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About This Role
- Corporate
- R369879
- Full time
- Not Remote
- 10850 Via Frontera, San Diego, CA 92127 United States
Want to help pets live their best lives?
We’re proud to be where the pets go and where the pet people go. If you want to make a real difference, create an exciting career path, feel welcome to be your whole self and nurture your wellbeing, Petco is the place for you.
Our core values capture that spirit as we work to improve lives by doing what’s right for pets and people.
- Pet First – Protect & Empower. All pets should Live their Best Life. We put the needs of pets and pet parents at the center of everything we do.
- Foster the Fun – Connect & Bond. Our Passion for pets brings us together! We celebrate the journey of pet parenthood through district experiences, products, and services.
- Let’s Go! Own & Commit. We are stronger as One Petco team. We bring our unique superpowers and champion authenticity in everyone to drive success.
About Petco: We’re proud to be "where the pets go" to find everything they need to live their best lives for more than 60 years — from their favorite meals and toys, to trusted supplies and expert support from people who get it, because we live it. We believe in the universal truths of pet parenthood — the boundless boops, missing slippers, late night zoomies and everything in between. And we’re here for it. Every tail wag, every vet visit, every step of the way. We are 29,000+ strong and together we nurture the pet-human bond in more than 1,500 Petco stores across the U.S., Mexico and Puerto Rico, 250+ Vetco Total Care hospitals, hundreds of preventive care clinics and eight distribution centers. In 1999, we founded Petco Love. Together, we support thousands of local animal welfare groups nationwide and have helped find homes for approximately 7 million animals through in-store adoption events.
Sr. Engineer, AI Platform Engineering
Job Summary
The Sr. Engineer, AI Platform Engineering will play a critical role in the design, architecture, and deployment of scalable AI infrastructure, foundation models, and generative AI solutions across Petco. This role requires deep expertise in AI platform engineering, model serving, vector databases, and LLMOps/MLOps pipelines. The Sr. Engineer will collaborate closely with cross-functional software engineering, product, and data teams to drive innovation, establish robust AI platform capabilities, and contribute to the strategic technology vision of the organization.
Key Responsibilities
AI Architecture & Solution Design
- Lead end-to-end AI architecture and solution design to build resilient, enterprise-grade platform infrastructure.
- Design and implement Generative AI capabilities, including LLMs, foundation models, RAG (Retrieval-Augmented Generation), and Agentic AI / AI agents.
- Architect efficient model serving and high-throughput inference systems optimized for real-time and batch workloads.
- Integrate embeddings, vector databases, and cloud AI/ML services into core engineering products.
MLOps, LLMOps & Infrastructure
- Establish and champion MLOps and LLMOps best practices across the engineering organization.
- Build automated CI/CD pipelines specifically tailored for AI/ML applications, ensuring seamless testing, deployment, evaluation, and monitoring.
- Design end-to-end AI platform pipelines emphasizing scalability, security, high availability, and performance.
Research & Platform Innovation
- Stay at the forefront of artificial intelligence technologies, open-source foundation models, and emerging platform tooling.
- Lead evaluation and proof-of-concept initiatives for cutting-edge generative AI frameworks, inference engines, and vector stores.
Cross-Functional Collaboration & AI Integration
- Partner with software engineers, system architects, and business stakeholders to drive seamless AI application integration into production ecosystems.
- Translate complex enterprise requirements into robust, reusable AI platform services and developer APIs.
Mentorship & Knowledge Sharing
- Provide technical leadership, architectural guidance, and mentorship to software and platform engineers.
- Maintain comprehensive documentation of AI architectures, platform interfaces, design patterns, and operational procedures.
- Foster a culture of technical excellence and contribute to internal technical knowledge repositories.
Continuous Improvement
- Continuously evaluate and optimize AI platform infrastructure, runtime performance, resource utilization, and reliability.
Qualifications and Requirements
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Software Engineering, or a related technical field.
- 5+ years of hands-on experience in software engineering, platform engineering, or AI engineering in production environments.
- Proven track record in end-to-end AI solution design, model serving, inference optimization, and AI application integration.
- Strong expertise with Generative AI concepts including LLMs, foundation models, RAG architectures, embeddings, and vector databases.
- Hands-on experience implementing MLOps / LLMOps frameworks and building robust CI/CD pipelines for AI/ML applications.
- Advanced programming proficiency in languages such as Python, Go, or Java.
- Demonstrated technical leadership and mentorship capabilities.
- Excellent problem-solving, architectural design, and communication skills.
Preferred Skills
- Deep hands-on experience with major cloud platforms and cloud AI/ML services (e.g., AWS, Azure, Google Cloud).
- Strong familiarity with containerization, microservices, and orchestration tools (e.g., Docker, Kubernetes, Ray).
- Experience with distributed systems and big data technologies.
- Strong foundation in modern software development methodologies, API design, and system reliability engineering (SRE).
#LI-CS1
#CORP
Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
The pay ranges outlined below are presented in accordance with state-specific regulations. These ranges may differ in other areas and could be subject to variation based on regulatory minimum wage requirements. Actual pay rates will depend on factors such as position, location, level of experience, and applicable state or local minimum wage laws. If the regulatory minimum wage exceeds the minimum indicated in the pay range below, the regulatory minimum wage will be the minimum rate applied.
Salary Range: $137,100.00 - $227,000.00
Hourly or Salary Range will be reflected above. For a more detailed overview of Petco Total Rewards, including health and financial benefits, 401K, incentives, and PTO - see https://careers.petco.com/us/en/key-benefits
Petco Animal Supplies, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or any other protected classification.
Salary Context
This $137K-$227K range is above the median 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 Petco, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($182K) sits 14% below the category median. Disclosed range: $137K to $227K.
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.
Petco AI Hiring
Petco has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $227K - $227K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 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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