ML Engineer

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  "active_status": "active"
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  "language": "en",
  "location": {
    "raw": "Dresden, DE (primary site)",
    "city": "Dresden",
    "region": "DE",
    "country": "United States",
    "is_remote": false,
    "confidence": 0.9
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{
  "id": "2246552",
  "name": "ML Engineer",
  "office": "Dresden, DE (primary site)",
  "keywords": [],
  "schedule": "full-time",
  "createdAt": "2025-07-14T19:38:09+00:00",
  "seniority": "experienced",
  "department": "Software",
  "occupation": "general_and_other_engineering",
  "subcompany": null,
  "employmentType": "permanent",
  "jobDescriptions": [
    {
      "name": "About the Role",
      "value": "As an ML Engineer at SEMRON, you will be responsible for developing the training infrastructure that enables models to run efficiently on our novel analog in-memory compute platform. A core part of this work is designing a geo-distributed Quantization-Aware Training (QAT) framework that allows the machine learning community to collectively contribute compute resources, enabling them to quantize their favorite models and make them compatible with SEMRON’s hardware."
    },
    {
      "name": "What you will do:",
      "value": "<ul><li>Design and implement a <strong>geo-distributed QAT system</strong> for preparing models for analog inference</li><li>Build collaborative training and tooling infrastructure that allows users to contribute GPUs and quantize models together</li><li>Translate new quantization and <strong>analog-aware training methods</strong> into robust engineering components</li><li>Collaborate with researchers to integrate their algorithms into a production-grade pipeline</li></ul>"
    },
    {
      "name": "What you should bring in:",
      "value": "<ul><li>A Master’s degree, PhD, or a personal project or open-source contribution that<strong> clearly demonstrates strong engineering skills and a solid understanding of ML tooling</strong></li><li>Fluency in <strong>Python and PyTorch</strong></li><li>Understanding of<strong> training workflows</strong> and <strong>practical experience with model optimization</strong></li><li>Ability to architect and maintain scalable systems, with clean code and clear interfaces</li><li>A collaborative mindset and comfort working across software, research, and hardware domains</li></ul>"
    },
    {
      "name": "Helpful but not required:",
      "value": "<ul><li>Prior experience with QAT or other model compression techniques</li><li>Familiarity with projects like<strong> DiLoCo, SWARM</strong>, or other decentralized or peer-to-peer learning systems</li><li>Background in <strong>compiler stacks</strong>, <strong>graph transformations</strong>, or <strong>model deployment tooling</strong></li><li>Contributions to open-source <strong>ML infrastructure</strong> or <strong>research software</strong></li></ul>"
    }
  ],
  "occupationCategory": "engineering",
  "recruitingCategory": "ML"
}