Related skills
java docker sql python kubernetes📋 Description
- Partner with product, UX, and tech to translate requirements into ML problems.
- Design, implement, and maintain scalable ML solutions in production.
- Build reproducible ML workflows for data prep, training, and inference.
- Implement monitoring to improve data quality, model performance, and latency.
- Collaborate cross-functionally to deliver resilient ML-powered services.
- Own operational excellence: SLAs, on-call, and post-mortems.
🎯 Requirements
- Strong ML foundation (stats/prob/optimization) with real-world use.
- 5+ years building, deploying, and operating data/ML systems in production.
- Proficient in Python, Java, SQL; strong system design and testing.
- Hands-on with Airflow/Kubeflow; cloud platforms (SageMaker, Snowflake).
- ML lifecycle/MLOps: MLflow, SageMaker; LangChain; eval/observability.
- Docker/Kubernetes, Argo CD; AWS/GCP/Azure; data modeling & distributed systems.
🎁 Benefits
- Competitive pay and comprehensive benefits.
- Generous time off, parental and wellness leave.
- Healthcare, 401(k), and retirement savings.
- Equity eligibility and corporate bonus plans.
- Flexible, remote-friendly culture.
- Support for volunteering and community impact.
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