Epsilon Health
Research Engineer - ML Infrastructure
San Francisco, CA
Sponsorship not specifiedDetected 264 days ago
PythonDistributed SystemsBigQuerySnowflakeDatabricksAWSGCPCloud PlatformsDockerKubernetesMachine LearningPyTorchSparkAirflowData EngineeringMLOpsA/B TestingComplianceHIPAARadiologyResearch
About the role
- This role requires someone who can move fluidly between model training, data engineering, ML systems, and production deployment.
Responsibilities
- Build centralized data storage solutions with standardized formats (e.g., protobufs) that enable efficient retrieval and training across the organization.
- Create model inference pipelines and evaluation frameworks that work seamlessly across research experimentation and production deployment.
- Collaborate with researchers to rapidly prototype new ideas and translate them into production-ready code.
- Own end-to-end delivery of ML systems from experimentation through deployment and monitoring.
Requirements
- Strong Python skills and expertise in PyTorch or JAX
- 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 with reinforcement learning training pipelines (e.g., RLHF, reward modeling, or online learning systems)
- Support A/B testing and experimentation workflows for model rollouts, including monitoring statistical significance and managing canary deployments.
- Familiarity with vision-language models (VLMs) or multimodal architectures
- Experience with medical imaging formats (DICOM) and healthcare data standards
Nice to have
- Background in distributed training frameworks (PyTorch Lightning, DeepSpeed, Accelerate)
- Familiarity with MLOps practices and model deployment pipelines
- Experience with privacy-preserving data systems and HIPAA compliance
Benefits
- Build and optimize distributed ML infrastructure for training foundation models on large-scale medical imaging 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.
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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