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  "job_title": "Research Engineer",
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  "employer_name": "Impax Recruitment",
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  "job_posted_at": "18 days ago",
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  "job_description": "AI Research Engineer (Medical Imaging) – On-site (Manchester, UK)\n\nFull-time | On-site 5 days | Competitive salary + progression\n\nWe're partnered with a fast-growing UK med-tech group building AI that's used in real clinical workflows.\n\nThe team is in an R&D build phase and now needs 1–2 AI Research Engineers who can take work beyond the research stage - turning models into reproducible, reliable, production-ready systems.\nThis is not a \"paper-only\" role. You'll work with a small, high-output team and ship applied ML into a product.\n\nWhat you'll do\nBuild and iterate deep learning models for medical imaging (segmentation / classification / detection).\n\nOwn the \"after research\" phase:\nturn experiments into clean, reproducible, testable code.\nDesign robust evaluation (data splits, leakage prevention, metrics) and improve model performance.\nWork with real-world clinical datasets (messy, evolving) and implement privacy-safe data handling.\nPackage models for inference (e.g., TorchScript / ONNX) and collaborate with engineers on serving (HTTP/gRPC).\nContribute to tooling that makes the team faster: experiment tracking, dataset/version control, model registry.\n\nWhat we're looking for (must-haves)\nStrong hands-on PyTorch (training loops, debugging, optimisation).\nSolid applied computer vision experience (segmentation/classification/detection).\nEvidence you can take models from notebook → production-minded implementation:\nreproducibility, structured codebases, evaluation discipline, versioning.\nComfort working on-site with a fast-moving team (high ownership, high pace).\n\nNice-to-haves (big plus)\nMedical imaging experience (DICOM, CT/MRI/CBCT, volumetric/3D).\nMONAI / nnU-Net / 3D U-Net experience.\nInference optimisation (batching, quantisation, latency awareness).\nFamiliarity with Docker + basic deployment concepts (APIs, gRPC, monitoring).\nAnnotation/clinical tooling exposure (3D Slicer, ITK-SNAP).\n\nWhat you'll get\nHigh ownership in an R&D team building real clinical AI (not \"research theatre\").\nFast progression and direct access to technical leadership.\nA team environment that values execution, speed, and impact.",
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  "job_employment_type": "Full–time",
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  "job_posted_at_timestamp": 1772496000,
  "job_posted_at_datetime_utc": "2026-03-03T00:00:00.000Z"
}