We are seeking Palo Alto, CA or Pune, India to design, build, and deploy machine learning systems that power intelligent features across our B2B SaaS platform. This role focuses on taking models from experimentation to production and ensuring they operate reliably at scale.
You will work closely with Backend Engineers, Product Managers, and Data teams to integrate ML capabilities into real customer workflows.
Key Responsibilities
Model Development & Deployment
Design, train, and evaluate machine learning models for production use cases
Deploy and maintain models in live environments with appropriate monitoring
Improve model performance through iteration, experimentation, and data analysis
Data Pipelines & Feature Engineering
Build and maintain data pipelines for training and inference
Perform feature engineering and data preprocessing for structured and unstructured data
Ensure data quality, reproducibility, and version control
Production ML Systems
Integrate ML models into backend services and APIs
Implement model monitoring, logging, and performance tracking
Handle model retraining, updates, and lifecycle management
Collaboration & Delivery
Partner with Product and Engineering to translate requirements into ML solutions
Work with DevOps to optimize ML infrastructure and deployment pipelines
Participate in code reviews, design discussions, and technical planning
Responsible & Scalable ML
Ensure models meet standards for reliability, fairness, and explainability where required
Follow best practices for security, data privacy, and compliance
Qualifications
4–8+ years of experience in Machine Learning Engineering or Applied ML
Strong proficiency in Python and common ML libraries (e.g., PyTorch, TensorFlow, scikit-learn)
Experience deploying ML models in production environments
Solid understanding of data structures, algorithms, and system design
Experience working with real-world, messy data
Preferred Experience
Experience building ML systems for SaaS or enterprise products
Familiarity with MLOps practices and tooling
Experience with cloud platforms and scalable ML infrastructure
Exposure to NLP, speech, recommendation systems, or predictive modeling
What We Offer
Opportunity to build ML features used in real customer workflows
Ownership of end-to-end ML systems, not just models
