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Jobs / LLM Engineer in United States of America
10 months ago
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Fireworks·10 months ago
10 months ago

Member of Technical Staff, LLM Infrastructure

San Mateo, United States of AmericaFull-timeRemoteMid · 5+ years₹1.5Cr – ₹1.8Cr/yr · est.LLM Engineer

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Must-have skills for this role

  • python
  • go
  • pytorch
  • kubernetes

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Apply faster with autofill FREEfireworks uses Ashby - autofill it instead of retyping.careers.example.com/applyAutofillingFull namePriya SharmaEmailpriya.sharma@example.comPhone+49 30 1234567LocationBerlGet the extension

What you'll do

  • Contribute to the design and development of scalable backend infrastructure that supports distributed training, inference, and data pipelines
  • Build and maintain core backend services such as LLM CI/CD pipeline, control plane, and model serving systems
  • Support performance optimization, cost efficiency, and reliability improvements across compute, storage, and networking layers
  • Building frameworks and safeguards to ensure Fireworks AI has the best model quality in the industry
  • Collaborate with performance, training, and product teams to translate research and product needs into infrastructure solutions
  • Participate in code reviews, technical discussions, and continuous integration and deployment processes

What they're looking for

  • Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
  • 3 years of experience in software engineering, with a focus on infrastructure or machine learning systems
  • Strong programming skills in Python, Go, or a similar language
  • Proven experience in ML infrastructure and tooling (e.g., PyTorch, MLflow, Vertex AI, SageMaker, Kubernetes, etc.).
  • Basic understanding of LLM knowledge (e.g., context length, disaggregated prefill, KV cache memory estimation, etc)

Nice to have

  • 5+ years of experience in software engineering, with a focus on infrastructure or machine learning systems
  • Experience with open source inference engine like vLLM, Sglang, or TRT-LLM
  • Contributions to open-source infrastructure or ML projects
  • Experience in building large scale ML/MLOps infrastructure

Summarised by NextRaise from the employer’s description, which follows in full below.

Full description from employer

About Us:

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.

The Role:

As a Software Engineer on our AI Infrastructure team, you will help design the core systems that power Fireworks AI’s generative AI platform. You will help build infrastructure and tools that ensure the reliability, performance, quality, and availability of our AI system. 

Our mission is to make Fireworks AI the most reliable and user friendly generative AI platform in the world. You will partner closely with our cloud infrastructure team, product team, and performance team to deliver infrastructure that bridges the gap between our customer and the ultra-performant proprietary Fireworks inference engine. 

Key Responsibilities:

  • Contribute to the design and development of scalable backend infrastructure that supports distributed training, inference, and data pipelines

  • Build and maintain core backend services such as LLM CI/CD pipeline, control plane, and model serving systems

  • Support performance optimization, cost efficiency, and reliability improvements across compute, storage, and networking layers

  • Building frameworks and safeguards to ensure Fireworks AI has the best model quality in the industry

  • Collaborate with performance, training, and product teams to translate research and product needs into infrastructure solutions

  • Participate in code reviews, technical discussions, and continuous integration and deployment processes

Minimum Qualifications:

  • Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)

  • 3 years of experience in software engineering, with a focus on infrastructure or machine learning systems

  • Strong programming skills in Python, Go, or a similar language

  • Proven experience in ML infrastructure and tooling (e.g., PyTorch, MLflow, Vertex AI, SageMaker, Kubernetes, etc.).

  • Basic understanding of LLM knowledge (e.g., context length, disaggregated prefill, KV cache memory estimation, etc) 

Preferred Qualifications:

  • 5+ years of experience in software engineering, with a focus on infrastructure or machine learning systems

  •  Experience with open source inference engine like vLLM, Sglang, or TRT-LLM

  •  Contributions to open-source infrastructure or ML projects

  •  Experience in building large scale ML/MLOps infrastructure

Why Fireworks?

  • 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.

Company

Fireworks
San Mateo, United States of America

Company facts come from this company's own listings. We only show what the postings themselves carry.

Sourced from Fireworks's careers site·first seen 29 Aug 2026·last verified 13 Sept 2026·How we source jobs

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