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About This Role
Company Description
WD is building the infrastructure behind the AI-driven data economy.
As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time. That’s where we come in.
We combine deep engineering expertise with global-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide.
This isn’t theoretical work. It’s real systems, at real scale, people solving some of the hardest challenges in technology today.
We’re looking for people who want to build, solve, and operate at that level.
Join us and let’s shape the future of data.
Job Description
Western Digital is seeking a Principal Full Stack Developer and Solutions Architect to design and build the AI, data analytics, and cloud solutions used by our Product Development engineering organization. This is a hands-on principal role: you will own the solution architecture, write the code, and design the interfaces engineers actually work in.
You will sit with product engineering teams, identify where AI, data, and cloud capability create real leverage, and take those use cases from prototype to production. Your scope spans the full solution — agentic and AI/ML services, the data and analytics layer beneath them, and the cloud platform they run on. You will set the design patterns, evaluation standards, and quality gates that the wider team builds against, and act as the senior technical counterpart to the AI Center of Excellence, Enterprise Data, and Engineering Cloud organizations. Success in this role is measured by engineering productivity gained, not only by systems deployed.
Essential Duties and Responsibilities:
Solution Architecture & Technical Leadership
- Own end-to-end solution architecture for Product Development use cases spanning AI/ML, data analytics, and cloud — from data source through to the engineer’s interface.
- Establish reference architectures, design patterns, and technology standards, and select the tools the organization will standardize on.
- Make build-vs-buy decisions and defend them to IT and business leadership.
- Ensure solutions align with enterprise security and data governance requirements by design.
- Provide design review and technical mentorship to the AI/Data Engineer and Platform/DevOps Engineer on the team.
- Ability and proven track record managing software developers
AI & Agentic Systems
- Design agent systems for engineering workflows: tool use, orchestration, multi-agent boundaries, and human-in-the-loop controls.
- Define evaluation methodology and quality gates — golden sets, regression suites, LLM-as-judge — so that no solution reaches an engineer’s desk unvalidated.
- Set model routing and guardrail policy through the enterprise LLM gateway.
- Determine where AI is the right answer and where a deterministic pipeline, analytics solution, or existing automation is the better engineering decision.
Data & Analytics
- Architect the data and retrieval layer that AI and analytics solutions depend on: ingestion, modeling, semantic layers, and access patterns across engineering and test data.
- Select and integrate data analytics and BI tooling, and enable self-service analytics for engineering teams rather than one-off reporting.
- Design pipelines and data products that land on Enterprise Data foundations rather than duplicating them.
- Ensure data quality, lineage, and permission propagation so that downstream AI and analytics outputs are trustworthy.
Cloud & Platform
- Architect solutions for AWS and containerized runtimes, balancing performance, reliability, and cost.
- Define deployment, infrastructure-as-code, and CI/CD patterns in partnership with the Platform/DevOps Engineer.
- Engineer for latency, inference cost, and cloud spend as first-class design constraints.
- Build on Engineering Cloud and AI CoE platform foundations rather than constructing parallel stacks.
Full Stack Development & User Experience
- Build applications end-to-end: backend services, orchestration logic, APIs, data access, and the user-facing surface.
- Own the user experience — how work is initiated, how progress is made visible, and how output is reviewed and corrected by the engineer.
- Decide embedded-versus-standalone delivery across Slack, Teams, IDE, BI, and web surfaces, and ship working pilot interfaces without waiting on a front-end team.
- Define the interaction patterns that ITES-US Engineering & Web Apps productionizes for scaled deployment.
Delivery & Enablement
- Partner directly with Product Development engineers to identify, scope, and co-develop solutions; work on-site alongside engineering teams on a regular cadence.
- Translate Product Development leadership priorities into a sequenced solution roadmap with measurable outcomes.
- Run skill-transfer sessions so that PD engineers can extend and maintain what is built.
Qualifications
- Bachelor’s degree in Computer Science, Engineering, Physics or a related field; advanced degree a plus.
- 8+ years of professional software engineering experience, including production ownership of systems you designed.
- Demonstrated solution architecture experience spanning more than one domain — application, data, cloud, or AI — with the ability to own a design end-to-end.
- Strong Python and SQL; proficiency in a modern front-end stack (React/TypeScript) or rapid UI frameworks (Streamlit, Gradio) sufficient to ship a usable internal tool independently.
- Demonstrated experience building LLM-based applications with orchestration frameworks (LangGraph, Langflow, or equivalent), including tool/function calling and MCP-based integrations, and practical experience with RAG architectures.
- Hands-on experience with data engineering and analytics tooling — pipeline orchestration (Airflow, dbt, or equivalent), warehouse or lakehouse platforms, and BI or visualization tools.
- Solid AWS experience, including containerized workloads (Kubernetes/EKS), infrastructure-as-code, and cost-aware architecture.
- Working knowledge of evaluation methodology for non-deterministic systems, with a track record of measuring quality rather than asserting it.
- Excellent written and verbal communication; able to present technical direction to director and VP audiences.
- Ability to work on-site in San Jose, CA, with regular embedded time alongside engineering teams.
Preferred Qualifications
- Prior delivery of an AI or analytics solution in production at meaningful scale, with adoption evidence.
- Experience in semiconductor, storage, or hardware product development environments, including engineering and test data.
- Familiarity with engineering toolchains — GitHub, Jira, Confluence, CI/CD systems, and test data platforms.
- Design-system literacy and prior ownership of a developer-facing product surface.
- Experience operating within an enterprise AI platform (LLM gateway, RAG-as-a-service, managed inference) and enterprise data governance frameworks.
Key Competencies
- Architect who ships — comfortable moving between whiteboard and codebase in the same day
- Breadth across AI, data, and cloud, with the judgment to know which one the problem actually calls for
- Product sense: judges success by engineer adoption, not by technical elegance
- Ability to influence without authority across IT and engineering organizations
- Comfortable with ambiguity in a newly formed team
- Collaborative and customer-centric mindset toward the engineers being served
What Success Looks Like
- Use cases move from prototype to production adoption within Product Development, with measurable engineering time returned.
- Documented reference architectures and evaluation standards exist across AI, data, and cloud, and are used by the wider team.
- Product Development engineers request the team’s involvement rather than being recruited into it.
- Solutions are built on enterprise foundations, with no parallel stacks created.
Additional Information
Western Digital is committed to providing equal opportunities to all applicants and employees and will not discriminate against any applicant or employee based on their race, color, ancestry, religion (including religious dress and grooming standards), sex (including pregnancy, childbirth or related medical conditions, breastfeeding or related medical conditions), gender (including a person’s gender identity, gender expression, and gender-related appearance and behavior, whether or not stereotypically associated with the person’s assigned sex at birth), age, national origin, sexual orientation, medical condition, marital status (including domestic partnership status), physical disability, mental disability, medical condition, genetic information, protected medical and family care leave, Civil Air Patrol status, military and veteran status, or other legally protected characteristics. We also prohibit harassment of any individual on any of the characteristics listed above. Our non-discrimination policy applies to all aspects of employment. We comply with the laws and regulations set forth in the "Know Your Rights: Workplace Discrimination is Illegal” poster. Our pay transparency policy is available here.
Western Digital thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.
Western Digital is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [email protected] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
Based on our experience, we anticipate that the application deadline will be 12/29/2026, although we reserve the right to close the application process sooner if we hire an applicant for this position before the application deadline. If we are not able to hire someone from this role before the application deadline, we will update this posting with a new anticipated application deadline.
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Compensation & Benefits Details
- An employee’s pay position within the salary range may be based on several factors including but not limited to (1) relevant education; qualifications; certifications; and experience; (2) skills, ability, knowledge of the job; (3) performance, contribution and results; (4) geographic location; (5) shift; (6) internal and external equity; and (7) business and organizational needs.
- The salary range is what we believe to be the range of possible compensation for this role at the time of this posting. We may ultimately pay more or less than the posted range and this range is only applicable for jobs to be performed in California, Colorado, New York or remote jobs that can be performed in California, Colorado and New York. This range may be modified in the future.
- If your position is non-exempt, you are eligible for overtime pay pursuant to company policy and applicable laws. You may also be eligible for shift differential pay, depending on the shift to which you are assigned.
- You will be eligible to be considered for bonuses under either WD’s Short Term Incentive Plan (“STI Plan”) or the Sales Incentive Plan (“SIP”) which provides incentive awards based on Company and individual performance, depending on your role and your performance. You may be eligible to participate in our annual Long-Term Incentive (LTI) program, which consists of restricted stock units (RSUs) or cash equivalents, pursuant to the terms of the LTI plan. Please note that not all roles are eligible to participate in the LTI program, and not all roles are eligible for equity under the LTI plan. RSU awards are also available to eligible new hires, subject to WD's Standard Terms and Conditions for Restricted Stock Unit Awards.
- We offer a comprehensive package of benefits including paid vacation time; paid sick leave; medical/dental/vision insurance; life, accident and disability insurance; tax-advantaged flexible spending and health savings accounts; employee assistance program; other voluntary benefit programs such as supplemental life and AD&D, legal plan, pet insurance, critical illness, accident and hospital indemnity; tuition reimbursement; transit; the Applause Program; employee stock purchase plan; and the WD Savings 401(k) Plan.
- Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
Notice To Candidates: Please be aware that WD and its subsidiaries will never request payment as a condition for applying for a position or receiving an offer of employment. Should you encounter any such requests, please report it immediately to WD Ethics Helpline or email [email protected].
Salary Context
This $157K-$210K 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 WD, 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 ($184K) sits 13% below the category median. Disclosed range: $157K to $210K.
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.
WD AI Hiring
WD has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US. Compensation range: $210K - $210K.
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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