Build and own the infrastructure and pipelines for machine learning models in production at a fast-growing startup.
Posted by employer 1 month ago
First seen on Joblaze 1 week ago
Last verified on the company career page 5 days ago
Skills & Technologies
Requirements
- Experience
- 5–7 years
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In this role, the Senior MLOps Engineer is responsible for developing and maintaining the infrastructure and pipelines essential for training and deploying machine learning models in a production environment. Key skills include proficiency in Python, cloud technologies, Docker, Kubernetes, and CI/CD practices, alongside experience in managing production ML systems and GPU workloads. This position is ideal for candidates with 5 to 7 years of relevant experience, particularly those who thrive in fast-paced startup settings. The role emphasizes collaboration with cross-functional teams to enhance the scalability and reliability of the ML platform.
Joblaze insights
- Listed 9 days ago on Joblaze — tracked directly from Wonderful's career page.
- Wonderful has 153 other open roles including GTM Public Sector, Forward Deployed Engineer, Conversation Experience Designer.
- 6527 active Python roles on Joblaze right now.
- 2568 active Kubernetes roles on Joblaze right now.
- 1032 active Docker roles on Joblaze right now.
- Typical Python salary we track runs $125,000–$190,800 (median $160,000, across 3229 roles). See Python salary trends
Quick facts
- How much experience is required?
- 5–7 years of relevant experience for this Senior MLOps Engineer role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: CI/CD, Docker, Kubernetes, Python, infrastructure as code, production observability.
- What seniority level is this role?
- Wonderful targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Senior MLOps Engineer role at Wonderful.
From the original posting
Role Overview
Build and own the infrastructure and pipelines used to train, evaluate, package, deploy, and operate machine learning models in production.
Develop reliable MLOps capabilities across experiment tracking, model and data versioning, reproducibility, orchestration, automated testing, monitoring, and controlled model rollouts.
Partner with Data Science, Engineering, and Infrastructure teams to productionize models and continuously improve the scalability, reliability, observability, and cost efficiency of our ML platform.
What We’re Looking For
5–7+ years of experience in Machine Learning Engineering, MLOps, ML Infrastructure, Platform Engineering, or a related production engineering role.
Strong hands-on experience with Python, cloud infrastructure, Docker, Kubernetes, CI/CD, infrastructure-as-code, workflow orchestration, and production observability.
Proven experience building and operating production ML systems, including training pipelines, experiment tracking, model registries, versioning, monitoring, data-quality checks, staged deployments, and rollback mechanisms.
Experience managing GPU-based training workloads and/or distributed training infrastructure, and cloud cost optimization.
Experience working in a fast-growing startup, with the ability to operate in a dynamic, fast-paced environment.