# Systems Engineer, Data Center AI
$99,500 - $149,300 USD yearly
Originally posted 22 September 2026 by the employer.
Focus on best-in-class Qualcomm AI inference accelerators for data center and hybrid AI applications.
About the role
This role involves researching, developing, optimizing, and validating software, hardware, architecture, algorithms, and machine learning solutions for deploying AI datacenter technology. You will innovate and develop products and solutions around Qualcomm AI inference accelerators for data center and hybrid AI applications.
What you'll do
- Develop AI/ML solutions that integrate Qualcomm AI hardware products, technologies, software, and the ecosystem to achieve best-in-class AI inference performance, power efficiency, and scalability.
- Assist in the design, development, implementation, and deployment of Gen AI and LLM applications.
- Contribute to implementing fine-tuning and distillation techniques.
- Research, design, develop, simulate, and/or validate systems-level software, AI hardware, architecture, deep learning algorithms, and AI solutions, ensuring system-level requirements are met.
- Perform AI model benchmarking and functional analysis to establish requirements and specifications.
- Propose deployment strategies with AI model/workload optimization and deployment.
What you'll need
- Bachelor's degree in Engineering, Information Systems, Computer Science, or a related field.
- Strong proficiency in Python and ML frameworks.
- Deep understanding of ML development, deployment, and applications.
- Deep understanding of system performance profiling and parallel computing.
Nice to have
- Master's or PhD Degree in Engineering, Computer Science, Physics, or a related field.
- Good understanding of GenAI architectures from transformers, diffusion, hybrid - LLMs, LVMs, embeddings.
- Working experience with fine-tuning GenAI models and Reinforcement Learning.
- Background in compiler optimizations for ML workloads.
- Experience with architectural Patterns for Large-Scale AI Systems: Knowledge of microservices and distributed systems.
- Optimize inference performance across heterogenous nodes CPUs, GPUs, and specialized accelerators.
- Experience in implementing caching, batching, and parallelization strategies for high-throughput systems.
- Familiarity with hardware acceleration.
- Proficiency with version control systems (Git) and code review tools (Gerrit, GitHub, GitLab) and collaborative development workflows.
- Well versed with open-source development practices.
- Understanding of MLOps for AI application development and deployments.
- Experience with rack-level orchestration tools and data center automation.
- Experience working in a large matrixed organization.
Skills: AI inference accelerators, AI hardware, deep learning algorithms, AI model benchmarking, Gen AI
This role has been open 0 days — well below the 68-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
514
|
Median days open
68 d
|
Median salary
$232k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | Qualcomm | All employers we track in this specialty (514 roles · 75 employers) |
|---|---|---|
| Open roles in this specialty | 109 | 514 |
| Open roles in the wider Software, Firmware & Systems family | 767 | 5948 · 143 employers |
| Median days open | 98 d | 68 d (+30 d vs this employer) |
| Median salary (USD postings) | — | $232k |
Skills observed across this category: AI inference accelerators, AI hardware, deep learning algorithms, AI model benchmarking, Gen AI
Who's hiring in this category
- Qualcomm (this employer) · 106 open roles · median 101 d
- NVIDIA · 84 open roles · median 63 d
- AMD · 46 open roles · median 61 d
- Micron Technology · 33 open roles · median 43 d
- Mobileye · 21 open roles · median 57 d
- Analog Devices · 14 open roles · median 26 d
How we counted: 514 open AI/ML Hardware Engineering (AI ML Hardware) roles from 75 employers tracked in the SemiconductorJobs index, counted 23 Sept 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.