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About This Role
*Have you ever enjoyed Arnold®, Brownberry® or Oroweat® bread? A Thomas’® English muffin or bagel? Or perhaps snacked on a Sara Lee®, Entenmann’s® or Marinela® cake or donut? If the answer is yes, then you know Bimbo Bakeries USA!**More than 20,000 associates in bakeries, sales centers, offices and on sales routes work to ensure our consumers have the freshest products at every meal. In addition to competitive pay and benefits, we* *provide a safe and inclusive work environment that appreciates diversity, promotes development and allows our associates to be their authentic selves.*
\#LI\-KM1Come join the largest baking company in the world and our family of 20,000 associates nationwide!
Top Reasons to Work at Bimbo Bakeries USA:
Salary Range: $52,000 \- $67,600
Annual Bonus Eligibility
Comprehensive Benefits Package
Paid Time Off
401k \& Company Match
Position Summary:
The Warehouse System Support will support the automated DC and support all functions related to Oracle’s Cloud\-based Warehouse Management System (WMS). This position will work with all members of the team on supporting the day to day needs of the WMS system in the DC. This position will become an expert of the system to train key users and provide guidance on system configuration, process and layout design. The Warehouse System Support will act as the liaison from the operations team and the IT Helpdesk.
Key Job Responsibilities:
- Fulfill role of WMS knowledge expert for warehouse management system from requirements gathering/analysis through daily operations.
- Run waves to support flow of the building.
- Perform inventory counts and reconciliation processes.
- Troubleshoot day to day issues.
- Participate in integration meetings with technical teams to help determine specifications relative to integration mapping and product configuration.
- Assist in roll out of test plans, data, and cycles to ensure that all requirements identified in the requirements gathering phase will be validated.
- Provide guidance, and training/knowledge transfer to a team ensuring stability of the DC during and after initial deployment.
- Participate in on\-site implementation and customer interaction to ensure delivery of business requirements.
- Ensure that appropriate tools/processes are leveraged, and effective communication and support is provided throughout the process.
- Document any new applicable functional requirements as necessary for enhancements to core products to meet scope of proposed solution.
- Create and update training materials as needed.
- Train and mentor team associates on solutions and best practice of implementations and continuous improvement.
- Help define, monitor and analyze WMS Key Performance Indicators (KPIs).
- Other duties as assigned.
Education and Work History:
- Associates degree or IT background required, Bachelor’s Degree in Supply Chain Management (SCM), Warehousing, Logistics, Business or related field preferred.
- Five (5\) or more years of experience in warehousing operations or warehousing systems implementation.
- Two (2\) to Four (4\) years’ experience utilizing a major WMS platform; Oracle preferred.
- GUI or Web Design configuration experience is a plus.
- Exposure and knowledge of all facets of Supply Chain operations.
- Knowledge of FDA and OSHA regulations is helpful.
SPECIALIZED SKILLS AND KNOWLEDGE
- Well organized and able to manage multiple projects and priorities.
- Excellent computer skills.
- Ability to work effectively with multifunctional teams.
- Strong Communication skills (written \& verbal).
- Travel may be required for other sites.
- Ability to work nights and weekends as needed.
The physical and mental demands described in each job posting are representative of those that must be met by an associate to successfully perform the essential functions of each job. Reasonable accommodations may be requested to enable qualified individuals with disabilities to perform the essential functions of each job.
Bimbo Bakeries USA is an equal opportunity employer with a policy that provides equal employment opportunity for applicants and employees regardless of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, any other classification protected by law.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Salary Context
This $52K-$67K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Grupo Bimbo, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($59K) sits 64% below the category median. Disclosed range: $52K to $67K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Grupo Bimbo AI Hiring
Grupo Bimbo has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Orangeburg, SC, US. Compensation range: $67K - $67K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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