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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - **Company:** Grubhub Inc. - **Location:** New York, United States - **Experience:** Experienced - **Salary:** $208,500 - $216,500 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), 3d Models, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Cloud Computing, Information Engineering, Software Debugging, Distributed Systems, Java Virtual Machine (JVM), Java Web Services, Apache JMeter, Python (Programming Language), Load Testing, Machine Learning, NoSQL, Object-Oriented Software Development, Performance Tuning, Tensorflow, Azure Machine Learning, Software Engineering, SQL Databases, Data Streaming, Pytorch, Apache Spark, Spring-boot, Gatling, Backend, Kubernetes, Information Technology, Apache Flink, Cassandra, ONNX (Open Neural Network Exchange) Format, Xgboost, Apache Kafka, Machine Learning Operations, Software Version Control, Api Management, Docker - **Published:** November 7, 2026 - **Apply:** https://startup.jobs/staff-machine-learning-engineer-model-platform-grubhub-holdings-inc-10713069 ## About the Role * BS, MS, or PhD in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent experience * 8+ years of professional software engineering experience, with deep expertise building and running high-traffic backend services in Java * 5+ years building and running ML systems in production, including 3+ years at senior scope setting technical direction across teams or systems * Expert Java and object-oriented design skills, including service frameworks (Spring Boot or similar), REST or gRPC APIs, and JVM performance tuning * A track record of setting load testing and performance standards (Gatling, JMeter, or similar) and planning capacity for services as load changes * Experience designing highly available distributed systems (99.99% uptime) with automated testing and monitoring built in from the start * Deep expertise in production ML architecture, including training pipelines, batch and real-time serving, feature pipelines, model versioning, and rollback at scale * Strong Python and hands-on model development experience with common frameworks (XGBoost/LightGBM, PyTorch, or TensorFlow), including choosing and tuning models under real latency and cost limits * Strong data engineering skills in Spark and SQL, including building and debugging streaming or batch pipelines * Deep experience with cloud infrastructure on AWS or GCP (SageMaker, Vertex AI, or equivalent), Docker, and Kubernetes * Experience with experimentation, including A/B test design, guardrail metrics, and knowing when an offline metric is misleading * Proven technical leadership across ML and software engineering teams, including leading architecture reviews beyond your own team, influencing decisions without direct authority, and mentoring senior engineers * Clear communication of model behavior and technical trade-offs to technical and non-technical audiences, including executives Got These? Even Better * Experience in marketplace, e-commerce, or logistics, such as ranking, recommendations, ETA prediction, pricing, or dispatch * Serving models inside or alongside JVM services (ONNX Runtime, TensorFlow Java, XGBoost4J, or similar) * Building an internal ML platform used by multiple science teams * Cassandra or other distributed NoSQL databases * Streaming data systems (Kafka, Flink) * Leading an ML platform migration between cloud ## Description We're hiring a Staff Machine Learning Engineer with deep Java backend experience to set the technical direction for how our models and services work together. You'll define how every team calls our models. You'll set the standards for load testing and performance across the services that use them. You'll also lead complex ML work that spans multiple teams and systems. This is a hands-on technical leadership role. You'll write code, design models, and take on the hardest production problems yourself. You'll be the main technical link between Model Platform and our software engineering teams, and you'll partner with Applied Scientists, our data and infrastructure platform teams, and senior engineering leadership. Our interview process includes the same Java coding interview our software engineers take. The Impact You Will Make * Define the standard architecture for how Java services call ML models, including client libraries, API contracts, latency budgets, and fallbacks. * Set the standards for load testing, performance benchmarking, and capacity planning for every service that calls our models, and build them into the release process. * Drive performance work such as profiling, JVM tuning, caching, request batching, and concurrency for our highest-traffic model integrations. * Lead the design and delivery of complex ML solutions that span multiple teams or systems, and make the architecture decisions that cross team boundaries. * Build reusable ML architectures, pipelines, and deployment patterns that get Applied Science work into production faster. * Set the standards for ML reliability, scalability, monitoring, and lifecycle management, including model scorecards, reproducible training, safe rollout, and SLOs for tier-1 models. * Own model architecture and tuning for our most demanding deployments, balancing accuracy against latency, scalability, and cost. * Run design and code reviews across ML and software engineering teams, and push back on technical decisions when the evidence supports it. * Own the platform's technical roadmap with engineering leadership, sequence work across teams, and raise risks early. * Mentor senior and mid-level engineers, take part in hiring, and build depth on the team so no critical system depends on one person. * Explain technical trade-offs in business terms to product and executive stakeholders, and write down key decisions and the reasons behind them.