Java Backend Engineer (Spring Boot/Machine Learning)

Job Description

A personal finance management software company is expanding their Machine Learning capabilities and hiring a Senior Software Engineer who brings deep expertise in backend systems and applied ML. You ll join a collaborative, customer-driven environment, working on AI-powered features that improve how users interact with their data. You ll have real-world impact building and deploying ML services at scale while shaping the future of the product.

Responsibilities

• Deliver scalable, production-ready backend/server code in Java, integrating and deploying machine learning models and services within a cloud services architecture.
• Collaborate with data scientists and product teams to translate ML prototypes into reliable, maintainable, and scalable APIs and services.
• Architect and implement real-time and batch inference pipelines on AWS, ensuring high performance and reliability.
• Apply data engineering best practices to support feature extraction, data versioning, and model lifecycle management.
• Own the end-to-end lifecycle of ML services: deployment, monitoring, retraining, and A/B testing in production. Mentor other engineers; contribute to engineering excellence through code reviews, design discussions, and process improvements.
• Stay current with advances in ML infrastructure, MLOps tooling, and cloud-native technologies.

Required Skillset:

• 5+ years backend software development, with deep expertise in Java, Spring/Spring Boot, and microservices architecture.
• Strong grasp of REST API design, database modeling, and distributed systems.
• Proven experience deploying applications on AWS and working within Agile/Scrum environments.
• Fluency in SQL and NoSQL databases (MySQL preferred).
• 3+ years building and deploying ML models to production.
• Proficient in Python and common ML/AI frameworks such as scikit-learn, TensorFlow, PyTorch, XGBoost (or similar).
• Hands-on experience with MLOps tools/practices (e.g., MLflow, SageMaker, Kubeflow) or similar model lifecycle platforms.
• Deep understanding of feature engineering, model monitoring, performance metrics, and retraining strategies.
• Familiarity with data pipelines, streaming systems (e.g., Kafka), and data processing tools (e.g., Spark or pandas).
• Exposure to LLMs or generative AI use cases in real-world products.
• BS/MS in Computer Science, Engineering, or a related field. Advanced coursework in ML/AI is a plus.

Bonus Skillset:

• Experience with vector databases, embeddings, or retrieval-augmented generation (RAG) pipelines is a plus.
• Background in financial services or fintech is a strong advantage.

Location: Menlo Park, CA (Hybrid Tues. & Wed. Onsite)

Salary: $160k $180k base