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About This Role
Company:
Qualcomm Technologies, Inc. Job Area:
Engineering Group, Engineering Group > Machine Learning Engineering General Summary:
We are looking for experienced software engineers to help build and advance the software ecosystem for large-scale LLM inference on Qualcomm platforms.
In this role, you will work at the intersection of open-source LLM serving systems, distributed systems, cloud-native infrastructure, and AI accelerator software. You will contribute to open-source projects such as llm-d and related LLM inference engines, while developing the integrations and capabilities required to run these systems efficiently and reliably on Qualcomm AI accelerators.
Depending on your background and interests, you may focus more heavily on either distributed LLM serving and orchestration or inference runtime and accelerator integration, while collaborating across both areas.
This is an opportunity to work directly with leading open-source communities and help shape the next generation of scalable, production-grade LLM inference infrastructure.
Minimum Qualifications:
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. Responsibilities
- Contribute features, improvements, bug fixes, and architectural enhancements to open-source LLM inference projects, including llm-d and other open-source LLM inference and serving projects.
- Develop and improve support for Qualcomm AI accelerators in open-source LLM serving frameworks.
- Work with upstream maintainers and open-source communities to design, review, and upstream Qualcomm-related functionality.
- Develop and enhance inference engine integrations for Qualcomm platforms, including device abstraction, accelerator runtime integration, memory management, distributed execution, compilation, and performance optimization.
- Develop and improve llm-d integration and deployment capabilities for Qualcomm platforms, including reference architectures, deployment blueprints, configuration, and operational tooling.
- Design and implement distributed LLM inference capabilities such as: + Prefill/decode disaggregation + KV-cache management and transfer + Distributed communication and high-performance data movement + Request routing and load balancing + Tensor, pipeline, expert, and other forms of model parallelism + Multi-node and scale-out inference + Autoscaling and resource management + Failure detection, recovery, and service resiliency
- Integrate LLM inference software with Kubernetes and cloud-native infrastructure, including scheduling, autoscaling, networking, observability, and lifecycle management.
- Profile, benchmark, and optimize end-to-end inference performance, including time to first token (TTFT), inter-token latency (ITL), throughput, memory utilization, and scalability.
- Diagnose complex issues spanning model-serving frameworks, distributed runtimes, operating systems, networking, accelerator runtimes, and hardware.
- Collaborate closely with hardware, firmware, compiler, runtime, Kubernetes, and product teams to define requirements and deliver end-to-end solutions.
- Develop technical documentation, examples, reference deployments, and best practices to help customers and developers deploy LLM workloads on Qualcomm platforms.
Participate in architecture discussions and help influence the technical direction of Qualcomm's open-source LLM inference software strategy. *
Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience.
- Strong software engineering and debugging skills.
- Experience developing production-quality software in one or more of Python, C++, Go, or Rust.
- Strong understanding of at least one of the following areas: + Machine learning or LLM inference systems + Distributed systems + AI accelerator or GPU software + Cloud-native/Kubernetes infrastructure + ML compiler or runtime systems
- Experience working with complex software systems and debugging problems across multiple layers of the stack.
Ability to collaborate effectively across teams and communicate technical concepts clearly. *
Preferred Qualifications
Experience in one or more of the following areas is highly desirable:
- LLM inference engines, model-serving frameworks, or distributed inference systems.
- Architecture and implementation of high-performance LLM inference runtimes, including model execution, scheduling, memory management, attention, and KV-cache management.
- Distributed LLM inference architectures, including prefill/decode disaggregation and KV-cache transfer.
- llm-d, KServe, Kubernetes Gateway API, Kubernetes operators, or cloud-native AI infrastructure.
- PyTorch internals, torch.compile, Dynamo, Inductor, Triton, MLIR, or other compiler/runtime technologies.
- Hardware accelerator software stacks, GPU programming, or custom AI accelerator integration.
- RDMA, RoCE, GPU Direct-style communication, PCIe, high-speed networking, or distributed communication libraries.
- Model parallelism, including tensor parallelism, pipeline parallelism, expert parallelism, and data parallelism.
- Performance profiling and optimization of distributed AI workloads.
- Kubernetes scheduling, autoscaling, networking, observability, and production operations.
- Open-source development, including contributing patches, participating in design discussions, reviewing code, and collaborating with upstream maintainers.
- Experience working across hardware/software boundaries with hardware, firmware, compiler, runtime, and systems teams.
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).
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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 :
$162,000.00 - $243,000.00 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 $162K-$243K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $162K to $243K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Qualcomm AI Hiring
Qualcomm has 29 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span Santa Clara, CA, US, San Diego, CA, US, San Diego, CA. Compensation range: $149K - $291K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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