ML Research

Job Scope:

Bridge cutting-edge ML research with production-scale AI infrastructure.

Total /Relevant Experience:

2 Plus Years of experience.

Key Responsibilities:

• Track and analyze frontier research (distributed training, MoE, inference systems)
• Prototype and validate research ideas on large GPU clusters
• Publish internal/external whitepapers
• - Guide platform and product direction for future technology horizon

Must-have skill:

• Strong PyTorch and distributed ML experience
• Systems-aware research mindset
• Ability to move from paper code insigh

Good-to-Have Skills:

• Experience with multi-node GPU environments
• Hands-on experience with distributed training frameworks
• Working knowledge of the NVIDIA ecosystem (TensorRT, Triton, NeMo)
• Experience deploying and operating AI models at scale on Kubernetes clusters
• Familiarity with Slurm or other workload schedulers

Qualifications Criteria:

• PhD in ML/ Systems / HPC