Staff Data Scientist
This Staff-level role focuses on translating cutting-edge AI research into production-grade systems at NetApp, requiring expertise in deploying scalable models and bridging research gaps in real-world infrastructure. The candidate will lead incubation projects, ensuring models are optimized for performance, reliability, and business impact at scale.
Skills required
About this role
Job Summary
Job Summary
Are you ready to elevate your career in software engineering? NetApp is seeking Software Engineers to join the CPTO team and contribute to the development and design of innovative incubation projects that will drive the tech strategy for NetApp.
Role Overview
CPTO is looking for an Applied Scientist who is passionate about bridging the gap between AI research and large-scale production systems. In this role, you will not just apply off-the-shelf models; you will design, build, and iterate on novel algorithmic solutions to complex, ambiguous business problems.
Job Requirements
- Familiarity with the design and development of system software.
- 12+ years of hands-on experience in coding of AI/ML.
- Knowledge of Unix/Linux System Programming and Multithreaded programming.
- Expertise in algorithms, data structures, and multi-threaded programming is essential.
- Commitment to sound software engineering practices and a passion for quality.
- Proven track record of meeting tight deadlines without sacrificing quality.
- Strong communication skills, both written and oral, targeted at a wide variety of internal and external audiences.
- Ability to understand customer deployment scenarios and experience in collaborating with peer teams outside of engineering.
- Familiarity with database technologies such as NoSQL, Prometheus and datalake.
- Hands-on experience with code conversion tools like Git.
- Knowledge of software deployment - Docker/Kubernetes
Key Responsibilities:
Research & Design: Formulate complex business problems into formal mathematical frameworks
Design, experiment with, and evaluate advanced ML models, Deep Learning architectures, and statistical methods
Prototyping to Production: Write clean, maintainable, and scalable code
Own the end-to-end lifecycle of ML models, from data ingestion and training to deployment and post-launch monitoring
Scale & Optimize: Optimize model performance, latency, and resource utilization for high-throughput, low-latency production systems (e.g., distributing training, quantization, or optimizing inference graphs)
Technical Leadership: Stay at the forefront of AI research (LLMs, Retrieval-Augmented Generation (RAG), vector intelligence, multi-modal systems).
Mentor junior engineers and scientists, and foster a data-driven engineering
Education
- IC - Typically requires a minimum Bachelor/Master of Engineering in Computer Science; or equivalent experience.
12–15 years of hands-on experience in Data Science and related analytics disciplines.
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