Epsilon Health
Software Engineer - ML Infrastructure
San Francisco, CA
Sponsorship not specifiedDetected 1 day ago
PythonDistributed SystemsBigQuerySnowflakeDatabricksAWSGCPCloud PlatformsDockerKubernetesMachine LearningPyTorchSparkAirflowData EngineeringMLOpsA/B TestingComplianceHIPAARadiologyResearch
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About the role
- 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.
Responsibilities
- Build high-throughput data loading and preprocessing that keeps GPUs saturated on large volumetric and multimodal datasets.
- 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.
Requirements
- 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)
- Ability to move quickly and handle competing priorities in a fast-paced environment
- 5+ years building ML infrastructure, data pipelines, or ML systems in production
- Track record of building scalable data systems and shipping production ML infrastructure
- Experience building reinforcement learning training infrastructure: rollout generation, reward-model serving, or online/off-policy learning systems
- Experience building internal training or experimentation platforms used by research teams
- Familiarity with vision-language models (VLMs) or multimodal architectures
- Experience with medical imaging formats (DICOM) and healthcare data standards
Nice to have
- Experience with high-performance inference and serving (vLLM, SGLang, TensorRT, or Triton) for both training-time rollouts and production
- Experience supporting A/B testing and experimentation workflows, including canary deployments and monitoring statistical significance
- Familiarity with MLOps practices and model deployment pipelines
- Experience with privacy-preserving data systems and HIPAA compliance
Benefits
- 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.
- Design and implement robust data pipelines to collect, process, and store large-scale multimodal medical imaging data from both production traffic and offline sources.
- We'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.
Company info
- We're tackling one of healthcare's most critical challenges in medical imaging and diagnostics.
- Our company operates at the intersection of cutting-edge AI and clinical practice, building technology that directly impacts patient outcomes.
- We've assembled one of the industry's most comprehensive and diverse medical imaging datasets and have a proven product-market fit with a substantial customer pipeline already in place.
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This listing is sourced directly from Epsilon Health's careers page and normalized into a canonical job model.