At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Observe by Snowflake is an AI-powered observability platform built on the Snowflake AI Data Cloud and engineered for scale. We ingest and store logs, metrics, traces, and events on an open, scalable data lakehouse, using open formats like Apache Iceberg, at dramatically lower cost. A dynamic Context Graph and chat-based AI SRE provide rich context and automated workflows so teams can move from detection to root cause of production issue and resolution 10x faster. Leading engineering teams at companies like Capital One, Topgolf, and Dialpad rely on Observe to troubleshoot hundreds of terabytes of telemetry daily while maintaining reliability at enterprise scale. As part of Snowflake, Observe combines startup-style ownership and velocity with the global reach, operational excellence, and ecosystem of one of the world’s leading data platforms. The Team: We are hiring a Senior Software Engineer, Engineering Productivity for our Engineering team at Observe by Snowflake. As part of Engineering Productivity at Observe by Snowflake, you will empower engineering teams across the organization by building scalable developer tools, robust build and test infrastructure, and automated release systems. Working at the intersection of developer experience, build systems, AI agent systems, a
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Role overview
Job description
NVIDIA is hiring senior engineers to develop its AI platform and more specifically its performance optimizations in deep learning frameworks using JAX, a tool that can differentiate between Python control code and NumPy code. Our goal is to deliver a polished, fast, modular and coordinated platform to handle data, training and analysis for a wide range of deep learning solutions using JAX. To achieve this and provide an excellent user experience, you have strong programming skills and excellent system design skills. You should have excellent communication and planning skills for this role. Come join us to build the next generation of tooling that will advance the numerical computing and machine learning research around the world!
What you will be doing:
Play meaningful role in NVIDIA's effort in contributing to JAX.
Design and implement JAX core components and drive peak performance on NVIDIA products.
Work with AI applied researchers and leaders to build future-proof models
Build tools that will increase the efficiency of teams developing AI-based systems.
Work to bridge the gap between the latest in numerical computing, simulation and deep learning research and their applications in real world products.
What we need to see:
BS in Computer Science or Computer Engineering or related field (or equivalent experience).
6+ years relevant experience.
C/C++ and Python programming.
Experience with machine learning frameworks and their internals (e.g. PyTorch, TensorFlow, scikit-learn, etc.).
Proven ability developing customer-facing solutions, balancing feature requests and bugs.
Proven technical foundation in CPU and GPU architectures, numeric libraries, modular software design.
Highly motivated with excellent verbal and written communication skills.
Ability to work successfully with multi-functional teams, principles and architects. Coordinates effectively across organizational boundaries and geographies.
Ways to stand out from the crowd:
Understanding of JAX, Autograd, tracing, code generation and DSL compilers and their design.
Understanding of deep learning training in distributed contexts: multi-GPU, multi-node, synchronous vs asynchronous.
Background with software shipping cycles (dev, deploy, release, CI).
Experience building distributed systems and services at large scale.
What they are looking for
Skills & requirements
Hiring company
NVIDIA
We create the world’s fastest supercomputer and largest gaming platform.
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