> Markdown version of [/jobs/ext/1373370-ml-infrastructure-engineer](https://www.wearedevelopers.com/jobs/ext/1373370-ml-infrastructure-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# ML Infrastructure Engineer

- **Company:** Yobi & Tylo  LLC
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Large Language Models, Machine Learning Operations, TensorRT
- **Published:** July 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=8935f6e8d4390550

## About the Role

* Shipped at least one production inference stack on H100s or A100s.
 * Read a recent vLLM PR for fun.
 * Strong opinions about speculative decoding.

## Description

We serve inference at $/token margins that don't tolerate sloppy stacks. You'll own the serving layer - vLLM, TensorRT-LLM, Triton - and the benchmarking discipline that keeps it honest., * Serving-stack selection per workload (continuous batching vs. static, KV cache strategy, paged attention).

 * Quantization (FP8, AWQ, GPTQ) and the eval harness that proves the trade-offs.
 * The InferenceBench-style benchmarks that compare our serving against the field.


## Related Videos

- [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms)
- [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated)
- [Creating Industry ready solutions with LLM Models](https://www.wearedevelopers.com/videos/899-creating-industry-ready-solutions-with-llm-models)
- [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops)
- [Unveiling the Magic: Scaling Large Language Models to Serve Millions](https://www.wearedevelopers.com/videos/1619-unveiling-the-magic-scaling-large-language-models-to-serve-millions)
- [Lies, Damned Lies and Large Language Models](https://www.wearedevelopers.com/videos/1231-lies-damned-lies-and-large-language-models)

## Related Articles

- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market)
- [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)
- [Dev Digest 210: AI Agents Are Go! Is MCP Dead? LLMs Crack Anonymity](https://www.wearedevelopers.com/magazine/709-dev-digest-210-ai-agents-are-go-is-mcp-dead-llms-crack-anonymity)