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About the Role You will be one of the earliest engineering hires responsible for building the technical backbone that powers our 3D-volume foundation model and the agentic medical AI systems built on top of it. This role blends ML systems engineering, high-performance computing, and foundation-model infrastructure, enabling our research scientists to train and deploy cutting-edge multimodal models at scale. You will design the pipelines, tooling, distributed systems, and evaluation frameworks that make world-class research possibleand usable in clinical settings. If youre the kind of engineer who loves training clusters, PyTorch internals, scalable data loaders, CUDA kernels, model parallelism, and agentic inference systems, this is your role. What You Will Work On Model Training Infrastructure & Systems - Architect and maintain large-scale training pipelines for multimodal foundation models (3D volumes + text). - Implement distributed training using data parallelism, tensor parallelism, pipeline parallelism, and FSDP/ZeRO strategies. - Optimize training performance across A100/H100 clusters, including kernel-level optimizations and memory efficiency tuning. Data & Multimodal Engineering - Build scalable ingestion, preprocessing, and storage systems for 3D medical volumes, DICOM series, voxel grids, and text datasets. - Create multimodal data loaders and augmentation pipelines for high-throughput training. - Work on dataset versioning, weak-label pipelines, and automatic metadata extraction. Model Serving & Agent Runtime - Build and optimize inference runtimes for 3D-aware models and LLM-based medical agents. - Develop robust APIs and service layers for clinical workflows (retrieval, reporting, case summarization, multi-step agent chains). - Implement caching, quantization, batching, vector search, and agent orchestration. Tooling & Collaboration - Develop tools for researchers: experiment launchers, logging/visualization dashboards, model evaluation note .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.