About the role
About Sarvam Sarvam is building the bedrock of Sovereign AI for India. The company is developing India’s full-stack sovereign AI platform, building across research, models, infrastructure and applications with a singular focus on making AI genuinely work for India. Sarvam works with leading enterprises and public institutions and is backed by Lightspeed, Peak XV, and Khosla Ventures. Sarvam partners with India’s leading brands, including Tata Capital, SBI Life, CRED, IDFC, and LIC.
About
the Role You will own the infrastructure that our next family of foundational models is trained on. This is a deep systems role: distributed training at scale, parallelism strategies, GPU kernel work, throughput and stability optimization, and the unglamorous reliability engineering that determines whether a frontier training run actually finishes. The bar is high. A bad MFU number, a slow data loader, a switch flap — these are problems whose cost is measured in weeks and millions of dollars. You will be expected to find them before they happen, and to fix them fast when they do. What You’ll Do Build, maintain, and continually push the limits of our distributed training stack across large GPU clusters. Design and implement parallelism strategies — data, tensor, pipeline, sequence, expert — and reason about which combinations make sense for which architectures and which scales. Profile and optimize end-to-end training throughput: kernel performance, communication overlap, memory layout, checkpointing, data loading. Write and tune custom GPU kernels (CUDA, Triton) where off-the-shelf is leaving performance on the table. Own the reliability of long-running training jobs — fault tolerance, checkpoint integrity, deterministic restarts, automated detection of slow nodes and silent corruption. Partner closely with researchers to make sure architectural ideas can actually be trained efficiently, and with the data team to keep the pipeline from being the bottleneck. What We’re Looking For BS or MS in Computer Science or a closely related technical field (or equivalent demonstrated experience). 3+ years of experience building ML training infrastructure or large-scale distributed systems. Exceptional early-career candidates with a strong systems background will be considered. Hands-on experience training large models with distributed training frameworks — Megatron-LM, DeepSpeed, FSDP, NeMo, or equivalent. You should have been on-call for a real pretraining run. Deep working knowledge of GPU architecture, the CUDA programming model, and standard tools for profiling GPU workloads (Nsight, PyTorch profiler, etc.). Strong PyTorch internals knowledge. You're comfortable reading and modifying low-level training code, not just using high-level APIs. Meaningful open-source contributions in the training infrastructure ecosystem — Megatron, DeepSpeed, PyTorch, vLLM, Triton, NCCL, or similar.
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