
Accountable for managing people, setting direction and deploying resources; typically is responsible for performance evaluation, pay reviews and hire/fire decisions. Results are primarily achieved through the work of others and typically depend on the manager's ability to influence and negotiate with parts of the organization. Progression within Career Band reflects acquisition of broad technical expertise, business and industry knowledge, and process and people leadership capabilities. Accountable for business, functional or operational areas, processes or programs, which may include dotted-line or matrixed management.
Pay Range: $161585 per year - $209110 per year
Incentive Eligibility: Eligible to participate in Sodexo's incentive plan and earn variable compensation in addition to base salary
What’s in it for you
- Own and evolve the Sales AI product vision and multi-year roadmap in alignment with Sales leadership
- Identify high-value use cases by partnering with business and Sales stakeholders
- Ensure alignment with global and regional product factories to maximize reuse and scalability
- Prioritize initiatives based on business impact, feasibility, and strategic relevance
- Ensure successful deployment, adoption, and scaling of AI products across regions and business units
- Define and track clear success metrics (business impact, usage, ROI)
- Own budget management and financial tracking (including recharge models)
- Remove blockers and resolve critical issues impacting delivery or adoption
- Foster strong collaboration across product, engineering, data, and business teams
- Drive end-to-end product lifecycle: discovery, design, build, launch, and scale
What you’ll bring
- Strong business acumen with the ability to translate strategy into execution
- Deep understanding of product management practices, tools, and frameworks
- Solid grasp of data and AI ecosystems, architectures, and challenges
- Excellent communication skills, with the ability to engage both technical and non-technical stakeholders
- Demonstrated leadership and ability to influence across a global organization
Requirements
- Education Requirement: Bachelor's degree or equivalent experience
- Management Experience: 7 years
- Related Experience: 7 years
What we offer
Compensation is fair and equitable, partially determined by a candidate's education level or years of relevant experience. Salary offers are based on a candidate's specific criteria, like experience, skills, education, and training. Sodexo offers a comprehensive benefits package that may include:
- Medical, Dental, Vision Care and Wellness Programs
- 401(k) Plan with Matching Contributions
- Paid Time Off and Company Holidays
- Career Growth Opportunities and Tuition Reimbursement
More extensive information is provided to new employees upon hire.
Who we are
At Sodexo, our purpose is to create a better everyday for everyone and build a better life for all. We believe in improving the quality of life for those we serve and contributing to the economic, social, and environmental progress in the communities where we operate. Sodexo partners with clients to provide a truly memorable experience for both customers and employees alike. We do this by providing food service, catering, facilities management, and other integrated solutions worldwide.
Our company values you for you; you will be treated fairly and with respect, and you can be yourself. You will have your ideas count and your opinions heard because we can be a stronger team when you’re happy at work. This is why we embrace diversity and inclusion as core values, fostering an environment where all employees are valued and respected. We are committed to providing equal employment opportunities to individuals regardless of race, color, religion, national origin, age, sex, gender identity, pregnancy, disability, sexual orientation, military status, protected veteran status, or any other characteristic protected by applicable federal, state, or local law. If you need assistance with the application process, please complete this form.
Salary Context
This $161K-$209K range is above 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 Sodexo, 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. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $161K to $209K.
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.
Sodexo AI Hiring
Sodexo has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span North Bethesda, MD, US, Rockville, MD, US. Compensation range: $209K - $209K.
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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