Staff ML Engineer — Java Backend Leader (Hybrid)

Grubhub; New York (NY); Híbrido; USD 209 000 - 217 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity

401(k) matching

Medical, dental, and vision plans

Paid time off and parental leave

Meal discounts

Resumo da oferta

Wonder’s Model Platform team builds the systems that take machine learning models from prototype to production and keep them running well. We hire a Staff Machine Learning Engineer with deep Java backend experience to set technical direction for how our models and services work together.

You’ll define how every team calls our models and lead performance standards across services. This is a hands-on leadership role requiring you to write code, design models, and solve hard production problems.

Qualificações

• BS, MS or PhD in CS, Engineering, Mathematics, Statistics, or related field, or equivalent experience.
• 8+ years of professional software engineering with deep Java backend experience.
• 5+ years building and running ML systems in production, including 3+ years leading technical direction.
• Expert Java and OO design, Spring Boot, REST/gRPC, and JVM tuning.
• Experience setting load testing standards and capacity planning for services.
• Experience designing highly available distributed systems with built-in testing and monitoring.
• Deep production ML architecture including training pipelines and real-time serving.
• Strong Python and ML framework experience (XGBoost/LightGBM, PyTorch, TensorFlow).
• Strong Spark/SQL data engineering and streaming/batch pipelines.
• Cloud infrastructure on AWS or GCP, Docker, and Kubernetes.
• Experience with experimentation including A/B design and guardrail metrics.
• Proven technical leadership across ML and software teams and mentoring.
• Clear communication of model behavior and trade-offs to technical and non-technical audiences.

Responsabilidades

• 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 integrate them into the release process.
• Drive performance work such as profiling, JVM tuning, caching, request batching, and concurrency for high-traffic model integrations.
• Lead the design and delivery of complex ML solutions spanning multiple teams or systems, and guide cross-team architectural decisions.
• Build reusable ML architectures, pipelines, and deployment patterns to accelerate production readiness of Applied Science work.
• Set standards for ML reliability, scalability, monitoring, and lifecycle management, including model scorecards and safe rollouts.
• Own model architecture and tuning for demanding deployments, balancing accuracy, latency, and cost.
• Conduct design and code reviews across ML and software teams and push back when needed.
• Own the platform’s technical roadmap with engineering leadership and coordinate work across teams.
• Mentor senior and mid-level engineers and participate in hiring to deepen team capabilities.

Conhecimentos

Java backend

Spring Boot

REST or gRPC APIs

JVM performance tuning

Load testing and benchmarking

Distributed systems design

ML systems in production

Python for ML

Spark and SQL

Cloud AWS/GCP

Docker and Kubernetes

Experimentation and A/B testing

Technical leadership

Communication to executives

Formação académica

BS, MS, or PhD in CS/Engineering/Math/Statistics or equivalent

Ferramentas

Docker

Kubernetes

Gatling/JMeter

Descrição da oferta de emprego

Wonder’s Model Platform team builds the systems that take machine learning models from prototype to production and keep them running well. We hire a Staff Machine Learning Engineer with deep Java backend experience to set technical direction for how our models and services work together.

You’ll define how every team calls our models and lead performance standards across services. This is a hands-on leadership role requiring you to write code, design models, and solve hard production problems.