HiringCafe
Posted 11h ago

Member of Technical Staff, Inference

@ Mirendil
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San Francisco, California, United States
$350k-$500k/yrOnsiteFull Time
Responsibilities:building systems, optimizing performance, extending frameworks
Requirements Summary:Experience owning inference systems, optimizing inference performance on GPU/accelerator hardware, and extending distributed inference frameworks for high-throughput, low-latency serving.
Technical Tools Mentioned:vLLM, SGLang, TensorRT-LLM
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Job Description

Mirendil

Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We believe accelerating scientific discovery is one of the most powerful ways to improve the future of humanity, and that AI will play a central role in making that possible.

We are building a frontier AI research company and training our own models end-to-end. Our work spans areas such as model training, reinforcement learning, reasoning systems, and infrastructure for large-scale experiments. Our team includes researchers and engineers from Anthropic, Google DeepMind, xAI, OpenAI, Microsoft, Apple, and MIT.

The Role

We are looking for an engineer to own the inference systems that power our models in production and research. You'll work across the full inference stack, from serving infrastructure down to hardware-level optimization. Some example areas you might work on (not limited to):

  • Design and build high-throughput, low-latency inference serving systems for frontier models, optimizing for both research iteration and production deployment

  • Optimize inference performance across GPU and accelerator hardware - maximizing FLOPs utilization, memory bandwidth, and compute efficiency for large-scale models

  • Enable and extend distributed inference frameworks (e.g. vLLM, SGLang, TensorRT-LLM) to support novel architectures, long-context workloads, and agentic inference patterns

  • Implement and validate inference-time optimizations: speculative decoding, quantization, KV cache management, and batching strategies

  • Build observability and reliability infrastructure so the team can measure latency, throughput, and cost across every serving configuration

  • Partner directly with teams to bring new model architectures and post-training techniques into production quickly

If you're excited about pushing the performance limits of frontier model inference, we'd love to hear from you.

We offer a base salary of $350,000–$500,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.