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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 you're 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 notebooks, and reproducibility tooling. Partner closely with scientists on rapid model iteration , ablations, and experimental design. Participate in internal "ML performance tiger teams" to squeeze maximum throughput from models and data pipelines. Why This Role Appeals to Top-Tier ML Systems Engineers You get to build the entire foundational stack behind frontier multimodal models. Rare opportunity to combine 3D infrastructure , LLM agents , medical workflows , and distributed systems . Direct collaboration with researchers working on CLIP-style models, Chitrarth-type VLMs, document foundation models, and 3D multimodal architectures. Massive technical scope with freedom to propose current tools, new pipelines, new optimization strategies. Direct impact: your work will enable clinical-grade AI systems used in radiology and beyond. What We're Looking For Strong engineering experience with PyTorch , JAX , or DeepSpeed , plus hands-on distributed training expertise. Deep understanding of GPU internals , CUDA kernels, NCCL, memory profiling, and high-performance data pipelines. Experience building large-scale ML pipelines , especially for multimodal or heavy-data workloads (video, 3D, imaging). Familiarity with cloud or on-prem HPC scheduling: Slurm, Kubernetes, Ray, etc. Proficiency in Python + C++/CUDA; strong command of Linux systems. Ability to collaborate deeply with researchers, contribute ideas, and own end-to-end engineering projects. Nice to Have Experience with 3D data (MRI/CT, LiDAR, voxels, meshes, NeRFs). Exposure to vector search (FAISS, Milvus, Annoy) and embedding retrieval systems. Experience with agent frameworks, LLM serving, or multimodal inference pipelines. Contributions to open-source ML systems or performance optimization libraries. Background in healthcare/medical imaging pipelines (DICOM, PACS, segmentation workflows). What We Offer Competitive compensation. World-class compute access. Opportunity to build the core infrastructure for India's first 3D multimodal foundation model . Close collaboration with researchers, clinicians, and product teams. Autonomy, ownership, and the chance to shape the technical architecture from the ground up. .
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.