メンバー・オブ・テクニカル・スタッフ (AIトレーニングインフラストラクチャ)
機械学習エンジニア正社員リード · 3年以上ニューヨーク—
大規模モデル学習のためのインフラを設計・最適化する業務を担当します。コンピュータサイエンス関連の学士号または同等の実務経験が必須です。分散システムおよびMLインフラ分野で3年以上の経験が求められます。PyTorch、クラウドプラットフォーム、KubernetesおよびDockerの活用能力が必要です。
大規模モデル学習のためのインフラを設計・最適化する業務を担当します。コンピュータサイエンス関連の学士号または同等の実務経験が必須です。分散システムおよびMLインフラ分野で3年以上の経験が求められます。PyTorch、クラウドプラットフォーム、KubernetesおよびDockerの活用能力が必要です。
Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.
As a Training Infrastructure Engineer, you'll design, build, and optimize the infrastructure that powers our large-scale model training operations. Your work will be essential to developing high-performance AI training infrastructure. You'll collaborate with AI researchers and engineers to create robust training pipelines, optimize distributed training workloads, and ensure reliable model development.
Design and implement scalable infrastructure for large-scale model training workloads
Develop and maintain distributed training pipelines for LLMs and multimodal models
Optimize training performance across multiple GPUs, nodes, and data centers
Implement monitoring, logging, and debugging tools for training operations
Architect and maintain data storage solutions for large-scale training datasets
Automate infrastructure provisioning, scaling, and orchestration for model training
Collaborate with researchers to implement and optimize training methodologies
Analyze and improve efficiency, scalability, and cost-effectiveness of training systems
Troubleshoot complex performance issues in distributed training environments
Bachelor's degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience
3+ years of experience with distributed systems and ML infrastructure
Experience with PyTorch
Proficiency in cloud platforms (AWS, GCP, Azure)
Experience with containerization, orchestration (Kubernetes, Docker)
Knowledge of distributed training techniques (data parallelism, model parallelism, FSDP)
Master's or PhD in Computer Science or related field
Experience training large language models or multimodal AI systems
Experience with ML workflow orchestration tools
Background in optimizing high-performance distributed computing systems
Familiarity with ML DevOps practices
Contributions to open-source ML infrastructure or related projects
Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.
Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.