North AmericaFull TimeEngineering
Remotely
awsterraformgoogle cloudairflowcdkmlflowkubeflowweights & biases
Job Description
📋 Description
- Design and implement the ML platform that orchestrates the complete AI lifecycle: data processing
- Develop reliable, scalable ML workflows across cloud, Kubernetes, and automation environments.
- Build ML infrastructure components: model registries, feature stores, experiment tracking, and
- Create developer-facing APIs, CLI tools, and reusable infra for ML workflows accessible to
- Implement CI/CD for ML workflows: continuous retraining, automated testing, standardized packaging
- Apply MLOps best practices: reproducibility, data/model lineage, rollback, monitoring, governance
🎯 Requirements
- 5+ years of professional software engineering experience, including 2+ years in production ML
- Hands-on experience with ML platform components: experiment tracking, model registries, training
- Familiarity with ML orchestration/experiment management tools such as MLflow, Weights & Biases
- Strong experience with cloud-native platforms (AWS/GCP/Azure) and containers, plus Infrastructure
- Experience handling multimodal data (sensor logs, camera streams, behavioral traces) relevant to
- Solid software engineering fundamentals for reliable, production-grade systems.
🎁 Benefits
- Anticipated base salary of $197,000–$307,000 USD, with final compensation based on location
- Competitive total rewards package for full-time employees.
- 401(k) plan with 6% company match.
- Company stock options/equity.
- 100% company-paid medical, dental, and vision insurance for employees.
- Company-paid disability insurance.
