Software Engineering Manager, LLM Training at LinkedIn

Mountain View, California, United States -

Full Time

Start Date

Immediate

Expiry Date

23 Jun, 26

Salary

0.0

Posted On

25 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Distributed Training, Parallelism Strategies, Supervised Fine-Tuning, RLHF, Multi-Teacher Distillation, Pruning, Quantization, Speculative Decoding, Performance Engineering, Liger Kernels, FlashAttention, High-Performance Data I/O, Multi-Modal Strategy, Framework Mastery, Observability, Containerized Lifecycle Management

Industry

Software Development

Description

Company Description LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed. Join us to transform the way the world works. Job Description This role will be based in Mountain View, CA. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. As a Software Engineering Manager of the Post-Training Infra team, you will architect the high-throughput systems required for Supervised Fine-Tuning (SFT) and RL, Multi-Techer Distillation, Reinforcement Learning from Human Feedback (RLHF), Agentic Performance Optimization and Agentic Research at scale. You won’t just be "running scripts"; you’ll be optimizing the engine that makes rapid model alignment possible. Responsibilities Distributed Training Enablement: Enable and support sophisticated parallelism strategies, including data, tensor, pipeline, context, and expert parallelism, for models exceeding 100B+ parameters. Provide optimized configurations, reference examples, and platform-level integration so that customer teams can effectively leverage these techniques Post-Training Expertise: Maintain deep expertise across the post-training landscape, including Multi-Teacher Distillation, RL-based alignment and optimization (RLHF, GRPO), Pruning, Quantization, and Speculative Decoding. Build and maintain reusable platform components that enable customer teams to efficiently leverage these techniques in their workflows. Performance Engineering: Deep-dive into strategic customer workloads and drive workload-specific and platform-level optimizations, including Liger Kernels, FlashAttention, low-precision training, high-performance data I/O, and inter-node latency reduction. Multi-Modal Strategy: Video and Audio Models Post Training strategy Framework & Ecosystem Mastery: Act as a bridge to the OSS community. You will contribute to and troubleshoot the "Post-Training Stack," including Liger, PyTorch, Hugging Face (Accelerate/Transformers), Megatron, Ray, VERL, SGLang and vLLM. Observability & Profiling: Develop advanced telemetry for large-scale training runs. You will use profiling tools to debug hardware-level stalls (NCCL timeouts, memory fragmentation) and provide internal teams with actionable insights into training stability. Containerized Lifecycle Management: Lead the development of the "Golden Image" environment. Maintain and distribute optimized, containerized base images with compatible, validated builds of PyTorch, CUDA, and the broader training stack to ensure seamless training on our clusters. Responsible AI & Compliance Partnership: Serve as the bridge between the training platform and Responsible AI teams, collaborating on data compliance, model evaluation, and safety processes. Ensure the platform provides the tooling and integration points needed for RAI teams to effectively apply their frameworks throughout the training lifecycle. Agentic Strategy: Lead development of Agents for autonomous model research, performance optimization Lead, coach and manage core team of engineers working on building the infrastructure. Participate with senior management in developing a long-term technology roadmap for the team and company. Have the ability to dive deep into technical discussions to challenge the status quo, and steer the team in the right direction/to push the envelope. Communicate and collaborate effectively with stakeholders across engineering and business leadership. Help the team realize their potential by setting clear expectations, openly evaluating performance, upholding accountability, and providing challenges to stretch their skills. Drive a culture of operational excellence. Lead the team into defining performance goals, metrics and building the infrastructure and tooling necessary to maintain a high quality bar and detect issues in real time. Create an inclusive work environment that fosters autonomy, transparency, innovation and learning, while holding a high bar for quality. Qualifications Basic Qualifications BA/BS Degree in Computer Science or related technical discipline, or equivalent practical experience. 1+ year(s) of management experience or 1+ year(s) of staff level engineering experience with management training 5+ years of industry experience in software design, development, and large-scale software engineering Experience in LLMs - Post Training and/or Inference for a year minimum Hands on experience developing distributed system Preferred Qualifications MS or PhD in Computer Science or related technical discipline 2+ years of hands-on software engineering/technical management and people management experience 7+ years industry experience in software design, development, and algorithm related solutions. Experience in architecting, building, and running large-scale distributed systems Experience with industry, opensource, and/or academic research research papers published in the space Suggested Skills Distributed systems LLM Training AI infrastructure You will Benefit from our Culture: We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels. LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $170,000 - $277,000 Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits. Additional Information Equal Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful. If you need a reasonable accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us at [email protected] and describe the specific accommodation requested for a disability-related limitation. Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to: Documents in alternate formats or read aloud to you Having interviews in an accessible location Being accompanied by a service dog Having a sign language interpreter present for the interview A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response. LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information. San Francisco Fair Chance Ordinance Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records. Pay Transparency Policy Statement As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency. Global Data Privacy Notice for Job Candidates Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal. Workplace Type: Hybrid Career Track & Grade: MR3/9 Department: Engineering

Responsibilities

This role involves architecting high-throughput systems for Supervised Fine-Tuning (SFT), RL, Multi-Teacher Distillation, RLHF, and Agentic Performance Optimization at scale, while leading, coaching, and managing a core team of infrastructure engineers. Responsibilities include enabling sophisticated parallelism strategies, maintaining expertise in post-training techniques, driving workload-specific optimizations, and developing advanced telemetry for large-scale training runs.