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Qualcomm

Inventing the technologies the world loves.

Lead Model Accuracy Development and Test Engineer (Datacentre AI Engineering) - Riyadh, KSA

职能机器学习
级别Lead级
地点Riyadh, Saudi Arabia
方式现场办公
类型全职
发布2个月前
立即申请

必备技能

PyTorch

Company:

Qualcomm Middle East Information Technology Company LLC:

Job Area:

Engineering Group, Engineering Group > Software Engineering

General Summary:

About the Role

We are seeking a hands-on technical leader to own end-to-end model accuracy for large-scale deep learning workloads running on data-center accelerators. As the Lead for Model Accuracy Development & Test, you will set the technical direction, mentor engineers, and drive cross-functional programs to define KPIs, build accuracy pipelines, debug failures, and deliver accuracy parity across frameworks (Py Torch/ONNX) and hardware targets (AI100/AI200, NVIDIA, Gaudi, TPU). You will combine deep systems knowledge with pragmatic program execution to unblock production deployments and raise the bar for accuracy engineering.

Key Responsibilities will include

  • Technical Leadership & Strategy
  • Define the multi-quarter accuracy roadmap, KPIs, and bar for productization across precision modes (FP32/FP16/INT8/FP8/INT4)
  • Own architecture for accuracy evaluation pipelines, including data prep, replay, metrics, dashboards, and automated triage.
  • Make design decisions on quantization/calibration strategies, operator implementations, and accuracy-preserving optimizations (TensorRT, ONNX Runtime, AITemplate, Triton).
  • Delivery & Cross-Functional Ownership
  • Drive cross-team execution with model onboarding, runtime, compiler, firmware, and HW perf teams to reach accuracy sign-off.
  • Establish SLAs and acceptance criteria for accuracy parity across Py Torch → ONNX → backend engines and across hardware.
  • Create clear investigation plans for deltas (pre/post-processing drift, tokenization mismatch, QDQ placement, operator fallback).
  • Debugging & Analysis
  • Lead deep-dives for failure triage using intermediate dumps, layer-wise comparisons, attribution tools, and sensitivity analysis.
  • Perform slice-based analysis (batch, concurrency, sequence length, domain shifts) and design experiments for recovery (calibration, fine-tuning, hyperparameter sweeps).
  • Institutionalize patterns/playbooks for recurring issues (normalization overflow, activation scaling, precision loss, layout drift).
  • People Leadership & Mentoring
  • Mentor 3–6 engineers; conduct design reviews, code reviews, and guide experiment methodology and result interpretation.
  • Partner with recruiting; define interview rubrics; onboard and grow the accuracy engineering discipline within the team.
  • Quality, Compliance & Documentation
  • Ensure reproducibility and auditability of results (versioning of datasets, artifacts, firmware/runtime).
  • Publish clear dashboards, RFCs, and decision logs; drive stakeholder communication and status.

Required Experience & Skills

  • 10+ years of industry experience in AI/ML, with significant ownership of model accuracy and evaluation.
  • Expertise in architectures (Transformers, CNNs, Diffusion, MoE) and their accuracy sensitivities.
  • Proven depth with inference runtimes/backends (TensorRT, ONNX Runtime, AITemplate, Triton) and graph conversion (Py Torch → ONNX → engines).
  • Strong quantization background (INT8/FP8/INT4), calibration methods, QAT, and mixed-precision workflows.
  • Hands-on engineering in Python; solid software engineering practices (testing, CI, packaging).
  • Experience comparing results across hardware/software stacks (AI100/AI200, NVIDIA A100/H100/B200, Gaudi, TPU) and firmware/runtime versions.
  • Fluency with accuracy metrics, statistical analysis, visualization, and experiment design.
  • Demonstrated ability to lead cross-functional programs and mentor engineers.

We'd love to see

  • Experience with video generation/I2V accuracy and multi-modal benchmarking.
  • Familiarity with LLM/VLM accuracy tooling (lm-eval, HELM) and dataset curation.
  • Background in distributed systems/Kubernetes and cloud inference services.

Qualifications

  • BS/MS in Computer Science, Electrical/Computer Engineering, or related field; PhD is a plus.
  • 10+ years of relevant software/ML experience; 3+ years in a leading/mentoring capacity.
  • Strong problem-solving and communication skills; ability to influence across organizations.

Success Metrics (Examples)

  • Accuracy parity vs. Py Torch/ONNX baselines within target MAD/PSNR/SSIM thresholds for each model + precision mode.
  • Time-to-resolution for P0 accuracy regressions (median/95th).
  • Automation coverage of accuracy evaluation across supported frameworks and hardware targets.
  • Mentorship outcomes: onboarding time reduction and quality of design reviews.

What's on Offer

Apart from working with great people, we offer the below:

  • Salary including housing & transport allowance

  • Stock (RSU's) and performance related bonus

  • 16 weeks fully paid Maternity Leave

  • 6 weeks fully paid Paternity Leave

  • Employee stock purchase scheme

  • Child Education Allowance

  • Relocation and immigration support (if needed)

  • Life and Medical Insurance

  • Live+ Well Reimbursement for health and recreational membership fees

Minimum Qualifications:

  • Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience. OR
    Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
    OR
    PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.

  • 2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.

References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.

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).

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.

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.

If you would like more information about this role, please contact Qualcomm Careers.

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关于Qualcomm

Qualcomm

Qualcomm

Public

Inventing the technologies the world loves.

10,001+

员工数

San Diego

总部位置

$136B

企业估值

评价

3条评价

3.0

3条评价

工作生活平衡

3.0

薪酬

2.0

企业文化

2.5

职业发展

3.5

管理层

2.0

45%

推荐率

优点

Opportunity to work at reputable company

Interesting work and new skill development

Strong brand name recognition

缺点

Low compensation compared to market rates

Poor communication from employees

No benefits provided

薪资范围

21个数据点

Junior/L3

Junior/L3 · Data Scientist

0份报告

$196,000

年薪总额

基本工资

$150,000

股票

$33,000

奖金

$13,000

$166,600

$225,400

面试评价

8条评价

难度

2.8

/ 5

时长

14-28周

面试流程

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

常见问题

Coding/Algorithm

Technical Knowledge

System Design

Behavioral/STAR

Past Experience