Software Engineer, ML Compiler
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Minimum qualifications:
- Bachelor's degree in Computer Science, related technical/quantitative field, or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages, and with data structures/algorithms.
- 3 years of experience with ML infrastructure (e.g., model deployment, model
evaluation, data processing, debugging)
- Experience in developing software systems in modern C++.
Preferred qualifications:
- 3 years of experience working on compiler optimization.
About the job
Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
From startups building their first app to billion-user consumer companies crunching big data, the Google Cloud Platform gives developers and businesses the most advanced data and analytics solution on the market, along with Google's secure, high performance infrastructure. Our team of technical account managers and software engineers work with partners across all functions to develop solutions that harness Google's planet-wide platform to solve the world's most challenging problems.
Responsibilities
- Profile and analyze the performance of compiled ML models.
- Improve code generation in the XLA GPU compiler or the Triton compiler.
- Serve on rotations that triage bugs, guide PRs from external contributors, or integrate MLIR into Google.
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