Machine Learning Engineer

2 weeks ago

San, India FullThrottle Labs Pvt Ltd Full time ₹ 1,04,000 - ₹ 1,30,878 per year

We are building a distributed LLM inference network that combines idle GPU capacity from around the world into a single cohesive plane of compute that can be used for running large-language models like DeepSeek and Llama 4. At any given moment, we have over 5,000 GPUs and hundreds of terabytes of VRAM connected to the network.

We are a small, well-funded team working on difficult, high-impact problems at the intersection of AI and distributed systems. We primarily work in-person from our office in downtown San Francisco.

**Responsibilities

**

  • Design and implement optimization techniques to increase model throughput and reduce latency across our suite of models

  • Deploy and maintain large language models at scale in production environments

  • Deploy new models as they are released by frontier labs

  • Implement techniques like quantization, speculative decoding, and KV cache reuse

  • Contribute regularly to open source projects such as SGLang and vLLM

  • Deep dive into underlying codebases of TensorRT, PyTorch, TensorRT-LLM, vLLM, SGLang, CUDA, and other libraries to debug ML performance issues

  • Collaborate with the engineering team to bring new features and capabilities to our inference platform

  • Develop robust and scalable infrastructure for AI model serving

  • Create and maintain technical documentation for inference systems

**Requirements

**

  • 3+ years of experience writing high-performance, production-quality code

  • Strong proficiency with Python and deep learning frameworks, particularly PyTorch

  • Demonstrated experience with LLM inference optimization techniques

  • Hands-on experience with SGLang and vLLM, with contributions to these projects strongly preferred

  • Familiarity with Docker and Kubernetes for containerized deployments

  • Experience with CUDA programming and GPU optimization

  • Strong understanding of distributed systems and scalability challenges

  • Proven track record of optimizing AI models for production environments

Nice to Have

  • Familiarity with TensorRT and TensorRT-LLM

  • Knowledge of vision models and multimodal AI systems

  • Experience implementing techniques like quantization and speculative decoding

  • Contributions to open source machine learning projects

  • Experience with large-scale distributed computing

**Compensation

**We offer competitive compensation, equity in a high-growth startup, and comprehensive benefits. The base salary range for this role is