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Principal Software Engineer(distributed systems)

Microsoft

Principal Software Engineer(distributed systems)

Microsoft

China, Beijing, Beijing; China, Jiangsu, Suzhou

·

On-site

·

Full-time

·

3w ago

Required skills

Java

JavaScript

Overview:

Online Advertising is one of the fastest‑growing businesses on the Internet. Microsoft Ads powers large‑scale deep learning workloads across Search, Recommendations, Click Prediction, and Relevance. Deep learning sits at the core of how Ads drives business performance and delivers high‑quality user experiences. We are building a unified, high‑performance inference platform to serve Ads deep learning models at extreme scale. This platform serves billions of requests daily, with strict requirements on latency, throughput, reliability, and cost.

We are seeking a Principal Software Engineer with solidexpertise in high‑performance C++ systems and large‑scale distributed serving, with preferred experience in GPU inference and acceleration technologies. You will be a senior technical leader driving the architecture, performance, and reliability of the next‑generation serving stack for Ads.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

  • Responsibilities- Design and build a unified inference platform for Ads, ensuring scalability, reliability, and efficiency.
  • Optimize model inference via batching, quantization, scheduling, memory management, runtime optimization, and other performance improvements.
  • Develop, optimize, and maintain performance‑critical components for high‑throughput, low‑latency production inference, including GPU‑accelerated paths when applicable.
  • Collaborate with algorithm/model teams to co‑design serving‑aware model architectures and optimizations.
  • Profile and improve end‑to‑end system performance: concurrency, memory footprint, throughput, and latency.
  • Provide senior technical leadership across teams; elevate engineering best practices and influence long‑term technical strategy.

Qualifications:

Required Qualifications:

  • Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or PythonOR equivalent experience.
  • 6+ years' experience building high‑performance, large‑scale distributed systems or ML infrastructure.
  • Experience building and optimizing performance‑critical production systems.
  • Experience working in Ads, Search, Recommendation systems, or other large‑scale online serving systems.

Preferred Qualifications:

  • Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or PythonOR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR equivalent experience.
  • Experience with GPU inference runtimes such as TensorRT, ONNX Runtime, Triton, TRT‑LLM, or vLLM.
  • Expertise in CUDA kernel development and GPU performance engineering.
  • Familiarity with LLM / Transformer inference optimizations, including: sharding, tensor / KV‑cache parallelism, paged attention, continuous batching, quantization (FP8 / AWQ), and hybrid CPU–GPU orchestration.

#MicrosoftAI

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

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About Microsoft

Microsoft

Microsoft

Public

Microsoft Corporation is an American multinational technology conglomerate headquartered in Redmond, Washington.

10,001+

Employees

Redmond

Headquarters

$3000B

Valuation

Reviews

3.8

5 reviews

Work-life balance

4.1

Compensation

4.3

Culture

3.4

Career

3.2

Management

3.0

65%

Recommend to a friend

Pros

Excellent compensation and benefits package

Four-day workweek with improved work-life balance

Supportive managers and teams

Cons

High-pressure environment causing anxiety

Unprofessional interview processes

Limited creative work opportunities

Salary Ranges

5,620 data points

Senior/L5

Senior/L5 · Account Management

5 reports

$209,483

total per year

Base

$181,941

Stock

-

Bonus

-

$194,895

$209,483

Interview experience

1 interviews

Difficulty

4.0

/ 5

Duration

14-28 weeks

Experience

Positive 0%

Neutral 0%

Negative 100%

Interview process

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

Common questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit