Megatron-LM Jobs

Together AI

The Speculative Decoding team at Together AI focuses on developing draft models and algorithms to accelerate large language model generation without altering outputs. Their work integrates model training, inference algorithms, and serving systems, optimizing all three for reduced cost and latency. As a research intern, you will explore open questions in speculative decoding, including new drafter architectures, verification schemes, knowledge distillation, and adaptive drafters. You will train and fine-tune draft models at scale on GPU clusters and evaluate your solutions against realistic serving workloads, such as long-context and agentic traffic.

Together AI

The Speculative Decoding team at Together AI focuses on developing draft models and algorithms to accelerate large language model generation without altering outputs. Our work spans model training, inference algorithms, and serving systems, aiming to reduce generation cost and latency. As a research intern, you will explore open questions in speculative decoding, including new drafter architectures, verification schemes, knowledge distillation, and adaptive drafters. You will train and fine-tune draft models at scale on GPU clusters and evaluate your ideas against realistic serving workloads, such as long-context and agentic traffic. Projects are designed to address key research questions in speculative decoding, leading to faster generation on frontier models, drafters that adapt to changing workloads, and robust evaluation under real traffic.

Aleph Alpha is seeking a Senior AI Researcher to join their Pre-training team, focusing on advancing the architecture and training of next-generation foundation models. This role involves designing inference-efficient architectures, optimizing large-scale training recipes, and working with thousands of NVIDIA GPUs. The position is ideal for someone excited about the intersection of research and engineering, translating mathematical reasoning into principled training decisions from small-scale experiments to large-scale runs. The researcher will influence model quality, run reliability, and efficiency by combining rigorous experimental design with high-quality production code.

Anduril Industries is a defense technology company focused on transforming military capabilities with advanced technology. We are seeking a Senior AI Infrastructure Engineer to build, scale, and optimize the end-to-end machine learning platform that powers our autonomous systems. In this role, you will own critical components of our ML platform and MLOps tooling, building and operating the infrastructure required to train, evaluate, host, and serve complex AI models across various environments. You will collaborate closely with AI Research Scientists and Platform Engineers to streamline model development, enhance hardware utilization, and ensure the reliable deployment of models into safety-critical operational settings.

$191k - $253k

Thinking Machines is seeking an infrastructure research engineer to design and build the core systems that enable efficient large-scale model training, with a particular focus on numerics. This role involves improving the numerical foundations of their distributed training stack, from precision formats and kernel optimizations to communication frameworks, ensuring the stability, scalability, and speed of training trillion-parameter models. The ideal candidate will excel at the intersection of research and systems engineering, possessing a strong understanding of both optimization mathematics and the practicalities of distributed compute.

$350k - $475k

Thinking Machines is seeking an infrastructure research engineer to design and build the core systems that enable scalable, efficient training of large models for deployment and research. The goal is to make experimentation and training fast and reliable, allowing research teams to focus on science rather than system bottlenecks. This role is ideal for individuals with deep systems and performance expertise combined with a curiosity for machine learning at scale, who will own the training stack end-to-end to ensure every GPU cycle drives scientific progress.

$350k - $475k

Liquid AI, spun out of MIT CSAIL, is developing general-purpose AI systems designed for efficient execution across various deployment targets, from data center accelerators to on-device hardware. The company focuses on ensuring low latency, minimal memory usage, privacy, and reliability, partnering with enterprises in consumer electronics, automotive, life sciences, and financial services. The Training Infrastructure team is building the distributed systems that power next-generation Liquid Foundation Models, requiring the design, implementation, and optimization of large-scale training infrastructure. This role is focused on runtime, performance, and reliability within training systems, offering a high-ownership opportunity to build critical systems from the ground up within a small team with fast feedback loops.