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Vllm Jobs (NOW HIRING)

Inferact

Head of Engineering

San Francisco, CA · On-site

Overview Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM ...

Doist

Head of Engineering

San Francisco, CA · On-site

$260 - $380/hr

Overview Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM ...

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Inferact

Product Marketing Manager

San Francisco, CA · On-site

$181K/yr

Overview Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM ...

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Vllm information

How does a vLLM engineer typically collaborate with data scientists and product teams during model deployment?

VLLM Engineers work closely with data scientists to understand the specific requirements and fine-tuning needs of large-scale language models. They are often responsible for integrating these models into production systems, ensuring scalability and efficiency. Collaboration with product teams is crucial to align model capabilities with user needs and to troubleshoot real-world application challenges. Frequent communication and agile workflows are common, as updates or optimizations may be needed rapidly based on feedback from both teams.

What is a vLLM?

VLLM stands for 'Virtual Large Language Model.' In the context of AI development, VLLM professionals work with optimized inference engines for large language models, enabling faster and more efficient deployment of AI models in production environments. Their responsibilities often include integrating LLMs into applications, optimizing model performance, and ensuring scalability for real-time use cases. They may also collaborate with data scientists and engineers to manage resources and streamline AI workflows.

What is the difference between Vllm vs Data Analyst?

AspectVllmData Analyst
Required CredentialsTypically requires knowledge of machine learning, AI, and programming languages like Python or RRequires skills in statistics, Excel, SQL, and data visualization tools
Work EnvironmentOften in tech companies, research labs, or AI-focused teamsCommonly in business, finance, healthcare, and marketing sectors
Industry UsageEmerging role in AI and machine learning projectsEstablished role in data-driven decision making
Common Search/ComparisonVllm vs Data Analyst

The main difference between Vllm and Data Analyst lies in their focus and skill set. Vllm professionals specialize in AI and machine learning models, often working in tech environments, while Data Analysts focus on interpreting data to inform business decisions. Both roles require analytical skills, but Vllm roles demand programming and AI expertise, whereas Data Analysts emphasize statistical analysis and data visualization.

What are the key skills and qualifications needed to thrive as a machine learning engineer working with vLLM, and why are they important?

To thrive as a Machine Learning Engineer specializing in vLLM (a high-throughput LLM inference library), you need a strong understanding of machine learning principles, deep learning frameworks, and experience with Python programming. Familiarity with tools like PyTorch, CUDA, distributed computing, and cloud platforms, as well as relevant certifications in ML or data engineering, is highly valuable. Strong problem-solving, collaboration, and communication skills are essential for optimizing model performance and integrating with cross-functional teams. These capabilities ensure effective deployment and scaling of large language models, driving innovation and efficiency in AI applications.
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Vllm jobs near you
Infographic showing various Vllm job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 95% Full Time, 1% Part Time, and 3% Contract. Highlights an 81% Physical, 5% Hybrid, and 14% Remote job distribution.

Member of Technical Staff, AMD GPU Performance Engineering

Inferact

San Francisco, CA • On-site

$200 - $400/hr

Other

Medical, Dental, Vision, Retirement

Re-posted 14 days ago

Job description

Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.

About the Role

We're looking for an AMD GPU performance engineer to make vLLM a first‑class inference engine across the AMD accelerator ecosystem. You'll build and optimize AMD GPU backends, kernels, runtime paths, and benchmarking infrastructure using ROCm, HIP, Triton, CK, AITER, and related tooling so vLLM can deliver frontier inference performance on AMD GPUs.

You'll work at the boundary of inference systems, kernels, compilers, and hardware architecture, improving performance‑critical paths such as attention, GEMM, sampling, KV cache, and communication‑heavy operations. Your work will help make AMD GPU support in vLLM usable, fast, benchmarked, and maintainable.

Skills and Qualifications

Minimum qualifications:

  • Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar.
  • Hands‑on experience optimizing AMD GPU workloads using ROCm, HIP, Triton, CK, AITER, or similar AMD ecosystem tools.
  • Deep understanding of AMD GPU execution, memory behavior, toolchains, kernel performance, and backend‑specific performance constraints.
  • Experience optimizing ML kernels or inference paths such as attention, GEMM, sampling, KV cache, fused kernels, or communication‑heavy runtime paths.
  • Strong performance profiling and benchmarking skills, with the ability to use measurements, hardware counters, correctness tests, and reproducible benchmarks to guide optimization work.

Preferred qualifications:

  • Experience with vLLM, SGLang, TensorRT‑LLM, ROCm‑based serving, or other LLM inference systems.
  • Familiarity with batching, KV cache, decoding, serving tradeoffs, and backend performance constraints in production inference systems.
  • Experience with compiler and kernel technologies such as Triton, MLIR, LLVM, CK, AITER, HIP, or other kernel DSLs and backend libraries.
  • Knowledge of quantization methods such as INT8, FP8, mixed precision, or AMD hardware‑specific numeric formats, including accuracy and performance tradeoffs.

Bonus points if you have:

  • Contributed to vLLM, ROCm, HIP, Triton, CK, AITER, PyTorch, compiler projects, or other open‑source ML infrastructure.
  • Built AMD GPU benchmarking infrastructure or automated performance regression detection for accelerator workloads.
  • Worked directly with AMD, accelerator platform teams, or early‑access programs to ship backend, compiler, or inference performance improvements.
Logistics
  • Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
  • Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
  • Visa sponsorship: We sponsor visas on a case‑by‑case basis.
  • Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
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