About the company
Our client is a healthcare technology company developing AI-enabled imaging tools. Researchers and engineers work together on production machine learning systems for clinical applications.
The role
Raydar is recruiting for this opportunity through the Paraform network. The position is with our client. Own ML infrastructure across distributed training, reinforcement learning, and production serving. You will establish engineering practices and help researchers translate experiments into reliable systems.
What you'll do
- Build distributed training infrastructure with parallelism and checkpointing.
- Develop reinforcement-learning infrastructure for rollout generation, reward models, and experience collection.
- Partner with researchers to turn experimental workflows into production systems.
- Build data loading and preprocessing pipelines for multimodal datasets.
- Improve model serving, canary deployment, monitoring, and rollout tooling.
Requirements
What we're looking for
- 6+ years of ML infrastructure or distributed systems experience.
- Strong Python and hands-on production ML serving experience.
- Kubernetes and Docker experience with GPU scheduling, autoscaling, and reproducible environments.
- Distributed training expertise in PyTorch or JAX, including FSDP, DeepSpeed, or comparable approaches.
- Experience with RL or online-learning loops, logging, checkpointing, and evaluation.
- Breadth across the ML infrastructure stack plus technical leadership or mentoring experience.
Bonus points
- Startup experience.
- A/B testing and production ML experimentation platforms.
- A computer science or other STEM degree.
Benefits
Compensation and benefits
- Base salary: USD 250,000 to 300,000 per year.
- Equity: Competitive equity.
Location and work model
- San Francisco, California, United States.
- Five days per week onsite.
- Visa transfers and new visa sponsorships are supported.
Skills Required
- 6+ years of ML infrastructure or distributed systems experience
- Strong Python experience
- Hands-on production machine learning serving experience
- Kubernetes and Docker experience, including GPU scheduling, autoscaling, and reproducible environments
- Distributed training expertise in PyTorch, JAX, FSDP, DeepSpeed, or comparable approaches
- Experience with reinforcement learning or online-learning loops, logging, checkpointing, and evaluation
- Breadth across the machine learning infrastructure stack
- Technical leadership or mentoring experience
- Startup experience
- Experience with A/B testing and production machine learning experimentation platforms
- Computer science or other STEM degree
What We Do
Raydar is a talent acquisition and business consulting firm that connects world-class and emerging talent with growing organizations. It supports companies through team development, strategic hiring, and customized growth solutions, helping clients recruit roles such as engineers, product managers, executives, legal counsel, and quantitative traders. Raydar focuses on understanding each organization’s needs, culture, and long-term goals to build high-impact teams.
