Senior Software Engineer, Machine Learning
Tasks
- Architect ML model lifecycle
- Automate data processing workflows
- Build CI/CD pipelines
- Build production inference services
- Deploy models with canary and blue green
- Design feature store strategy
- Engineer ETL ELT data pipelines
- Implement MLOps framework
- Implement event-driven architecture
- Optimize LLM inference performance
- Provision cloud infrastructure with Terraform
- Set up model training versioning and deployment
- Track experiments and evaluate models
- Tune Python for low latency serving
- Use quantization and compilation techniques
Perks/Benefits
- 401k match
- Employee affinity groups
- Family leave
- Fully-paid health care
- Mental wellness programs
- Parental leave
- Physical wellness programs
- Tuition assistance
- Volunteer opportunities
Skills/Tech-stack
A/B | A/B Testing | AWS | AWS Lambda | Airflow | Amazon Bedrock | Amazon EC2 | Amazon EMR | Amazon RDS | Amazon Redshift | Amazon S3 | Amazon SageMaker | Anomaly Detection | Axum | B testing | Blue/green | Blue/green deployments | Boto3 | CD pipelines | CI/CD | CI/CD pipelines | Canary deployments | Compilation | DBT | Dagster | Docker | Evaluation | Experiment tracking | FastAPI | Feature Store | GRPC | Google BigQuery | Graph Neural Networks | Infrastructure as Code | Jenkins | Kafka | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Model versioning | Neural Networks | Python | Quantization | Recommendation Systems | SQL | Series forecasting | Snowflake | Spark | Spring Webflux | Terraform | Time Series | Time Series Forecasting | Weights and Biases | “as-code”
Education
N/A
Regions
Countries
States
Cities
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