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About the Role

We are seeking a Software Engineer to build the ML infrastructure and data systems necessary for training and deploying state-of-the-art models for medical imaging. This role involves owning data pipelines, distributed training infrastructure, and inference/evaluation systems, requiring a blend of ML systems, data engineering, distributed training, and production deployment expertise.

Responsibilities

• Build and optimize distributed training infrastructure for foundation models on large-scale medical imaging, including parallelism and checkpointing for volumetric CT/MR training.
• Build the reinforcement learning training stack, enabling large-scale online, multi-reward RL.
• Build high-throughput data loading and preprocessing to keep GPUs saturated on large volumetric and multimodal datasets.
• Design and implement robust data pipelines for collecting, processing, and storing large-scale multimodal medical imaging data.
• Build centralized data storage solutions with standardized formats for efficient retrieval and training.
• Partner with researchers to prototype new ideas and translate them into production-ready code, owning end-to-end delivery.
• Contribute to production serving and deployment pipelines, including model rollout, canary deployments, and monitoring.

Requirements

• 5+ years building ML infrastructure, data pipelines, or ML systems in production.
• Strong Python skills and expertise in PyTorch or JAX.
• Experience with distributed training at scale (FSDP, DeepSpeed, or Megatron-style parallelism) and optimizing large GPU jobs.
• Hands-on experience with data pipeline technologies (e.g., Spark, Airflow, BigQuery, Snowflake, Databricks, Chalk) and schema design.
• Experience with distributed systems, cloud infrastructure (AWS/GCP), and containerization (Docker/Kubernetes).
• Track record of building scalable data systems and shipping production ML infrastructure.
• Ability to move quickly and handle competing priorities in a fast-paced environment.

Skills

• Python
• PyTorch
• JAX
• Distributed Training
• FSDP
• DeepSpeed
• Megatron-style parallelism
• Data Pipeline Technologies
• Spark
• Airflow
• BigQuery
• Snowflake
• Databricks
• Chalk
• Schema Design
• Distributed Systems
• Cloud Infrastructure
• AWS
• GCP
• Containerization
• Docker
• Kubernetes
• Reinforcement Learning
• High-performance inference
• vLLM
• SGLang
• TensorRT
• Triton
• A/B testing
• Vision-language models (VLMs)
• Multimodal architectures
• Medical Imaging formats
• DICOM
• Healthcare data standards
• MLOps
• Model deployment pipelines
• Privacy-preserving data systems
• HIPAA compliance

Experience Level

• 5+ years

About the Company

• Tackling critical challenges in medical imaging and diagnostics using AI and clinical practice.
• Building technology that directly impacts patient outcomes.
• Possess one of the industry's most comprehensive and diverse medical imaging datasets.
• Have proven product-market fit with a substantial customer pipeline.