Software Engineer, AI Training Infrastructure

2 Months ago • 3 Years + • $175,000 PA - $220,000 PA
Devops

Job Description

Fireworks AI is building the future of generative AI infrastructure, offering a platform with high-quality models and fast, scalable inference. The Training Infrastructure Engineer will design, build, and optimize the infrastructure for large-scale model training operations. This role involves collaborating with AI researchers and engineers to create robust training pipelines, optimize distributed training workloads, and ensure reliable model development. Responsibilities include designing scalable infrastructure, developing distributed training pipelines for LLMs and multimodal models, optimizing training performance across multiple GPUs, nodes, and data centers, implementing monitoring and debugging tools, architecting data storage solutions, automating infrastructure provisioning, and troubleshooting performance issues in distributed training environments.
Good To Have:
  • Master's or PhD in Computer Science
  • Experience training large language models
  • Experience with multimodal AI systems
  • Experience with ML workflow orchestration tools
  • Background in optimizing high-performance computing
  • Familiarity with ML DevOps practices
  • Contributions to open-source ML projects
Must Have:
  • Bachelor's degree in Computer Science or related field
  • 3+ years of experience with distributed systems
  • Experience with ML infrastructure
  • Experience with PyTorch
  • Proficiency in cloud platforms (AWS, GCP, Azure)
  • Experience with containerization
  • Experience with orchestration (Kubernetes, Docker)
  • Knowledge of distributed training techniques
Perks:
  • Meaningful equity in a fast-growing startup
  • Competitive salary
  • Comprehensive benefits package

Add these skills to join the top 1% applicants for this job

problem-solving
aws
azure
model-serving
pytorch
docker
kubernetes

About Us:

Here at Fireworks, we’re building the future of generative AI infrastructure. Fireworks offers the generative AI platform with the highest-quality models and the fastest, most scalable inference. We’ve been independently benchmarked to have the fastest LLM inference and have been getting great traction with innovative research projects, like our own function calling and multi-modal models. Fireworks is funded by top investors, like Benchmark and Sequoia, and we’re an ambitious, fun team composed primarily of veterans from Pytorch and Google Vertex AI.

The Role: 

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.

Key Responsibilities:

  • 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

Minimum Qualifications:

  • 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)

Preferred Qualifications:

  • 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

Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.

Base Pay Range (Plus Equity)

$175,000 - $220,000 USD

Why Fireworks AI?

  • 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.

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