Software Engineer - Machine Learning

Overview

We are seeking a highly skilled and motivated Machine Learning Engineer with a strong desire to improve the code analysis and remediation, to join the Machine Intelligence Team. We own the process from ideation to product, in a strong collaboration with other groups and divisions. Example of a feature we own is, but not limited to, is Snyk DeepCode AI Fix.

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, one of 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, 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.

You should apply if you:

• Fluency in programming languages, specifically C++ and Python.
• ML-serving savviness: you saw something as 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, ability to operate at high testing standards.

We’d especially love to hear from you if you:

• 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, Rust.
• Experience with production/serving stack, such as Docker, k8s.
• Research activities, publications in the field of ML, 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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