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About the Role
Wonder's Model Platform team builds systems to deploy and maintain machine learning models for high-traffic Java services, impacting millions of diners. This Staff Machine Learning Engineer role involves setting technical direction for model-service integration, defining standards for performance and load testing, and leading complex ML initiatives. It's a hands-on technical leadership position requiring coding, model design, and problem-solving, with close collaboration across various engineering and science teams.
Responsibilities
- 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.
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, 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
- 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
- Wonder does not sponsor applicants for employment visa status for this role.
Skills
- Java
- Object-oriented design
- Spring Boot
- REST APIs
- gRPC APIs
- JVM performance tuning
- Load testing
- Performance benchmarking
- Capacity planning
- Distributed systems design
- Automated testing
- Monitoring
- Production ML architecture
- Training pipelines
- Batch serving
- Real-time serving
- Feature pipelines
- Model versioning
- Model rollback
- Python
- XGBoost
- LightGBM
- PyTorch
- TensorFlow
- Spark
- SQL
- Streaming pipelines
- Batch pipelines
- AWS
- GCP
- SageMaker
- Vertex AI
- Docker
- Kubernetes
- Experimentation
- A/B testing
- Guardrail metrics
- Technical leadership
- Architecture reviews
- Mentoring
- Communication
Location
- New York
Work Type
- Hybrid
Experience Level
- Staff
- 8+ years of professional software engineering experience
- 5+ years building and running ML systems in production
- 3+ years at senior scope setting technical direction across teams or systems
Education Level
- BS, MS, or PhD in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent experience
Salary/Compensations
- $208,500 - $216,500 per year
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
- Flexible time off for exempt employees
- Paid vacation for non-exempt employees
- Paid sick leave in compliance with applicable law
- Paid parental leave
- Discounted meals
- Exclusive perks across the Wonder family of brands
About the Company
- Wonder is a mealtime platform offering a variety of dishes from multiple restaurants in a single order, with options for delivery, dine-in, or pick-up.
- Wonder manages every aspect of the dining experience, from recipe development to technology, ensuring quality, variety, and consistency.
- The company aims to make great food more accessible.
Equal Opportunity
- Wonder builds the best teams by hiring with an objective lens — evaluating people for their potential while championing diversity, equity, and inclusion.
- Wonder does not discriminate based on race, color, religion, gender identity or expression, sexual orientation, national origin, age, military service eligibility, veteran status, marital status, disability, or any other protected class.
- Wonder participates in the federal government's E-Verify program to confirm employment eligibility.
- If you need an accommodation during the interview process, please let your recruiter know.