Staff Machine Learning Engineer
Sailpoint👥 1001 - 5000 employees🏢 Computer Software
Lead the design and deployment of production-grade ML systems powering SailPoint's AI-driven identity security platform.
Applied ML Engineer
📅 Jul 9
PythonML CodeDeep Learning StackPytorch
📜 Description
- Own the research-to-production pipeline: take research checkpoints and turn them into production models, defining the repeatable path from a working result to a deployed, monitored, scaled service.
- Partner directly with research scientists to productionize new models — translating experimental training and evaluation code into robust, reproducible, well-tested workflows.
- Build and extend the tooling and abstractions that let researchers and engineers move models through training, evaluation, packaging, and deployment with minimal friction and maximal reproducibility.
- Design and own model release gates — automated evaluation, regression detection, and quality/latency/throughput checks that decide whether a model is ready to ship.
- Optimize models and serving for production: efficient inference, batching, memory and latency tuning, and the profiling work that turns a research model into something that performs economically at scale.
- Strengthen the build and delivery layer for models on our custom infrastructure, spanning our GPU compute and cloud environments, so that shipping a model is fast, safe, and observable.
🛠️ Requirements
- Strong software engineering fundamentals, with proficiency in Python and experience writing production-quality, well-tested ML code.
- Hands-on experience taking ML models from research or prototype stage into production at scale — not just training models, but shipping and operating them.
- A working understanding of the modern deep learning stack (e.g., PyTorch) and the realities of training, evaluating, and serving large models.
- Experience building ML pipelines and tooling — training orchestration, evaluation harnesses, model packaging, deployment, or CI/CD for models.
- Familiarity with serving and inference optimization — latency, throughput, batching, and resource efficiency for production model workloads.
- Comfort operating across distributed systems and GPU compute, whether in the cloud, on bare metal, or both.
- A collaborative, builder mindset — you can partner with researchers, scope an ambiguous problem, and drive it to a measurable result.
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