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About This Role
Company:
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Qualcomm Technologies, Inc.
Job Area:
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Operations Group, Operations Group \> Product Management
General Summary:
This role requires full\-time onsite work in San Diego, CA (5 days per week).About the Team
The AI Platforms \& Systems Solutions team sits at the center of how next\-generation AI comes to life across on\-device and hybrid computing. We define the end\-to\-end hardware–software solutions that turn cutting\-edge AI/ML models, agentic experiences, and orchestration runtimes into real products people use every day—fast, efficient, and built to scale.
Our charter spans the full AI stack: from model enablement and inference optimization, to the agentic frameworks and orchestration engines that coordinate work across compute tiers, to the open protocols and specifications that make the broader ecosystem interoperable. We partner deeply with silicon, software, standards bodies, OEMs, and developers to shape not just individual products, but the standards the industry builds on.
This is a place for people who want to work at the frontier—where systems design, product strategy, and open standards meet. If you are energized by defining what comes next in AI computing and driving it from concept through adoption, you will feel at home here.
Job Description
The AI Platforms \& Systems Solutions team defines and delivers the end\-to\-end hardware–software solutions that power next\-generation on\-device and hybrid AI experiences, with a strong focus on the Windows AI ecosystem. As a Staff PDM, you will own the product strategy, definition, and lifecycle for AI platform products and specifications—spanning AI/ML model enablement, agentic systems, orchestration runtimes, and the open protocols and standards that connect them across local AI accelerators (CPU, GPU, NPU).
You will develop, define, and execute plans of record—including schedules, budgets, resources, deliverables, and risks—and direct a comprehensive product strategy from conception and definition through end of life. You will function as a central resource across systems, engineering, standards, quality, marketing, and partner teams as products and specifications move through their lifecycle.
This role requires deep fluency in the Windows edge\-AI stack and the ability to translate emerging model, agentic, and orchestration trends into a differentiated, defensible product and specification roadmap.
Technical Focus Areas
This role places strong emphasis on the following domains. Candidates should demonstrate depth in several and working fluency across all:
- AI / ML Models: model lifecycle, quantization and optimization, inference performance, and on\-device / hybrid deployment trade\-offs.
- Agentic Systems: planning, tool use, multi\-agent workflows, and the product requirements that make agentic experiences reliable and performant.
- Orchestration: task decomposition, routing, execution, and aggregation across compute tiers, including NPU\-first runtime strategies.
- Protocols \& Standards: authoring, evolving, and driving adoption of interoperability specifications and open standards across the ecosystem and standards bodies.
- Windows \& Edge AI Ecosystem: delivering AI products and services on the Windows ecosystem that leverage local AI accelerators (CPU, GPU, NPU) across the app\-to\-silicon stack.
- Product \& Specification Development: translating system solutions into shippable products and ratifiable specifications with clear KPIs, conformance criteria, and partner enablement.
Windows Ecosystem \& Edge AI Experience
This role is centered on the Windows AI ecosystem and on\-device inference. Candidates should demonstrate hands\-on depth across the following (Windows strongly preferred):
- Hands\-on experience delivering Windows ecosystem\-based AI products/services that utilize local AI accelerators (CPU, GPU, or NPU).
- Detailed understanding of AI\-related APIs/DDIs and SDKs for edge devices (Windows preferred; Android/Mac/Linux acceptable) and the flow of AI workloads from app\-to\- silicon—e .g., DirectX, WinML , OpenCL (OCL), and GGML.
- Understanding of AI middleware and frameworks such as llama.cpp, Ollama , and AnythingLLM , and familiarity with the AI application development flow.
- Understanding of LLM/VLM models in the \~1B–150B parameter range and the performance, power, latency, memory\-footprint, and security trade\-offs of running them on edge devices; familiarity with the Qwen, Llama, Gemma, and gpt\-oss model families preferred.
Principal Duties and Responsibilities
- Identifies gaps and opportunities based on complex analysis of market, customer, technology, and ecosystem demands across the AI/ML, agentic, and orchestration landscape, and uses this insight to guide solution and design conversations.
- Leads the creation and validation of business cases for new and complex AI platform products and specifications, ensuring alignment with the differentiated product roadmap and business direction.
- Owns product definition for Windows\-based AI/ML model enablement, agentic frameworks, orchestration runtimes, and associated protocols—including scope, requirements, cost/impact analysis, and success KPIs.
- Drives authoring and evolution of technical specifications and standards, and partners with standards bodies, consortia, and ecosystem partners to accelerate ratification and adoption.
- Prepares and delivers highly complex technical presentations on product and specification roadmaps to customers, partners, and senior leadership.
- Markets new and complex products and technologies through technical marketing channels— conferences, developer events, and direct customer engagement—and drives launch and adoption efforts.
- Translates customer feedback, competitive intelligence, and the external AI environment into roadmap changes and communicates these clearly to senior leaders.
- Directs and oversees development of highly complex products and new product areas independently, maintaining communication across systems, engineering, and cross\-functional teams.
- Ensures successful cross\-functional collaboration by confirming that agreed KPIs, conformance criteria, and specifications are met upon delivery by engineering teams.
- Holds self and teams accountable for staying current on competitors, model and agentic advances, and the broader Windows AI ecosystem, and leads design innovation to articulate product differentiation.
- Collaborates with key stakeholders and program sponsors to develop product goals (performance, cost, timeline, customer schedules), assess feasibility, and facilitate cross\-functional decision making.
Level of Responsibility
- Works independently with little supervision, making decisions that are significant in impact where errors may not be readily apparent due to complexity and typically require significant time and resources to correct.
- Uses verbal and written communication skills to convey complex, detailed information to multiple audiences with differing knowledge levels; may require strong negotiation, influence, and communication to large groups or high\-level constituents.
- Has a moderate\-to\-high amount of influence over key organizational decisions and is consulted by senior leadership on strategic direction.
- Completes multi\-step, cognitively complex tasks that can be performed in various orders and require managing information across short\- and long\-term horizons.
- Exercises exceptional creativity to innovate new products, specifications, and processes without established objectives or known parameters.
- Applies deductive and inductive problem solving where information is often missing or conflicting; advanced data analysis and interpretation skills are required.
- Occasionally participates in strategic planning within own area affecting immediate operations.
Required Competencies
*(All competencies below are required upon entry.)*
- Analytical Skills — gathers, integrates , and interprets information from multiple sources to identify fundamental patterns and trends.
- Building Trusting Relationships — builds collaborative rapport with diverse people and businesses, delivers on commitments, and maintains confidentiality.
- Communication — conveys information clearly and accurately using a technically sound style, choosing the most effective delivery method.
- Creating the New and Different — produces breakthrough ideas, manages innovation, and translates new concepts into shippable solutions and specifications.
- Decision Making — makes quick, accurate decisions, weighing alternatives and the impact on people and resources.
- Getting Work Done — organized, resourceful, and planful; leverages multiple resources, manages parallel tasks, and plans around obstacles.
- Mentoring and Coaching — develops, coaches, and mentors associates through development experiences and networking opportunities.
- Product Management — directs a comprehensive product strategy from conception through end of life, functioning as a central cross\-functional resource.
Preferred Qualifications
- 10\+ years of Product Management or related technical work experience.
- Advanced degree (MS or MBA) in a technical or business discipline preferred.
- Direct experience defining or shipping Windows\-ecosystem AI/ML platforms, inference runtimes, agentic frameworks, or orchestration systems on local accelerators (CPU, GPU, NPU).
- Detailed understanding of edge AI APIs/DDIs and SDKs (Windows preferred; Android/Mac/Linux acceptable) and the app\-to\-silicon workload flow (DirectX, WinML , OpenCL, GGML).
- Familiarity with AI middleware/frameworks (llama.cpp, Ollama , AnythingLLM ) and the AI app development flow.
- Working knowledge of LLM/VLM models in the \~1B–150B range and their edge trade\-offs (perf, power, latency, memory, security); Qwen, Llama, Gemma, gpt\-oss families preferred.
- Experience authoring or contributing to technical specifications, protocols, or industry standards (e.g., via consortia or standards bodies).
- 3\+ years working in a large, matrixed organization.
- 2\+ years working with operating budgets and/or project financials.
- 2\+ years negotiating third\-party business agreements or partnerships.
The Responsibilities of This Role Do Not Include
- Direct financial accountability (e.g., budgeting ownership).
Minimum Qualifications:
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related technical field.
- 5\+ years of Product Management or related work experience.
- Completed advanced degrees in a relevant field may be substituted for up to two years (Master’s \= one year, Doctorate \= two years) of work experience.
Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e\-mail disability\[email protected] or call Qualcomm's toll\-free number found here . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
To all Staffing and Recruiting Agencies : Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
EEO Employer: Qualcomm 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, Veteran status, or any other protected classification.
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
Pay range and Other Compensation \& Benefits :
$179,200\.00 \- $268,800\.00
The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales\-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link .
If you would like more information about this role, please contact Qualcomm Careers .
Salary Context
This $179K-$268K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Qualcomm, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $179K to $268K.
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.
Qualcomm AI Hiring
Qualcomm has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span San Diego, CA, US, Raleigh, NC, US, New York, NY, US. Compensation range: $141K - $301K.
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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