Senior ML Inference Systems Engineer - Remote-Friendly

Atlassian

Mountain View (CA)

Hybrid

USD 206,000 - 269,000

Full time

3 days ago
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Job summary

At Atlassian, a Senior ML System Engineer on the AI & ML Platform’s Inference Team designs and optimizes large-scale model serving systems, covering distributed infrastructure, batching, and low-level GPU kernels.

You will work with senior ML engineers to fine‑tune open-source LLMs, implement CI/CD for inference, and push performance to meet billions of requests daily across multiple zones. Mountain View offers flexible work options with hybrid arrangements.

Qualifications

  • 5+ years of software engineering experience, 2+ years of system performance optimization experience.
  • Deep low-level systems programming (C/C++ or Rust).
  • Experience with large-scale, high-concurrent production serving.

Responsibilities

  • Architect and implement scalable distributed infrastructure for model serving (load balancing, auto-scaling, batch scheduling, global KV cache).
  • Optimize latency and throughput of model inference under real production workloads.
  • Build reliable, high-concurrency serving systems that serve billions of requests reliably
  • Benchmark, fine-tune, and accelerate inference engines.
  • Create robust CI/CD infrastructure for seamless model deployment and inference engine updates.
  • Partner with senior ML engineers to fine‑tune open-source LLMs and deploy

Skills

Software engineering
Performance optimization
Low-level programming
Large-scale serving
GPU inference engines
System optimizations

Job description

At Atlassian, a Senior ML System Engineer on the AI & ML Platform’s Inference Team designs and optimizes large-scale model serving systems, covering distributed infrastructure, batching, and low-level GPU kernels.

You will work with senior ML engineers to fine‑tune open-source LLMs, implement CI/CD for inference, and push performance to meet billions of requests daily across multiple zones. Mountain View offers flexible work options with hybrid arrangements.

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