Interested in this AI/ML Engineer role at Qualcomm?
Apply Now →About This Role
Company:
------------
Qualcomm Technologies, Inc.
Job Area:
-------------
Operations Group, Operations Group \> Product Management
General Summary:
Join Qualcomm's Data Center Business Unit to lead the strategy, definition, and execution of custom silicon and chiplet\-based platforms for AI and cloud infrastructure customers. This role serves as the business and product leader for complex custom silicon engagements, driving customer requirements, product strategy, commercial success, and cross\-functional execution from concept through production ramp.
Working closely with customers and Qualcomm's architecture, engineering, operations, and business organizations, you will helfp define next\-generation AI, compute, connectivity, memory, chiplet, and advanced packaging solutions that enable differentiated cloud infrastructure at scale.
Product Strategy \& Customer Engagement
- Define product vision, roadmap, use cases, success metrics, and market positioning for custom AI, compute, and chiplet\-based silicon platforms.
- Partner directly with customers to understand workload requirements and translate business objectives into product requirements and platform roadmaps.
- Lead product and system architecture discussions with customers and internal technical teams, balancing performance, power, cost, schedule, scalability, and business objectives.
- Drive platform strategy across custom silicon and heterogeneous integration technologies, enabling reusable solutions that can be leveraged across multiple customer engagements.
- Build multi\-generation product, chiplet, and platform roadmaps that maximize technology reuse, accelerate time\-to\-market, and support long\-term customer adoption.
- Collaborate with architecture and engineering teams on system\-level design and technology tradeoffs across silicon, packaging, interconnect, memory, and platform integration.
Program Leadership \& Execution
- Own product lifecycle execution from opportunity qualification through specification, development, silicon bring\-up, production ramp, and lifecycle management.
- Drive cross\-functional alignment between architecture, design engineering, software, systems, operations, quality, finance, legal, and sales.
- Partner with PMO on executive reviews, milestone tracking, risk management, escalation resolution, and customer communication throughout program execution.
- Establish measurable KPIs for schedule, cost, quality, customer satisfaction, and business performance.
Business \& Commercial Leadership
- Develop business cases, investment proposals, NRE models, pricing strategies, and profitability analysis for custom silicon engagements.
- Partner with sales and finance teams on customer negotiations, contracts, statements of work, change orders, and revenue planning.
- Drive customer growth strategies, follow\-on engagements, and long\-term platform adoption across strategic hyperscaler accounts.
Manufacturing \& Supply Chain
- Partner with Manufacturing and Supply Chain to ensure manufacturing readiness and production success.
- Drive supply assurance, capacity planning, cost optimization, yield improvement, and risk mitigation strategies.
- Ensure successful transition from engineering development into high\-volume production.
This is an office based position located in Santa Clara, CA and is expected to comply with the company's onsite work policy.
Minimum Qualifications:
- Bachelor's degree in Engineering, Information Systems, Computer Science, Humanities, or related field.
- 8\+ years of Product Management or related work experience.
Preferred Qualifications:
8\+ years of product management, business leadership, or program leadership experience within semiconductors, custom silicon, AI infrastructure, compute, networking, or data center markets.
- Proven track record leading customer engagements and complex custom silicon programs.
- Deep understanding of semiconductor development lifecycles from specification through high\-volume manufacturing.
- Experience with advanced packaging, chiplets, memory systems, high\-speed interconnects, and AI accelerator architectures.
- Strong commercial and financial acumen, including pricing strategies, investment planning, revenue forecasting, and business case development.
- Experience presenting to senior executives, including customer CTOs, VPs, and C\-level leadership.
- Demonstrated ability to lead large cross\-functional organizations without direct authority.
- Strong understanding of supply chain, manufacturing operations, capacity planning, and production ramp execution.
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\-accomodations@qualcomm.com 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 :
$229,800\.00 \- $344,800\.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 $229K-$344K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($287K) sits 31% above the category median. Disclosed range: $229K to $344K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Qualcomm AI Hiring
Qualcomm has 12 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span San Diego, CA, US, Austin, TX, US, Santa Clara, CA, US. Compensation range: $184K - $375K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.