Senior Machine Learning Engineer

Build and productionize machine learning at scale with Intuit in Mountain View, CA (onsite). In this role, you’ll architect, code, optimize, and deploy ML models using modern tools and techniques. You’ll also help automate the lifecycle of ML solutions, monitor performance, and continuously improve outcomes for customers. What you’ll do Design and build systems that improve ML scalability, usability, and performance . Collaborate cross-functionally with product managers, data scientists, and engineers to understand and refine machine learning and other algorithms. Communicate results clearly to peers and leaders. Evaluate state-of-the-art technologies and apply them to deliver customer benefits . Work with multiple data sources , refining features from underlying data and building end-to-end pipelines with partners. ML impact areas Model productionalization : Partner with data scientists to take prototype models to customer-scale readiness. Increase training data where needed. Automate training and prediction. Orchestrate data for continuous prediction. Understand training data details and provide metrics to compare models. Model enhancement : Improve prediction performance or reduce training time by working on existing codebases. Conduct exploratory performance-focused work or execute directed improvements based on ideas from data science partners. Machine learning tools : Build project-specific or decoupled tooling to reduce pain points in the data science process. Speed up training. Simplify data processing. Strengthen data management tooling. Key responsibilities in practice You’ll be expected to help architect, implement, optimize, and deploy ML models using the latest industry tools. In addition, you’ll automate, deliver, monitor, and improve machine learning solutions. Success in this role depends on strengths across software development, systems engineering, data wrangling, feature engineering, software testing, and ML systems design . Qualifications Education : BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience. Languages : Scala, Java, Python. Computer science fundamentals : data structures, algorithms, performance complexity, and how computer architecture affects software performance (for example, I/O and memory tuning). Software engineering fundamentals : version control systems (Git, GitHub), production-ready coding practices, and development workflows. ML and data science tool familiarity : SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras. ML concepts : training, validation, testing, and techniques such as classification, regression, and clustering. Data processing and distributed systems : Spark, Hive, Flink. Cloud : AWS and AWS SageMaker. DevOps concepts : CI/CD. Containers : Docker and Kubernetes. Compensation USD 171,000 - 231,500 per year . Tech stack you’ll work with : Scala, Java, Python, SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras, Spark, Hive, Flink, AWS, AWS SageMaker, Docker, Kubernetes. #J-18808-Ljbffr Intuit