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Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformKubernetesNoSQLNumpyPandasPythonPyTorchScikit-LearnSQL
About the role
Key responsibilities & impact- Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference.
- Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies.
- Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques.
- Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation.
- Apply machine learning design patterns to build modular, reusable, and production-ready models.
- Collaborate with data engineers to develop high-performance data pipelines for training and inference.
- Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes.
- Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.
Requirements
What you’ll need- Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch.
- Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques.
- Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows.
- Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL).
- Knowledge of cloud-based ML deployment and infrastructure management.
- Ability to implement real-time and batch inference pipelines efficiently.
- Strong analytical and problem-solving skills to translate business needs into scalable ML solutions.
- Eagerness to work in a fast-paced environment and continuously refine ML processes for efficiency and accuracy.
Benefits
Comp & perks- Top of the market compensation for top performers
- Comprehensive health, dental, and vision benefits plan
- $1,500 annual learning & home-office stipend
- $1,000 annual wellness stipend
- Monthly Lunch Stipend
- Commuter Benefits
- Paid Parental leave
- 20 annual PTO days + unlimited sick days
- Quarterly Team Gatherings
- In Office Amenities
ATS Keywords
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Hard Skills & Tools
machine learningPythonMLOpsfeature engineeringhyperparameter tuningmodel versioningdata manipulationsupervised learningunsupervised learningreal-time inference
Soft Skills
analytical skillsproblem-solving skillscollaborationadaptabilitycommunication