About the Role
As a Senior Engineer in our ML Platform squad, you’ll drive the development of a brand-new ML Platform for the whole Engineering organization at Super Technologies. You will be enabling adoption of ML and AI across all the teams and tribes in our org, and your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.
This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.
What you’ll be doing:
- Design and build scalable backend services and APIs to support ML experimentation, deployment, and monitoring.
- Re-architect and rebuild existing data pipelines to handle real-time data processing and support production ML models.
- Develop tools that make it easier for ML engineers to design, test, and deploy ML pipelines.
- Ensure platform services are robust, secure, and meet high standards for performance and reliability.
- Collaborate with ML engineers, data scientists, and product teams to understand their needs and translate them into platform capabilities.
- Drive adoption of ML best practices and promote reusability of tools across teams.
- Contribute to the overall architecture and vision of the ML platform, ensuring it can scale with the company’s growth.
- Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.
We’re looking for someone with:
- 7+ years of professional experience as a Backend Engineer, Software Engineer, or similar role.
- Strong programming skills in Python.
- Proven experience designing and building distributed systems and APIs at scale.
- Hands-on experience with real-time data processing frameworks (e.g., Kafka, Flink, Spark Streaming, Pulsar).
- Experience working with cloud platforms (AWS, GCP, or Azure) and containerization/orchestration (Docker, Kubernetes).
- Strong understanding of data modeling, storage systems, and streaming/processing architectures.
- Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
- Familiarity with ML model lifecycle management (training, deployment, monitoring).
- Excellent collaboration and communication skills, with the ability to work cross-functionally.
Bonus point for:
- Experience building or working on ML platforms or MLOps frameworks.