Proficient in Python and ML libraries (Scikit-learn, LightGBM, PyTorch)
Strong grasp of machine learning algorithms (supervised and unsupervised)
Experience with MLOps tools (MLflow, Kubeflow, SageMaker)
Hands-on with data manipulation (Pandas, NumPy) and databases (SQL, NoSQL)
Knowledge of cloud ML deployment and infrastructure management
Skilled in real-time and batch inference pipeline implementation
Strong analytical and problem-solving abilities
Eager to adapt in a fast-paced environment.
Responsibilities
Design, develop, and deploy end-to-end machine learning pipelines.
Implement MLOps best practices, ensuring efficient model lifecycle management.
Optimize ML models through feature engineering and hyperparameter tuning.
Manipulate structured and unstructured data using Pandas, NumPy, and SQL.
Build modular, reusable ML models using design patterns.
Collaborate with data engineers to create high-performance data pipelines.
Deploy and manage models using cloud platforms and container orchestration tools.
Maintain model performance with continuous monitoring and bias detection.
Benefits
Top of the market compensation for top performers
Comprehensive dental and vision coverage
$1,500 annual learning stipend
$1,000 annual wellness stipend
$250 monthly lunch stipend
Two annual company retreats
Parental leave
Unlimited Paid Time Off (PTO)
Full Job Description
About the Role
We're looking for aMachine Learning Engineer to build and scale high-impact, world-class ML systems. You're passionate about deploying AI solutions, optimizing performance, and driving measurable results. Your work will power critical decisions and shape the future of our technology.
What You'll Do
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.
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.
