Our Opportunity We're looking for a passionate and experienced Machine Learning engineer to join our Machine Intelligence team.
In this role, you'll help us build best-in-class products, including Snyk DeepCode AI Fix.
This platform will play a key role in ensuring application security throughout the development lifecycle.
The ideal candidate will have industrial-level (research and/or production) ML experience of 3+ years, and a proven track record of developing production-grade ML pipelines.
This role requires a deep understanding of language model optimizations and the ability to run and own experimentation end-to-end.
The successful candidate will be proficient in C++, Python, and one of the typical ML training and serving frameworks, and will play a crucial role in advancing our machine learning capabilities for code analysis and program understanding.
You'll Spend Your Time: Design, implement, and maintain machine learning models at the intersection of program analysis and language processing, such as automatic code generation and code understanding. Develop and optimize production-grade pipelines to ensure scalability and efficiency. Optimize existing machine learning models for performance and efficiency. Lead cross-team collaborations to integrate machine learning solutions into our products and services. This is a "research engineer" position – we expect that you own features end-to-end, from ideation at our regular small-hands hackathons to production. What You'll Need: Fluency in programming languages, specifically C++ and Python. ML-serving savviness: you have seen something like Triton/TensorRT/ONNX/llama.cpp in action. Demonstrated experience in creating and maintaining production-grade data, training, and evaluation pipelines. Expertise in model optimization and the ability to manage experimentation processes end-to-end. Familiarity with build systems such as Bazel (or similar), CI/CD pipelines, and the ability to operate at high testing standards. We'd be Lucky if you have experience in: Ability to not stop at the demo-level: Jupyter notebook or even a locally running Python script is not your final goal. Familiarity with ML training, evaluation, and experiment tracking frameworks. Additional backend-heavy languages such as Go or Rust. Experience with production/serving stack, such as Docker and Kubernetes. Research activities, publications in the field of ML, and contributions to open-source projects. Prior experience in the security domain, code analysis, or program understanding. Experience with large-scale machine learning projects.
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