[About the job]
Our team is looking for experienced engineers to work on exciting technical problems for marketing AI. On the 8ndpoint team, you’ll have the opportunity to own and drive projects that help us scale the platform. As a Machine Learning Engineer, you’ll build out our machine learning platform and machine learning infrastructure, including model hosting, model inference, deep learning, and modeling R&D. You’ll have the full support of engineering to ship greenfield solutions, and you’ll also advise our product managers on how we can best deliver the power of AI to our customers.
MoBagel has an extraordinarily open and relaxed work culture. There’s immense freedom to work on what you think is most important, and generous support for personal development. We’re a small and rapidly growing team with great work-life balance, generous remote work policy, open and supportive teammates, and free food. Come meet the team!
Since this role is highly cross-functional, there are ample opportunities (and support) for diving into DevOps, MLOps, data engineering, and machine learning modeling projects.
1. Architect and build production ML systems, plus monitoring, debugging, and alert systems.
2. Design and build machine learning services and APIs at scale.
3. Develop CI/CD and integration testing pipelines in conjunction with data scientists and data engineers.
4. Evangelize and encourage best software development practices through documentation, code reviews, and sharing.
5. Consult on data infrastructure, data pipeline development, and scalability.
1. 3+ years of professional software development experience.
2. Strong technical communication skills.
3. Demonstrated experience with designing scalable architectures as solutions to business problems, or technical leadership.
4. Experience with CI/CD pipelines and integration testing.
5. Experience on a data or machine learning team in a production environment.
[Nice to Have]
1. Prior experience working with feature stores or real time model inference systems is a massive plus.
2. Experience with deep learning infrastructure in a production environment.
3. Experience with event streaming architectures and Protobuf or other serialization protocols.
4. Comfortable with distributed data systems fundamentals (e.g., CAP theorem).
5. Familiarity with SQL and data engineering principles.
6. Familiarity with cloud services (e.g., GCP, AWS, Azure).
We prioritize candidates that can quickly learn new technologies over domain knowledge, but the following is a snapshot of what our day-to-day looks like:
1. Development: Python, Java, Scala
2. Machine Learning: Python (Pandas, sklearn, xgboost, etc.)
3. Orchestration: Airflow, Kubernetes, Docker
4. GCP: BigQuery, GKE, Artifact Registry, Cloud SQL, Pub/Sub
5. Frontend: React, TypeScript
6. DevOps: ArgoCD, Rancher/Longhorn
7. VCS & CI/CD: Gitlab