Senior Machine Learning Engineer

Conviva•Foster City, CA

32d•Onsite

About The Position

Conviva is the intelligence layer for digital businesses, turning every consumer interaction into outcome-based intelligence—linking engagement patterns across AI agents, apps, websites, and streaming video to real results such as purchases, bookings, and resolved support requests. Powered by its patented Time-State Technology®, the Conviva® Operational Data Platform delivers real-time insights and automation that help leading enterprises grow, improve satisfaction, and build lasting trust. As a Senior Machine Learning Engineer, you will play a key role in advancing our AI initiatives and contributing to the development of our Real-Time Performance Analytics Platform. You will work across a broad range of ML technologies to create innovative, customer-focused solutions. In this role, you’ll collaborate closely with vertical product teams to design and deploy ML models directly within product pipelines. You’ll work with other team members to build scalable ML tooling and infrastructure, combining research, product integration, and data development.

Requirements

PhD in Computer Science, Machine Learning, or a related technical field; OR a Master’s degree in Computer Science or Machine Learning with 2+ years of experience in machine learning engineering or a related field.
Proven track record of owning the ML lifecycle: data pipelines, model training, validation, deployment, monitoring, and optimization.
Strong software engineering background with 5+ years of experience and proficiency in modern languages like Python, Rust, Java, or Scala.
Experience with big data frameworks (e.g., RAY, Spark, Flink) and ML infrastructure (e.g., MLflow, Kubeflow, or custom pipelines).
Strong cross-functional collaboration skills and a bias toward proactive problem-solving, continuous improvement, and system-level thinking.

Nice To Haves

Experience with real-time data processing and analytics.
Familiarity with time series analysis and forecasting techniques.
Background in building AI-powered alerts, anomaly detection, or similar AI driven applications.
Familiarity with cloud-based ML deployment and MLOps best practices.

Responsibilities

Design, develop, and deploy machine learning models that power AI-based features across a large-scale data analytics platform.
Own ML systems end-to-end, from data ingestion and feature engineering to training, deployment, monitoring, and iteration in production environments.
Build and maintain ML infrastructure and MLOps pipelines for scalable, repeatable model development and deployment.
Collaborate with infrastructure and backend engineers and product teams to ensure ML solutions are aligned with platform performance and user needs.
Continuously evaluate model performance using robust testing, monitoring, and alerting systems for data and model drift.
Identify opportunities to improve system robustness and proactively detect data, device, or user level anomalies through intelligent alert mechanisms.
Drive technical decisions, code quality, and best practices for ML engineering within a small, high-impact team.