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About This Role
Company:
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Qualcomm Technologies, Inc.
Job Area:
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Engineering Services Group, Engineering Services Group \> Program Management
General Summary:
GSOC (Global\-System\-On\-a\-Chip) organization is seeking a Technical Program Manager to drive the execution, governance, adoption, and operational readiness of Agentic AI capabilities across engineering workflows. This role will partner with engineering, cybersecurity, infrastructure, and platform teams to enable agentic execution workflows through AI platforms and Foundations . The role is responsible for driving Agentic AI enablement, secure management of AI artifacts, cross\-functional roadmap execution, platform adoption, qualification readiness, and alignment of GSOC and Qualcomm specific AI Foundations technology strategies to support scalable deployment of AI\-powered engineering workflows.
The successful candidate will lead cross\-functional programs that improve engineering productivity through AI\-enabled workflows, ensuring alignment across stakeholders, execution against shared roadmaps, adoption of new capabilities, and implementation of secure governance mechanisms for agentic AI assets and platforms. The role will serve as a key interface between GSOC, Qualcomm AI Foundations (internal organization/platform team responsible for enterprise AI infrastructure and enablement), Cybersecurity, GMRD (GSOC Machine Learning R\&D, an internal applied AI/ML engineering team), and engineering teams to help scale the next generation of AI\-enabled engineering solutions.
Key Responsibilities
Technical Program Leadership
- Lead execution of strategic Agentic AI initiatives across GSOC.
- Develop and maintain integrated program plans, roadmaps, milestones, dependencies, risks, and executive status reporting.
- Establish program governance, operating rhythms, execution metrics, and program review mechanisms to ensure successful delivery.
- Drive alignment across multiple organizations while balancing competing priorities and resource constraints.
- Facilitate technical reviews, roadmap discussions, decision\-making forums, and executive communications.
- Drive issue resolution, dependency management, and risk mitigation across participating teams.
Agent Development \& Qualification Tracking
- Track overall agent development activities to ensure alignment toward common system objectives and program milestones.
- Drive roadmap execution, milestone tracking, dependency management, and readiness reviews for agentic AI initiatives.
- Track and support benchmark creation and qualification activities for agent deployments.
- Define and monitor operational metrics, qualification readiness indicators, and program health measurements.
- Support continuous improvement of agent development processes through stakeholder feedback and data\-driven decision making.
Platform Adoption \& Enablement
- Track and drive adoption of Qualcomm AI platforms and related AI enablement platforms.
- Define and monitor adoption, usage, roadmap, qualification, and operational readiness metrics.
- Gather stakeholder feedback and partner with development teams to prioritize improvements and enhancements.
- Ensure visibility and awareness across engineering organizations regarding available AI capabilities, roadmap priorities, and deployment readiness.
CCI / Secure Asset Management
- Lead coordination of Company Confidential Information/Secure Asset Management initiatives supporting Agentic AI development. Agentic AI introduces new engineering artifacts including prompts, skills, rollouts, memories, and related AI assets that require appropriate classification, governance, and lifecycle management.
- Partner with GMRD, Cybersecurity, Qualcomm AI Foundations, and engineering stakeholders to define requirements and implement mechanisms that meet security, compliance, and governance requirements while achieving optimal agentic performance.
- Drive alignment of governance processes, asset management requirements, access controls, qualification criteria, and operational readiness activities associated with Agentic AI solutions.
- Track execution and closure of security, compliance, and governance\-related program milestones.
Qualcomm AI Foundations Tracking, Alignment \& GSOC Enablement
- Serve as the primary program management interface between GSOC and Qualcomm AI Foundations teams to ensure alignment of infrastructure, platform capabilities, and roadmap priorities.
- Manage and coordinate GSOC requests requiring support from Qualcomm AI Foundations, helping consolidate and prioritize requests originating from multiple stakeholder groups.
- Track and align Qualcomm AI Foundations and platforms, and GSOC technology roadmaps to ensure dependencies, deliverables, and readiness milestones remain synchronized.
- Drive visibility, communication, dependency management, and executive reporting related to AI platform enablement initiatives across participating organizations.
- Identify roadmap gaps, execution risks, and resource conflicts and facilitate resolution through appropriate stakeholder engagement and escalation.
- Support the shared roadmap required to enable agentic workflows across engineering domains and readiness for deployment into production engineering environments.
Cross\-Functional Coordination
- Coordinate activities across GSOC, GMRD, Qualcomm AI Foundations, Cybersecurity, infrastructure, and engineering organizations.
- Build strong partnerships with technical leaders, architects, development teams, and business stakeholders.
- Communicate program status, risks, decisions, and trade\-offs to both technical and executive audiences.
- Operate effectively in fast\-paced, highly technical, and evolving environments with multiple stakeholders and competing priorities.
Minimum Qualifications:
- Bachelor's degree in Engineering, Computer Science, or related field.
- 2\+ years of Program Management or related work experience.
Preferred Qualifications
- Master's degree in Computer Science, Engineering, Information Systems, Business Administration, or a related field.
- Software development, software engineering, platform engineering, AI/ML engineering, or technical product development background.
- Experience with Generative AI, Agentic AI systems, AI agents, copilots, LLM\-based solutions, workflow automation, RAG systems, or enterprise AI platforms.
- Experience working with AI platform deployment, adoption, governance, qualification, or operational readiness programs.
- Familiarity with cybersecurity, compliance, secure development practices, data governance, or access\-control frameworks.
- Experience building dashboards, KPIs, adoption metrics, roadmap metrics, and executive\-level program reporting.
- Experience driving large\-scale cross\-functional initiatives involving engineering, infrastructure, security, and business stakeholders.
- Familiarity with semiconductor development, silicon engineering, validation, emulation, design verification, engineering productivity platforms, or technical infrastructure programs.
- PMP, PgMP, Agile, Scrum, or equivalent program management certification.
Why Join GSOC
This role provides a unique opportunity to help shape how AI and agentic technologies are deployed across engineering organizations. You will work at the intersection of AI enablement, software platforms, governance, cybersecurity, and engineering productivity while helping define the operational framework for next\-generation engineering workflows.
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 :
$126,400\.00 \- $189,600\.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 $126K-$189K range is below the median 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($158K) sits 26% below the category median. Disclosed range: $126K to $189K.
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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