Staff Applied Scientist
At a glance
Senior AI Research role at NetApp. Bangalore.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Turn complex business challenges into mathematical models, then test and assess advanced ML, deep learning and statistical approaches
- Build scalable, maintainable code and carry models from data intake and training through release and ongoing monitoring
- Improve model speed and resource use in high-throughput systems through methods such as distributed training or inference optimization
- Track work in LLMs, RAG, vector intelligence and multimodal systems, while mentoring junior engineers and scientists
What you bring
- At least twelve years of practical AI/ML coding experience, with twelve to fifteen years across data science or related analytics
- A bachelor's or master's in computer science engineering, or equivalent experience
- Strong grounding in algorithms, data structures, multithreaded programming and Unix/Linux system development
- Experience with customer deployment scenarios and collaboration beyond engineering, plus clear communication for varied audiences
Who this fits
This senior individual-contributor role suits an applied scientist who can take ML work from research through production and guide junior teammates. The role is based in Bangalore, India.
From the employer
Job Summary
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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