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.
We’re looking for an ML infrastructure engineer to design and build the core systems that enable scalable, efficient training of large models for deployment and research. Your goal is to make experimentation and training at Epsilon Health fast and reliable to ensure our research teams can focus on science rather than system bottlenecks.
Sitting in the Engineering team and working closely with research, you'll own 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.
Partner directly with researchers to deeply understand their workflows, then anticipate and design for how those needs will change
Build a 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 high-throughput data loading and preprocessing that keeps GPUs saturated on large volumetric and multimodal datasets.
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.
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.
6+ years of experience designing, building, and operating large-scale distributed systems or infrastructure in production
Have 2+ years of experience building ML infrastructure or systems in production
Strong Python skills and expertise in PyTorch or JAX
Experience and familiarity with the compute, tooling, and workflow needs of large-scale machine learning research
Experience building infrastructure or platforms specifically for research or machine learning workflows
Deep experience building and operating Kubernetes and cloud infrastructure at scale
Experience with distributed training at scale (FSDP, DeepSpeed, or Megatron-style parallelism) and the systems concerns of keeping large GPU jobs efficient
Prior experience as a technical lead or mentor for other engineers
Experience operating in a startup or startup-like environment, i.e. a small, fast-moving team with high autonomy
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 optimizing inference and serving for large models: batching, KV/prompt caching, quantization, and low-latency, high-throughput sampling.
Experience optimizing training performance: parallelism, distributed communication, mixed/low precision, and utilization.
Experience building internal training or experimentation platforms used by research teams, supporting A/B testing and experimentation workflows
Familiarity with vision-language models (VLMs) or multimodal architectures
The anticipated annual base salary for this position is up to $250,000. This range does not include any other compensation components or other benefits for which an individual may be eligible. The actual base salary offered depends on a variety of factors, which may include as applicable, the qualifications of the individual applicant for the position, years of relevant experience, specific and unique skills, level of education attained, certifications or other professional licenses held, and the location in which the applicant lives and/or from which they will be performing the job.
Skills Required
- 5+ years building ML infrastructure, data pipelines, or ML systems in production
- Strong Python skills
- Expertise in PyTorch or JAX
- Experience with distributed training at scale (FSDP, DeepSpeed, or Megatron-style parallelism)
- Hands-on experience with data pipeline technologies (e.g., Spark, Airflow, BigQuery, Snowflake, Databricks, Chalk) and schema design
- Experience with distributed systems and cloud infrastructure (AWS or GCP)
- Experience with containerization (Docker and 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
- Experience building reinforcement learning training infrastructure (rollout generation, reward-model serving, experience collection)
- Experience with high-performance inference and serving (vLLM, SGLang, TensorRT, or Triton)
- Experience building internal training or experimentation platforms used by research teams
- Experience supporting A/B testing and experimentation workflows, canary deployments, and monitoring
- Familiarity with vision-language models 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
What We Do
Epsilon Health exists to solve the looming global radiology crisis before it reshapes patient care. Radiology underpins nearly every medical specialty, yet it is one of the most strained parts of the healthcare system. We’re rethinking how imaging and interpretation work to make radiology faster, more reliable, and future-proof.
