Role overview

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
Collaborative, product-focused engineering culture
Competitive compensation and benefits

Compensation

Palo Alto, CA:: $180,000 – $230,000 base + bonus, equity, and benefits
Pune, India:: Competitive local market compensation

MachineLearningEngineer