Interested in this AI/ML Engineer role at CEVA Logistics?
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588553
Alameda. Ca, US
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Global AI \& Innovation Senior Specialist (Ground \& Rail)
CEVA Logistics provides global supply chain solutions to connect people, products, and providers all around the world. Present in 170\+ countries and with more than 110,000 employees spread over 1,500 sites, we are proud to be a Top 5 global 3PL.
We believe that our employees are the key to our success. We want to engage and empower our diverse, global team to co\-create value with our customers through our solutions in contract logistics and air, ocean, ground, and finished vehicle transport. That is why CEVA Logistics offers a dynamic and exceptional work environment that fosters personal growth, innovation, and continuous improvement.
At CEVA Logistics, we Rise in Motion. Your career is always on the move, growing as fast and as far as your ambition takes you. Join a global team of nearly 200 nationalities, shaping the future of global trade, moving essential goods, forging new paths, and pushing boundaries to serve an ever\-changing world. The pace is fast, the challenges are real, but the rewards are greater: growth, purpose, and the chance to make a meaningful impact. This is more than a job. It’s a journey on which you rise.
YOUR ROLE
Instrumental part of CEVA Logistics Ground \& Rail product Transformation Roadmap, the AI \& Innovation BPE Specialist support, within the global organization, the deployment, execution, and scaling of AI use cases within Ground \& Rail local \& regional scope.
The role supports the delivery of Global \& Regional AI initiatives, and ensures effective delivery, adoption by the Ground organization of said initiatives while translating business needs into measurable value \&
ensuring expected ROI impacts.
WHAT ARE YOU GOING TO DO ?
Support delivery of Global AI initiative within the Ground region \& local scope
Work within the Global BPE team, support Global Innovation product manager, data teams, IT, Innovation, and business stakeholders to ensure seamless delivery
Facilitate and coordinate PoCs, MVPs, pilots, and scaling activities ensuring timely and value\-driven outcomes for AI use\-cases to support the Innovation roadmap
Lead or support the end\-to\-end execution of AI use cases identified by the region, using structured governance frameworks (ideation framing build test deploy).
Drive adoption of AI solutions through training, communication, and feedback loops
Monitor performance of deployed AI solutions and escalate issues for resolution
Act as a connector between SMEs, analysts, engineers, and IT; facilitate workshops
Track KPIs, benefits, cost savings, and user satisfaction; report to leadership
WHAT ARE WE LOOKING FOR?
Bachelor’s or Master’s in Business, Engineering, Data, or related field
3\+ years of experience in Analytics, operations, digital transformation, or technical project management
Project Management
Understanding of AI/ML concepts (NLP, predictive models, LLMs)
Ability to run PoCs, MVPs, agile iterations
Problem\-solving applied to project deployment
Detail\-oriented with ability to manage timelines and risks
Fluency in English
Desirable
Certifications in AI, Data, Agile or Project Management
Experience in Transport, Logistics related industry
Delivered process improvement project
Knowledge of AI governance
Capacity to Translate business needs into AI requirements
Knowledge of Process Improvement method (Lean Six sigma certification or exposure)
Process mapping experience and identifying inefficiencies
WHAT DO WE HAVE TO OFFER?
With a genuine culture of reward and recognition, we want our employees to grow, develop and be part of our journey. There’s no doubt that you will be compensated for your hard work and commitment so if you’d like to work for one of the top Logistics providers in the world then please do get in touch to find your next role.
ABOUT TOMORROW
We value your professional and personal growth. That’s why we share plenty of career opportunities for you to thrive within CEVA. Join CEVA for a challenging career.
CEVA Logistics is proud to be an equal opportunity work place and an affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status or any other characteristic. We are an Equal Opportunity Employer of Minorities, Females, Protected Veterans, and Individual with Disabilities.
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Nearest Major Market: San Francisco
Nearest Secondary Market: Oakland
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CEVA Logistics, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
CEVA Logistics AI Hiring
CEVA Logistics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alameda, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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