Distributed Training Jobs

We are a dedicated research lab focused on building, understanding, using, and risk-managing foundation models. Our mission is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy. As part of our team, you will work on the core of cutting-edge foundation model training alongside world-class researchers, data scientists, and engineers. You will tackle fundamental and impactful challenges in AI development, contributing to groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills are essential for establishing our institution as a global hub for high-performance computing in deep learning.

$150k - $450k

Anthropic

Reinforcement learning (RL) is crucial for Claude's ability to reason, write code, and act autonomously over long horizons. This role is part of the team responsible for building and maintaining the RL training system, which is used to train production models and for researchers across Anthropic to run their experiments. The team collaborates closely with research teams company-wide on the science and engineering required to scale RL effectively. As a Research Engineer, you will be at the heart of RL at Anthropic, with a direct view of RL training at the frontier, as the system you own supports both production and research runs. You will work with collaborators across research teams to understand system performance, identify areas of fragility, and drive future improvements.

$500k - $850k

SambaNova is seeking a talented and driven ML performance engineer to optimize and scale state-of-the-art foundation models on SambaNova's reconfigurable dataflow platform. You will work hands-on with advanced models to push the limits of throughput, latency, and efficiency. This role bridges the gap between deep learning and systems performance, collaborating across compiler, runtime, and hardware layers to deliver world-record performance for large-scale AI inference.

$180k - $255k

Datadog AI Research (DAIR) is seeking a Research Scientist to partner with Research Engineers on fundamental research problems. The role involves translating research advances into products by collaborating with Datadog's product and engineering teams. The team focuses on high-risk, high-reward problems in cloud observability and security, building on existing AI-powered solutions. The research areas include developing multimodal foundation models for understanding distributed systems and training autonomous agents for observability tasks like incident response and code repair.

$320k - $400k

NVIDIA is seeking a Senior Software Engineer to lead the bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales. In this role, you will set technical direction for communication libraries, model frameworks, and inference/training stacks to ensure state-of-the-art LLM workloads run efficiently and reliably at scale. You will lead deep performance and reliability investigations on multi-GPU and multi-node deployments, define how new platforms are benchmarked and qualified, and build the resilience and failure-attribution capabilities essential for large clusters. This is a hands-on, senior individual-contributor role for an engineer experienced at the intersection of deep learning systems, GPU performance, distributed computing, and large-scale operations.

$184k - $357k

Joining NVIDIA's DGX Cloud AI Efficiency Team means advancing the performance, efficiency, and resiliency of large-scale AI workloads. You will help AI researchers and platform teams understand end-to-end behavior across GPUs, networking, storage, and software stacks. Your work will shape scalable DGX Cloud systems, turn complex measurements into prioritized engineering decisions, and continuously raise the performance and reliability of AI workloads. Join a technically diverse team of infrastructure experts to unlock more efficient AI at scale.

$224k - $431k

PostEra is seeking a Machine Learning Researcher to spearhead the development of an agentic research vertical. This role involves leveraging agentic systems to automate the creation of mechanistic models for biochemical and physiological processes, and to analyze biological data for target validation. You will collaborate closely with chemists and biologists to guide drug discovery decisions using these models. A key focus will be on developing machine learning methods capable of rapid adaptation to new drug discovery challenges with limited labeled data, particularly in molecular and tabular in-context learning and building foundation models from proprietary multimodal data. You will be responsible for the entire research loop, from defining tasks and constructing datasets to training and scaling models, performing rigorous ablations, and translating successful methods into practical capabilities for PostEra's scientists. While prior drug discovery experience is not mandatory, a strong motivation to learn the domain and collaborate with interdisciplinary scientists is essential.

The Core ML team is seeking an engineer to map novel algorithms for efficient large-scale AI training and inference to the Cerebras architecture. This role involves determining when these algorithms outperform competing approaches and how their advantages scale with models, workloads, and hardware. You will combine analytical performance modeling, empirical benchmarking, and hands-on prototyping to characterize the efficiency frontiers of emerging ML algorithms. Your work will span kernel-level and end-to-end performance, influencing research directions, implementation on current systems, and future hardware/software capabilities.

Mirage is seeking an ML Engineer to build and scale the systems powering their video generation models. This role sits at the intersection of research and systems engineering, focusing on making advanced models faster, more efficient, and capable of ultra-low latency, real-time generation. You will work on novel modeling approaches, training objectives, scaling strategies, and inference optimization and efficiency to bring cutting-edge models into production.

$175k - $300k

Together AI

The Model Shaping team at Together AI focuses on tailoring open foundation models for downstream applications, building services that help machine learning developers select and improve models with domain-specific data. We also develop novel methods for efficient model training and evaluation, drawing from machine learning, natural language processing, and ML systems. As a Research Engineer on the Scaling Team, you will translate cutting-edge research in efficient foundation model training into robust, high-performance systems. You will optimize Together's training infrastructure, identify performance bottlenecks, and implement state-of-the-art techniques in production environments. Your work will directly influence the fine-tuning experience for Together's customers. You will integrate newly released open-source models onto the platform, ensuring efficient and reliable training across diverse workloads. Collaborating with Research Scientists, you will build experimental infrastructure to accelerate research and deploy validated ideas at scale.

$200k - $290k

Perplexity is looking for an experienced Machine Learning Research Engineer to contribute to the development of next-generation advanced search technologies, with a particular emphasis on retrieval and ranking. This role involves architecting and building core components of the search platform and model stack, and deploying models from boosting algorithms to LLMs in a scalable and performant manner. The engineer will also be responsible for building and optimizing RAG pipelines for grounding and answer generation, and collaborating with cross-functional teams to ensure high-quality delivery.

Join our groundbreaking research team as we revolutionize the future of physical AI through advanced generative models. As a Research Scientist specializing in Generative AI for Physical AI, you will be at the forefront of developing next-generation algorithms that bridge the gap between virtual and physical realms. You will work with state-of-the-art technology and have access to massive computational resources to bring your ideas to life, shaping the future of AI where digital intelligence meets physical reality.

$168k - $265k

Thinking Machines is building AI that extends human will and judgment, focusing on training frontier models, developing tools for model customization, and crafting interfaces for human-AI communication. This role is crucial for shaping the fundamental reasoning capabilities of our models by owning the late-stage training process. It involves a blend of fundamental research and practical engineering, making it ideal for individuals comfortable working at the intersection of pre-training and post-training.

$350k - $475k

Databricks

Databricks is seeking a Staff Software Engineer to join their AI Research Infrastructure team. In this role, you will be responsible for developing and operating the research stack that powers Databricks' AI research efforts. You will design and build services for scheduling, orchestrating, and observing large-scale training and inference experiment workloads across thousands of GPUs. Your work will involve improving developer tooling to ensure researchers can iterate quickly while maintaining reliability, efficiency, and security. You will collaborate closely with research scientists, ML engineers, and platform teams to transform experimental workloads into robust, repeatable pipelines and push the boundaries of the infrastructure's capabilities.

$199k - $270k

Databricks

Databricks AI Research is seeking a Principal Research Scientist – Scaling to lead a team focused on advancing the state of the art in large-scale machine learning, particularly in LLM training and inference efficiency. This role involves defining and executing a research roadmap to improve how customers train, serve, and adapt LLMs at scale, working closely with product, data, and engineering leaders. The goal is to push the boundaries of LLM efficiency beyond existing models, exploring novel algorithms, systems, and infrastructure improvements. You will drive independent research agendas and collaborate on low-level implementation details to bring cutting-edge methods into production and enhance the Databricks AI platform.

$280k - $350k

Databricks

Databricks AI is seeking a Principal AI Research Scientist to lead a team focused on advancing the state of the art in large-scale machine learning, particularly in LLM training and inference efficiency. This role involves defining and executing a research roadmap to improve how customers train, serve, and adapt LLMs at scale, working closely with product, data, and engineering leaders. The ideal candidate will drive algorithmic innovations, optimize ML systems for distributed training and inference, and translate research breakthroughs into customer-impacting capabilities within the Databricks AI platform. This position also requires fostering a culture of scientific excellence, representing Databricks AI externally, and mentoring research talent.

$270k - $340k

We are seeking a mission-driven Developer Relations Manager focused on Foundational AI Research to engage leading academic labs advancing the next generation of AI models, systems, and methods. In this role, you will work directly with top researchers building frontier AI systems, including large language models, multimodal models, reasoning systems, training methods, inference systems, model serving, and scalable AI infrastructure. You will help researchers adopt NVIDIA’s AI and accelerated computing platforms to push the boundaries of model performance, efficiency, and scale.

$152k - $288k

We are seeking a skilled engineer to join our Autonomous Vehicles team, focusing on the L3/L4 autonomous-driving solution. You will be instrumental in developing a comprehensive, learned 3D/4D world model by fusing navigation, ego-motion, perception, and sensor signals. This role involves close collaboration with perception, prediction, planning, and simulation teams to create a world representation that is complete, temporally consistent, uncertainty-aware, and robust for challenging driving scenarios. Our vision is to develop a shared multimodal scene representation that supports road understanding, 3D object perception, motion prediction, and planning, aiming to achieve human-level AI for navigation in unconstrained environments.

At Thinking Machines, we build tools that empower individuals to customize AI models for their specific needs, including the ability to train model weights. In this role, you will focus on frontier customization techniques and contribute to developing a leading post-training engine called Tinker. Your work will involve a comprehensive understanding of Reinforcement Learning (RL) science, and your findings will directly influence Tinker's training defaults, API design, and the open-source Tinker Cookbook. You will collaborate with internal research teams and contribute to open science initiatives for external partners.

$350k - $475k

Thinking Machines is building AI that extends human will and judgment, with a focus on training frontier models and developing tools that allow users to customize AI for their unique needs. This role leads the Fine-tuning Science team, focusing on frontier customization techniques and the Tinker post-training engine. It's a player-coach position where you will remain hands-on with scientific research while also guiding the team, shaping its direction, and ensuring research findings are integrated into Tinker. You will collaborate with internal research teams, contribute to open science, and engage with external users.

$350k - $475k