Role OverviewWe're seeking a
Software Engineer to build the
ML infrastructure and data systems that let our research team train and ship state-of-the-art models for medical imaging. Sitting in the
Engineering team and working closely with research, you'll own the data pipelines that unify live production traffic with offline datasets, the distributed training and reinforcement learning infrastructure our foundation-model and post-training work runs on, and the inference and evaluation systems that carry models from experimentation into production. This role requires someone who can move fluidly between ML systems, data engineering, distributed training, and production deployment, and who measures success by how quickly the research team can iterate.
Key Responsibilities- Build and optimize distributed training infrastructure for foundation models on large-scale medical imaging, including the long-context parallelism and checkpointing that volumetric CT/MR training demands.
- Build the reinforcement learning training stack (high-throughput rollout generation, reward-model serving, and experience collection), enabling the research team to run online, multi-reward RL at scale.
- Build high-throughput data loading and preprocessing that keeps GPUs saturated on large volumetric and multimodal datasets.
- Design and implement robust data pipelines to collect, process, and store large-scale multimodal medical imaging data from both production traffic and offline sources.
- Build centralized data storage solutions with standardized formats (e.g., protobufs) that enable efficient retrieval and training across the organization.
- Partner with researchers to prototype new ideas and translate them into production-ready code, owning end-to-end delivery from experimentation through deployment and monitoring.
- Contribute to production serving and deployment pipelines - model rollout, canary deployments, and monitoring - alongside the backend team.
Qualifications- 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 the systems concerns of keeping large GPU jobs efficient
- 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
Preferred Qualifications- Experience building reinforcement learning training infrastructure: rollout generation, reward-model serving, or online/off-policy learning systems
- Experience with high-performance inference and serving (vLLM, SGLang, TensorRT, or Triton) for both training-time rollouts and production
- Experience building internal training or experimentation platforms used by research teams
- Experience supporting A/B testing and experimentation workflows, including canary deployments and monitoring statistical significance
- Familiarity with vision-language models (VLMs) or multimodal architectures
- Experience with medical imaging formats (DICOM) and healthcare data standards
- Familiarity with MLOps practices and model deployment pipelines
- Experience with privacy-preserving data systems and HIPAA compliance
