Original listing text, shown exactly as published by the company.
The Role
We are hiring a Machine Learning Engineer to build, deploy, and maintain the ML infrastructure that powers Oscilar. You will own the systems that take models from development to production, and you will work closely with data scientists, platform engineers, and product teams to integrate ML capabilities throughout the Oscilar platform.
Depending on your level of experience, this role can be filled at the Senior or Staff level. We are flexible on title and scope for the right person.
What You Will Do
- Scale and optimize existing ML systems. Improve the performance, reliability, and cost-efficiency of our current ML infrastructure, including feature stores, model serving, and orchestration pipelines.
- Build reproducible, automated ML pipelines. Design and operate the pipelines that power model training, deployment, and monitoring across the platform — so models ship reliably and repeatably, not as one-off integrations. Partner with data scientists to make low-latency production deployment a paved path.
- Build new ML infrastructure. Design and implement new components of our ML stack as the platform grows, with a focus on scalability, modularity, and developer experience.
- Set ML engineering standards. Help define best practices for model deployment, monitoring, and lifecycle management. Mentor teammates and raise the bar across the organization.
- Own production reliability. Be responsible for the uptime, performance, and correctness of ML systems serving real-time, business-critical decisions.
What We Are Looking For
Required
- 4+ years of experience building and maintaining production ML infrastructure.
- Strong software engineering fundamentals, with experience designing distributed systems and writing high-quality, maintainable code.
- Hands-on experience with the full ML lifecycle in production: feature engineering and serving, model deployment, monitoring, and retraining.
- Proficiency in Scala and Python, with hands-on experience building data and ML workloads on distributed processing frameworks such as Spark and Flink.
- Experience operating systems at scale, including performance tuning, observability, and incident response.
- Strong communication skills and the ability to collaborate effectively across data science, engineering, and product teams.
- Significant experience building and operating workloads on AWS.
Strongly Preferred
- Experience building ML infrastructure for fintech applications
- Track record of scaling ML systems through significant growth in traffic, models, or feature volume.
Nice to Have
- Prior experience as an ML engineer at a startup.
