Collide Capital LLC
Staff Software Engineer, AI Platform
Collide Capital LLC, Mountain View, California, us, 94039
Overview
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 opportunities, build skills, and gain insights. We are committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to a culture built on trust, care, inclusion, and fun where everyone can succeed. The work location for this role is hybrid, performed both from home and from a LinkedIn office on select days as determined by the business needs of the team. This role can be based in Mountain View, CA, San Francisco, CA, or Bellevue, WA. Responsibilities
Owning the technical strategy for broad or complex requirements with insightful and forward-looking approaches that go beyond the direct team and solve large open-ended problems. Designing, implementing, and optimizing the performance of large-scale distributed serving or training for personalized recommendation as well as large language models. Improving the observability and understandability of various systems with a focus on improving developer productivity and system sustenance. Mentoring other engineers, defining our challenging technical culture, and helping to build a fast-growing team. Working closely with the open-source community to participate and influence cutting edge open-source projects (e.g., vLLMs, PyTorch, GNNs, DeepSpeed, Huggingface, etc.). Functioning as the tech-lead for several concurrent key initiatives in AI Infrastructure and defining the future of AI Platforms. As a Staff Software Engineer, you will have first-hand opportunities to advance one of the most scalable AI platforms in the world. You will work with talented researchers and engineers to build your career and your personal brand in the AI industry. Team Focus Areas
Model Training Infrastructure: build next-gen training infrastructure, high-performance data I/O, distributed training for very large parameter models, and advanced support for internal AI teams in areas like model parallelism and tensor parallelism. Feature Engineering: shape the future of AI with the Feature Platform, processing data at scale using Spark, Beam, Flink, and more to transform raw data into feature insights stored in the Feature Store and served with high performance. Model Serving Infrastructure: build low-latency, high-performance inference infrastructure for LLMs and personalization models, with GPU-based inference and on-device and online training. ML Ops: support AI metadata, observability, orchestration, ramping, and experimentation across models to optimize performance. Qualifications
Basic Qualifications
Bachelor's Degree in Computer Science or related technical discipline, or equivalent practical experience 4+ years of experience in the industry with leading or building deep learning systems 4+ years of experience with Java, C++, Python, Go, Rust, C#, and/or functional languages such as Scala Hands-on experience developing distributed systems or other large-scale systems Preferred Qualifications
8+ years of relevant experience with BS or 7+ years with MS, or PhD with 4+ years of relevant experience Experience working with geographically distributed teams Strong interpersonal communication skills and ability to work in a diverse, team-focused environment Experience building ML applications, LLM serving, GPU serving Experience with search systems or large-scale distributed systems Expertise in ML infrastructure and technologies like MLFlow, Kubeflow, and large-scale distributed systems Experience with distributed data processing engines like Flink, Beam, Spark; feature engineering Co-author or maintainer of open-source projects Familiarity with containers and container orchestration Expertise in deep learning frameworks and tensor libraries (e.g., PyTorch, TensorFlow, JAX/Flax) Skills
ML Algorithm Development Machine Learning and Deep Learning Information retrieval / recommendation systems / distributed serving / Big Data Communication Stakeholder Management You will Benefit from our Culture
We offer generous health and wellness programs and time away for employees at all levels. LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $170,000 - $277,000. Actual compensation is based on factors including skill set, experience, certifications, and location. The total compensation may also include annual bonus, stock, benefits, and other incentive 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 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 an inclusive and accessible experience for all job seekers, including individuals with disabilities. If you need a reasonable accommodation, contact accommodations@linkedin.com with details of the accommodation requested. Acknowledgement of accommodations will be provided within three business days. LinkedIn will not discriminate against applicants for inquiring about pay or discussing pay, except as required by role and law.
San Francisco Fair Chance Ordinance Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider qualified applicants with arrest and conviction records. Pay Transparency Policy Statement As a federal contractor, LinkedIn follows pay transparency and non-discrimination provisions at: https://lnkd.in/paytransparency. Global Data Privacy Notice for Job Candidates Please follow this link to access the document about how LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.
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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 opportunities, build skills, and gain insights. We are committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to a culture built on trust, care, inclusion, and fun where everyone can succeed. The work location for this role is hybrid, performed both from home and from a LinkedIn office on select days as determined by the business needs of the team. This role can be based in Mountain View, CA, San Francisco, CA, or Bellevue, WA. Responsibilities
Owning the technical strategy for broad or complex requirements with insightful and forward-looking approaches that go beyond the direct team and solve large open-ended problems. Designing, implementing, and optimizing the performance of large-scale distributed serving or training for personalized recommendation as well as large language models. Improving the observability and understandability of various systems with a focus on improving developer productivity and system sustenance. Mentoring other engineers, defining our challenging technical culture, and helping to build a fast-growing team. Working closely with the open-source community to participate and influence cutting edge open-source projects (e.g., vLLMs, PyTorch, GNNs, DeepSpeed, Huggingface, etc.). Functioning as the tech-lead for several concurrent key initiatives in AI Infrastructure and defining the future of AI Platforms. As a Staff Software Engineer, you will have first-hand opportunities to advance one of the most scalable AI platforms in the world. You will work with talented researchers and engineers to build your career and your personal brand in the AI industry. Team Focus Areas
Model Training Infrastructure: build next-gen training infrastructure, high-performance data I/O, distributed training for very large parameter models, and advanced support for internal AI teams in areas like model parallelism and tensor parallelism. Feature Engineering: shape the future of AI with the Feature Platform, processing data at scale using Spark, Beam, Flink, and more to transform raw data into feature insights stored in the Feature Store and served with high performance. Model Serving Infrastructure: build low-latency, high-performance inference infrastructure for LLMs and personalization models, with GPU-based inference and on-device and online training. ML Ops: support AI metadata, observability, orchestration, ramping, and experimentation across models to optimize performance. Qualifications
Basic Qualifications
Bachelor's Degree in Computer Science or related technical discipline, or equivalent practical experience 4+ years of experience in the industry with leading or building deep learning systems 4+ years of experience with Java, C++, Python, Go, Rust, C#, and/or functional languages such as Scala Hands-on experience developing distributed systems or other large-scale systems Preferred Qualifications
8+ years of relevant experience with BS or 7+ years with MS, or PhD with 4+ years of relevant experience Experience working with geographically distributed teams Strong interpersonal communication skills and ability to work in a diverse, team-focused environment Experience building ML applications, LLM serving, GPU serving Experience with search systems or large-scale distributed systems Expertise in ML infrastructure and technologies like MLFlow, Kubeflow, and large-scale distributed systems Experience with distributed data processing engines like Flink, Beam, Spark; feature engineering Co-author or maintainer of open-source projects Familiarity with containers and container orchestration Expertise in deep learning frameworks and tensor libraries (e.g., PyTorch, TensorFlow, JAX/Flax) Skills
ML Algorithm Development Machine Learning and Deep Learning Information retrieval / recommendation systems / distributed serving / Big Data Communication Stakeholder Management You will Benefit from our Culture
We offer generous health and wellness programs and time away for employees at all levels. LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $170,000 - $277,000. Actual compensation is based on factors including skill set, experience, certifications, and location. The total compensation may also include annual bonus, stock, benefits, and other incentive 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 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 an inclusive and accessible experience for all job seekers, including individuals with disabilities. If you need a reasonable accommodation, contact accommodations@linkedin.com with details of the accommodation requested. Acknowledgement of accommodations will be provided within three business days. LinkedIn will not discriminate against applicants for inquiring about pay or discussing pay, except as required by role and law.
San Francisco Fair Chance Ordinance Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider qualified applicants with arrest and conviction records. Pay Transparency Policy Statement As a federal contractor, LinkedIn follows pay transparency and non-discrimination provisions at: https://lnkd.in/paytransparency. Global Data Privacy Notice for Job Candidates Please follow this link to access the document about how LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.
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