Staff Machine Learning Engineer, Model Platform

Wonder Group • New York, NY

1d • Hybrid

About The Position

Wonder is hiring a Staff Machine Learning Engineer for its Model Platform team. The team builds systems that move machine learning models from prototype to production and operate them at scale. Its models support restaurant and dish ranking, delivery-time prediction, order pricing, and courier dispatch across Grubhub and Wonder, with many predictions served by high-traffic Java services.

This is a hands-on technical leadership role focused on defining how Java services integrate with machine learning models and setting standards for performance, reliability, and model lifecycle management. The engineer will lead complex, cross-team ML initiatives, write code, design and tune models, and solve difficult production problems. They will serve as the primary technical link between Model Platform and software engineering teams, partnering with Applied Scientists, data and infrastructure platform teams, and senior engineering leadership.

The role uses a hybrid work model requiring three days per week in the office, with the option to work in the office up to five days per week. The listed New York salary range is $208,500–$216,500 per year; the offer may vary based on location and candidate qualifications. Wonder does not sponsor employment visas for this role.

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 service frameworks such as Spring Boot or similar, REST or gRPC APIs, and JVM performance tuning.
• Experience 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 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 frameworks such as XGBoost/LightGBM, PyTorch, or TensorFlow, including selecting and tuning models under 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, Docker, and Kubernetes, including platforms such as SageMaker, Vertex AI, or equivalent.
• Experience with experimentation, including A/B test design, guardrail metrics, and recognizing when an offline metric may be misleading.
• Proven technical leadership across ML and software engineering teams, including leading architecture reviews beyond one's own team, influencing without direct authority, and mentoring senior engineers.
• Ability to communicate model behavior and technical trade-offs clearly to technical and non-technical audiences, including executives.
• Must complete the same Java coding interview used for software engineering candidates.

Nice To Haves

• Experience in marketplace, e-commerce, or logistics domains, 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 the standard architecture for Java services to call ML models, including client libraries, API contracts, latency budgets, and fallbacks.
• Set standards for load testing, performance benchmarking, and capacity planning for services that call models, and incorporate these standards into the release process.
• Drive performance improvements for high-traffic model integrations, including profiling, JVM tuning, caching, request batching, and concurrency.
• Lead the design and delivery of complex ML solutions spanning multiple teams or systems, and make cross-team architecture decisions.
• Build reusable ML architectures, pipelines, and deployment patterns to help Applied Science teams move work into production faster.
• Set standards for ML reliability, scalability, monitoring, and lifecycle management, including model scorecards, reproducible training, safe rollouts, and SLOs for tier-1 models.
• Own model architecture and tuning for demanding deployments, balancing accuracy with latency, scalability, and cost.
• Lead 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 so critical systems do not depend on one person.
• 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 and a 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, paid vacation for non-exempt employees, and paid sick leave in compliance with applicable law.
• Paid parental leave.
• Discounted meals and exclusive perks across the Wonder family of brands. Eligibility, effective dates, and plan options vary by employment classification and location.

What This Job Offers

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