Engineering Manager, Inference ML Runtime

multimodal model execution (text, image, audio, video);

Team Leadership

• Build, manage, and grow a team of ML systems and infrastructure engineers.
• Provide technical direction, mentorship, and career development. - Foster a culture of ownership, velocity, and engineering excellence.
• Recruit top talent in ML systems, distributed systems, and runtime engineering.

Execution & Delivery

• Drive execution of complex, cross-functional initiatives across:

• ML engineering;

• compiler/runtime teams;

• cloud and infrastructure teams. - Own delivery of features such as:

• advanced inference capabilities (structured outputs, sampling strategies);

• heterogeneous model types, including test and multimodal;

• performance optimization (latency, throughput, memory efficiency);

• observability and reliability across the inference stack. - Ensure high-quality releases through strong testing, validation, and operational rigor. Platform & Performance Ownership

• Scale Cerebras’ inference platform to handle large volumes of concurrent requests at very fast speed

• Drive improvements in:

• latency;

• throughput;

• compute efficiency. - Identify and prioritize technical debt and system bottlenecks.

• Maintain Cerebras’ industry-leading inference speed advantage.

Cross-Functional Collaboration

• Partner with:

• ML researchers (model enablement);

• compiler teams (model execution optimization);

• cloud/platform teams (deployment and scaling). - Act as a bridge between research, infrastructure, and production systems.

What You Bring

Required

• 8+ years of experience in:

• large-scale software engineering;

• ML systems or distributed systems. - 2+ years of engineering management experience. - Strong programming skills in:

• Python (production systems);

• C++ (performance-critical systems). - Experience building and scaling large-scale inference systems (LLMs or multimodal). - Experience working with cloud infrastructures and following best-practices for building scalable microservices and applications. Preferred

• Experience with:

• LLM serving frameworks (e.g., vLLM, TensorRT-LLM, SGLang);

• PyTorch and deep learning frameworks;

• distributed systems and high-performance computing. - Familiarity with:

• ML runtime systems;

• model execution pipelines;

• performance optimization for AI workloads. Why This Role Matters

This team is central to Cerebras’ mission of delivering the fastest AI inference in the world. Your work will directly enable real-time AI applications and unlock new capabilities across enterprise and frontier AI use cases. #### Apply today and become part of the forefront of groundbreaking advancements in AI! We believe inclusive teams build better products and companies.* We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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