Staff Machine Learning Engineer, Model Platform

Wonder • New York, NY

2d • $208,500 - $216,500 • Hybrid

About The Position

Wonder is hiring a Staff Machine Learning Engineer for its Model Platform team, which builds systems that move machine learning models from prototype into production and keeps them running. The models support restaurant and dish ranking, delivery-time prediction, order pricing, and courier dispatch across Grubhub and Wonder. Most predictions are served to high-traffic Java services, so the role focuses on making models work effectively with those services.

This hands-on technical leadership role will set the technical direction for how models and services work together. The engineer will define how teams call models, establish load-testing and performance standards, and lead complex machine learning initiatives that span teams and systems. They will write code, design models, tackle challenging production problems, and serve as the main technical link between Model Platform and software engineering teams. The role partners with Applied Scientists, data and infrastructure platform teams, and senior engineering leadership.

Requirements

• BS, MS, or PhD in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent experience.
• 8+ years of professional software engineering experience, including deep expertise building and operating high-traffic Java backend services.
• 5+ years building and operating production ML systems, including 3+ years at senior scope setting technical direction across teams or systems.
• Expert Java and object-oriented design skills, including Spring Boot or similar service frameworks, REST or gRPC APIs, and JVM performance tuning.
• Track record establishing load-testing and performance standards using Gatling, JMeter, or similar tools, and planning service capacity as load changes.
• Experience designing highly available distributed systems (99.99% uptime), with automated testing and monitoring built in from the start.
• Deep production ML architecture expertise, 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 such as 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 AWS or GCP cloud infrastructure (SageMaker, Vertex AI, or equivalent), Docker, and Kubernetes.
• Experience with experimentation, including A/B test design, guardrail metrics, and recognizing when an offline metric is misleading.
• Proven technical leadership across ML and software engineering teams, including leading architecture reviews beyond one's own team, influencing decisions without direct authority, and mentoring senior engineers.
• Ability to clearly communicate model behavior and technical trade-offs to technical and non-technical audiences, including executives.
• Wonder does not sponsor applicants for employment visa status for this role.

Nice To Haves

• Experience in marketplace, e-commerce, or logistics, such as ranking, recommendations, ETA prediction, pricing, or dispatch.
• Experience serving models inside or alongside JVM services, using ONNX Runtime, TensorFlow Java, XGBoost4J, or similar.
• Experience building an internal ML platform used by multiple science teams.
• Experience with Cassandra or other distributed NoSQL databases.
• Experience with streaming data systems such as Kafka or Flink.
• Experience leading an ML platform migration between cloud providers.

Responsibilities

• Define standard architecture for Java services calling ML models, including client libraries, API contracts, latency budgets, and fallbacks.
• Establish load-testing, performance-benchmarking, and capacity-planning standards for services that call models, and incorporate them into the release process.
• Drive performance improvements for high-traffic model integrations through profiling, JVM tuning, caching, request batching, and concurrency.
• Lead the design and delivery of complex ML solutions spanning multiple teams or systems, making architecture decisions across team boundaries.
• Build reusable ML architectures, pipelines, and deployment patterns to help Applied Science work reach production faster.
• Set 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 demanding deployments, balancing accuracy with latency, scalability, and cost.
• Conduct design and code reviews across ML and software engineering teams, and challenge technical decisions when evidence supports doing so.
• Own the platform's technical roadmap with engineering leadership, sequence work across teams, and raise risks early.
• Mentor senior and mid-level engineers, participate in hiring, and build team depth to reduce dependence on individuals for critical systems.
• Explain technical trade-offs in business terms to product and executive stakeholders, and document key decisions and their rationale.

Benefits

• Competitive compensation package with equity.
• 401(k).
• Choice of medical, dental, and vision plans.
• Company-paid short- and long-term disability coverage.
• Paid time off, including flexible time off for exempt employees and paid vacation for non-exempt employees.
• Paid sick leave in compliance with applicable law.
• Paid parental leave.
• Discounted meals and exclusive perks across the Wonder family of brands.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior