# TL, Research Inference

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $380k – $555k/yr
**Skills:** Gpu Programming, Distributed Systems, Inference Optimization, PyTorch, TensorRT, CUDA, Multi-Gpu, Batching, Scheduling, Memory Management, Profiling, Benchmarking
**Posted:** 2026-03-19

> Leads development of high-performance inference systems for large-scale AI models, optimizing execution paths, distributed GPU inference, and operators. Partners with research teams to enable efficient exploration of new architectures grounded in real scalability constraints.

## Job Description

## Responsibilities
- Design and build high-performance inference runtimes for large-scale AI models, with a focus on efficiency, reliability, and scalability.
- Own and optimize core execution paths, including model execution, memory management, batching, and scheduling.
- Develop and improve distributed inference across multiple GPUs, including parallelism strategies, communication patterns, and runtime coordination.
- Implement and optimize inference-critical operators and kernels informed by real-world workloads.
- Partner closely with research teams to ensure new model architectures are supported accurately and efficiently in inference systems.
- Diagnose and resolve performance bottlenecks through profiling, benchmarking, and low-level debugging.
- Contribute to observability, correctness, and reliability of large-scale AI systems.

## Requirements
- Experience building production inference systems, not just training or running models.
- Comfortable with GPU-centric performance engineering, including memory behavior and latency/throughput tradeoffs.
- Worked on multi-GPU or distributed systems involving batching, scheduling, or runtime coordination.
- Can reason end-to-end about inference pipelines, from request handling through execution and output streaming.
- Able to understand research ideas and implement them within real system and performance constraints.
- Enjoy solving hard, ambiguous systems problems that only emerge at scale.
- Prefer hands-on technical ownership and execution over abstract design work.

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