LinkedIn
Overview
LinkedIn is the world’s largest professional network, built to help members of all backgrounds and experiences achieve more in their careers. Our vision is to create economic opportunity for every member of the global workforce. Every day our members use our products to make connections, discover opportunities, build skills and gain insights. We believe amazing things happen when we work together in an environment where everyone feels a true sense of belonging, and that what matters most in a candidate is having the skills needed to succeed. We invest in our talent and support career growth. This role is located in a hybrid work environment, performed both from home and from a LinkedIn office on select days as determined by the team’s needs. This role can be based in Mountain View, CA, San Francisco, CA, or Bellevue, WA. Responsibilities
Designing, implementing, and optimizing the performance of large-scale distributed serving or training for personalized recommendations as well as large language models. Improving the observability and understandability of various systems with a focus on improving developer productivity and system sustenance. Partner with peers, leads and partners to define, scope, prioritize, and build impactful features at a high velocity. As a Software Engineer, you will contribute to one of the most scalable AI platforms in the world, working with teams of researchers and engineers to advance the field of AI. Specific areas include: Model Training Infrastructure, Feature Engineering, Model Serving Infrastructure, and MLOps/Experimentation — building high-performance data I/O, distributed training capabilities, containerized pipelines, feature stores, low-latency serving, GPU inference, observability, and tooling for AI metadata, ramping, and experimentation. Join us to push the boundaries of scaling large models together and to grow your career in the AI industry. Qualifications
Basic Qualifications
Bachelor’s Degree in Computer Science or related technical discipline, or equivalent practical experience 1+ years of experience in the industry with leading/building deep learning systems Experience with Java, C++, Python, Go, Rust, C# and/or functional languages such as Scala or other relevant coding languages Experience or qualifications in Machine Learning, AI Preferred Qualifications
2+ years of relevant work experience MS or PhD in Computer Science or related technical discipline Experience building ML applications, LLM serving, GPU serving Experience with search systems or similar large-scale distributed systems Experience with distributed data processing engines like Flink, Beam, Spark, etc.; feature engineering Experience in distributed ML training infrastructure (Horovod, PyTorch FSDP, DeepSpeed, HuggingFace, PyTorch Lightning, LLMs, GNNs, MLFlow, Kubeflow, etc.) Familiarity with containers and Kubernetes Experience with deep learning frameworks and tensor libraries like PyTorch, TensorFlow, JAX/FLAX Benefits and Compensation
LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $114,000 - $189,000. Actual compensation packages are based on several factors that are unique to each candidate, including skill set, depth of experience, certifications, and location. The total compensation package may include annual performance bonus, stock, benefits and/or other incentive plans. For more information, visit the benefits page at https://careers.linkedin.com/benefits. Additional Information
Equal Opportunity Statement: LinkedIn is an equal opportunity employer and 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. Reasonable accommodations are provided upon request at accommodations@linkedin.com. Details on fair chance, pay transparency, and global data privacy notices are available in the links below. San Francisco Fair Chance Ordinance, Pay Transparency Policy, and Global Data Privacy Notice for Job Candidates are provided in this description where applicable: links include Pay Transparency and candidate portal references found on LinkedIn’s careers site.
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LinkedIn is the world’s largest professional network, built to help members of all backgrounds and experiences achieve more in their careers. Our vision is to create economic opportunity for every member of the global workforce. Every day our members use our products to make connections, discover opportunities, build skills and gain insights. We believe amazing things happen when we work together in an environment where everyone feels a true sense of belonging, and that what matters most in a candidate is having the skills needed to succeed. We invest in our talent and support career growth. This role is located in a hybrid work environment, performed both from home and from a LinkedIn office on select days as determined by the team’s needs. This role can be based in Mountain View, CA, San Francisco, CA, or Bellevue, WA. Responsibilities
Designing, implementing, and optimizing the performance of large-scale distributed serving or training for personalized recommendations as well as large language models. Improving the observability and understandability of various systems with a focus on improving developer productivity and system sustenance. Partner with peers, leads and partners to define, scope, prioritize, and build impactful features at a high velocity. As a Software Engineer, you will contribute to one of the most scalable AI platforms in the world, working with teams of researchers and engineers to advance the field of AI. Specific areas include: Model Training Infrastructure, Feature Engineering, Model Serving Infrastructure, and MLOps/Experimentation — building high-performance data I/O, distributed training capabilities, containerized pipelines, feature stores, low-latency serving, GPU inference, observability, and tooling for AI metadata, ramping, and experimentation. Join us to push the boundaries of scaling large models together and to grow your career in the AI industry. Qualifications
Basic Qualifications
Bachelor’s Degree in Computer Science or related technical discipline, or equivalent practical experience 1+ years of experience in the industry with leading/building deep learning systems Experience with Java, C++, Python, Go, Rust, C# and/or functional languages such as Scala or other relevant coding languages Experience or qualifications in Machine Learning, AI Preferred Qualifications
2+ years of relevant work experience MS or PhD in Computer Science or related technical discipline Experience building ML applications, LLM serving, GPU serving Experience with search systems or similar large-scale distributed systems Experience with distributed data processing engines like Flink, Beam, Spark, etc.; feature engineering Experience in distributed ML training infrastructure (Horovod, PyTorch FSDP, DeepSpeed, HuggingFace, PyTorch Lightning, LLMs, GNNs, MLFlow, Kubeflow, etc.) Familiarity with containers and Kubernetes Experience with deep learning frameworks and tensor libraries like PyTorch, TensorFlow, JAX/FLAX Benefits and Compensation
LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $114,000 - $189,000. Actual compensation packages are based on several factors that are unique to each candidate, including skill set, depth of experience, certifications, and location. The total compensation package may include annual performance bonus, stock, benefits and/or other incentive plans. For more information, visit the benefits page at https://careers.linkedin.com/benefits. Additional Information
Equal Opportunity Statement: LinkedIn is an equal opportunity employer and 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. Reasonable accommodations are provided upon request at accommodations@linkedin.com. Details on fair chance, pay transparency, and global data privacy notices are available in the links below. San Francisco Fair Chance Ordinance, Pay Transparency Policy, and Global Data Privacy Notice for Job Candidates are provided in this description where applicable: links include Pay Transparency and candidate portal references found on LinkedIn’s careers site.
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