01Overview
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. .
