About this role
Job title: Senior Machine Learning Engineer, MLOps
About the Role We are looking for a Senior Machine Learning Engineer, MLOps to help operationalize and scale our machine learning systems. This engineering-focused role centers on building the workflows, infrastructure, and processes that enable ML to move from research into reliable production systems. You will partner closely with research-oriented ML teammates to turn their work into scalable, maintainable, and cost-effective production systems. This is not a research-first role, but one for someone excited by the systems, tooling, and operational side of machine learning.
What You'll Do
- Build and maintain the workflows and infrastructure that support the end-to-end ML lifecycle.
- Partner with researchers and ML practitioners to productionize models and enable faster iteration.
- Design, build, and improve data pipelines and training pipelines.
- Improve data processing, annotation workflows, and ML system efficiency.
- Deploy and maintain the background systems that support model training and inference.
- Build tooling and processes for monitoring model performance, system reliability, and operational health.
- Improve the scalability, observability, and reproducibility of ML systems.
- Optimize ML infrastructure for speed, reliability, and cost-efficiency.
- Identify bottlenecks in the ML workflow and automate or streamline manual processes.
- Help establish best practices around ML operations, deployment, and system performance.
What We're Looking For
- Several years of experience in machine learning engineering, MLOps, ML infrastructure, data engineering, or backend/platform engineering in ML environments.
- Experience supporting ML systems end to end, from model handoff through deployment and monitoring.
- Strong experience building and owning data pipelines, training pipelines, or other production workflows that support ML.
- Experience working closely with researchers, data scientists, or ML practitioners to productionize models.
- Strong software engineering fundamentals and experience building production systems.
- Experience with monitoring, debugging, and improving production ML or data systems.
- A track record of improving reliability, scalability, speed, and/or cost efficiency in ML systems.
- Comfort operating in a fast-moving, startup-style environment with a high degree of ownership.
Compensation & Benefits
- Competitive salary and equity in a high-growth startup.
- Flexible PTO, take what you need.
- Medical, dental, and vision coverage.
- Great startup culture, including company off-sites.
- High-achieving team, including ex-Amazon engineers and alumni of Bain, BCG, Goldman Sachs, and more.