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About This Role
Company:
Qualcomm Technologies, Inc. Job Area:
Interns Group, Interns Group > Interim Engineering Intern - SW General Summary:
About Qualcomm
Qualcomm relentlessly innovates transforming businesses, enriching lives and fortifying societies. Our solutions are pivotal in crafting a smarter, interconnected world. Building on our nearly 40-year history of era-defining technology breakthroughs, we deliver leading-edge AI, high performance, lower-power computing, and unrivaled connectivity. We’re proud to be the partner of choice to many of the world’s largest enterprises. Together with our ecosystem partners, we enable next-generation digital transformation. At Qualcomm, we are engineering human progress.
Join Us in Shaping the Future
At Qualcomm, we're committed to engineering human progress by delivering intelligent computing everywhere. We're passionate about harnessing the power of technology to transform industries and improve lives. Our early career programs offer limitless opportunities for growth, mentorship, and meaningful work. By joining our team, you'll be part of a community that's pushing the boundaries of what's possible in AI, computing, and connectivity. With a global footprint spanning over 30 countries, you'll have the chance to collaborate with diverse talent and contribute to projects that are shaping the future of technology.
Our early career programs are designed to attract inventive minds, offering a platform to contribute to our long-term innovation pipeline. You'll be part of a community that values collaboration, creativity, and continuous learning. Our programs provide real-world experience, technical skills, and professional development opportunities, empowering you to make a meaningful impact and achieve your career goals. Join us for our U.S. 2027 summer intern class !
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Role Overview
As a Machine Learning / AI Engineering Intern , you will help build best-in-class solutions and tools that enable state-of-the-art technologies for next-generation mobile and embedded machine learning platforms. These platforms power smartphones, autonomous vehicles, robotics, and IoT devices. You’ll work in a dynamic research environment as part of a multi-disciplinary team of researchers and software developers, collaborating with internal teams, working with popular neural network frameworks, and gaining exposure to Qualcomm’s SOC compute and ML hardware accelerators. Projects may also focus on AI systems infrastructure, distributed inference platforms, compiler technologies, runtime systems, GPU acceleration, model serving, performance optimization, and large-scale deployment of machine learning workloads. You will design, develop, and test software for machine learning tools and frameworks that make models smaller and more efficient for edge devices.
This internship supports the Machine Learning/ AI Enginee ring track . Based on your resume you will be aligned to projects within this track.
Minimum Qualifications
- Currently enrolled in a bachelor’s, master’s, or Ph.D. degree program in computer engineering, computer science, electrical engineering, or a related field
- Must be available for 11–14 weeks during Summer 2027 (May–September)
- Expected graduation date of Nove mber 2027 or later
- 1+ years of experience with programming languages such as C, C++, Python
Preferred Qualifications
- Currently enrolled in a Master’s or PhD degree program in computer engineering, computer science, electrical engineering, or a related field
- Candidates actively pursuing a degree with an anticipated graduation between Nove mber 2027 and June 2028
- Proficiency in deep neural networks, machine learning algorithms, and architectures (CNNs, RNNs, LSTMs)
- Experience with ML frameworks like TensorFlow, TFLite, PyTorch
- Skills in neural network programming, video/image processing, and application development
- Knowledge of compiler frameworks (LLVM, GCC, TVM, XLA) and parallel computing
- Experience with AI infrastructure, inference systems, distributed computing, runtime systems, GPU programming, CUDA, Kubernetes, MLIR, or performance optimization of machine learning workloads.
- Understanding of linear algebra operations and fast math libraries
- Theoretical knowledge of ML, deep learning, model compression, quantization, and optimization
- Experience with reinforcement learning, neural architecture search, kernel optimization, Bayesian optimization
- Familiarity with on-device training, transfer learning, personalization, federated learning, NLP, and ML security/privacy
- Experience with deep generative models, audio/speech processing, NLP, computer vision, and wireless communication
- Background in ML data pipelines, data management, backend/frontend applications
- Research excellence with publications in NeurIPS, CVPR, ICML, ICLR, ICCV
- Experience in object-oriented software design (OOSD)
Qualcomm’s Internship Program
Our interns join world-class teams to help define and build what is next for mobility and the world. You’ll be paired with a mentor with visibility to your project and team, receive support from your manager, and have access to the entire Qualcomm employee community to ensure your success. To round out your summer internship, we host professional development workshops, social events, cross-functional speaker sessions, and an executive speaker series. In addition to competitive hourly pay and accrued vacation time, you will receive relocation coverage and furnished housing accommodations for the duration of your internship.
Internship opportunities may be available in the following locations, depending on team and project needs : Austin, Boulder, Boxborough, Bridgewater, Chandler, Irvine, New York City, Louisville, Raleigh, San Diego, Santa Clara, and Seattle.
Application Process
By applying here, you are expressing interest in one of our many 2027 Qualcomm Machine Learning / AI Engineering internships . It is important to note that this is not a job posting for a specific project. We will review resumes on an ongoing basis, and a recruiter may reach out to you. Advancing candidates may be invited to complete a timed HackerRank challenge to evaluate their technical skill set.
Export Control Notice
This role may require an export license for candidates with citizenship in certain countries (see current country list below – subject to change). Qualcomm, like other companies, is experiencing significant delays in the export license process. Please be advised that if an export license is required, you may not be able to start work with Qualcomm until the license is granted, which may impact your ability to participate in the internship.
Countries requiring export : Armenia, Azerbaijan, Belarus, Burma (Myanmar), Cambodia, China, Cuba, Georgia, Hong Kong, Iran, Iraq, Kazakhstan, Kyrgyzstan, Laos, Libya, Macau, Moldova, Mongolia, Nicaragua, North Korea, Russia, Sudan, Syria, Tajikistan, Turkmenistan, Ukraine, Uzbekistan, Venezuela, Vietnam, and Yemen.
Applicants : 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 [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 :
$17.00 - $98.00 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 $35K-$203K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($119K) sits 44% below the category median. Disclosed range: $35K to $203K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Qualcomm AI Hiring
Qualcomm has 42 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, Research Scientist, AI Product Manager. Positions span San Diego, CA, US, Beijing, Beijing, China, Shanghai, Shanghai, China. Compensation range: $149K - $291K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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