Staff Machine Learning Engineer, Model Platform

Wonder Group, INC; New York (NY); Híbrido; USD 209 000 - 217 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity

401(k)

Medical, dental, vision plans

Disability coverage

Paid time off

Parental leave

Employee discounts

Resumo da oferta

Wonder Group seeks a Staff Machine Learning Engineer to provide hands‑on technical leadership across Java services and ML model integrations, from prototype to production. You will define standard architectures, drive performance improvements, and mentor engineers to build scalable ML platforms.

The role focuses on combining Java services with ML pipelines, ensuring reliability, latency and cost targets, and leading cross‑team architectural decisions in a hybrid work setting in New York.

Qualificações

• 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 operating high‑traffic backend services in Java.
• 5+ years building and operating ML systems in production, including 3+ years in a senior scope setting technical direction across teams or systems.
• Expertise in Java and object‑oriented design, including Spring Boot, REST or gRPC APIs, and JVM performance tuning.
• Proven experience setting load testing and performance standards using Gatling or JMeter, plus capacity planning.

Responsabilidades

• Define the standard architecture for how Java services call ML models, including client libraries, API contracts, latency budgets, and fallback behavior.
• Set load testing, performance benchmarking, and capacity planning standards for every service that uses the models, and integrate those checks into the release process.
• Drive performance improvements through profiling, JVM tuning, caching, request batching, and concurrency work for high‑traffic model integrations.
• Lead the design and delivery of complex ML solutions across multiple teams or systems, making architecture decisions that span team boundaries.
• Build reusable ML architectures, pipelines, and deployment patterns to help Applied Science move work into production faster.
• Establish 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 the most demanding deployments, balancing accuracy against latency, scalability, and cost.
• Run design and code reviews across ML and software engineering teams, and challenge technical choices when evidence supports it.
• Own the platform technical roadmap with engineering leadership by sequencing work across teams and surfacing risks early.
• Mentor senior and mid‑level engineers, participate in hiring, and build team depth so critical systems do not depend on one person.
• Translate technical trade‑offs into business terms for product and executive stakeholders, and document key decisions and rationale.

Conhecimentos

Java backend

ML systems

Distributed systems

Gatling/JMeter

Cloud (AWS/GCP)

Kubernetes

Python

Architecture leadership

Formação académica

BS/MS/PhD in CS or related

Ferramentas

Gatling

JMeter

Docker

Kubernetes

Descrição da oferta de emprego

Wonder Group’s Model Platform team is hiring a Staff Machine Learning Engineer to provide hands‑on technical leadership. This role focuses on how Java services integrate with machine learning models, while improving performance, reliability, and cross‑team architecture from prototype through production.

Key Responsibilities

• Define the standard architecture for how Java services call ML models, including client libraries, API contracts, latency budgets, and fallback behavior.
• Set load testing, performance benchmarking, and capacity planning standards for every service that uses the models, and integrate those checks into the release process.
• Drive performance improvements through profiling, JVM tuning, caching, request batching, and concurrency work for high‑traffic model integrations.
• Lead the design and delivery of complex ML solutions across multiple teams or systems, making architecture decisions that span team boundaries.
• Build reusable ML architectures, pipelines, and deployment patterns to help Applied Science move work into production faster.
• Establish 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 the most demanding deployments, balancing accuracy against latency, scalability, and cost.
• Run design and code reviews across ML and software engineering teams, and challenge technical choices when evidence supports it.
• Own the platform technical roadmap with engineering leadership by sequencing work across teams and surfacing risks early.
• Mentor senior and mid‑level engineers, participate in hiring, and build team depth so critical systems do not depend on one person.
• Translate technical trade‑offs into business terms for product and executive stakeholders, and document key decisions and rationale.

Required Qualifications

• 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 operating high‑traffic backend services in Java.
• 5+ years building and operating ML systems in production, including 3+ years in a senior scope setting technical direction across teams or systems.
• Expertise in Java and object‑oriented design, including service frameworks such as Spring Boot (or similar), REST or gRPC APIs, and JVM performance tuning.
• Proven experience setting load testing and performance standards using tools such as Gatling or JMeter, plus planning 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 large‑scale rollback.
• Strong Python and hands‑on model development experience with frameworks such as XGBoost/LightGBM, PyTorch, or TensorFlow, including choosing and tuning models under latency and cost limits.
• Strong data engineering experience with Spark and SQL, including building and debugging streaming or batch pipelines.
• Deep cloud infrastructure experience on AWS or GCP (SageMaker, Vertex AI, or equivalent), plus Docker and Kubernetes.
• Experience with experimentation, including A/B test design, guardrail metrics, and evaluating when offline metrics can be misleading.
• Proven technical leadership across ML and software engineering teams, including leading architecture reviews, influencing decisions without formal authority, and mentoring senior engineers.
• Clear communication of model behavior and technical trade‑offs to both technical and non‑technical audiences, including executives.

Technologies

Java, Spring Boot, REST, gRPC, JVM performance tuning, Gatling, JMeter, Python, XGBoost, LightGBM, PyTorch, TensorFlow, Spark, SQL, AWS, GCP, SageMaker, Vertex AI, Docker, Kubernetes, ONNX Runtime, TensorFlow Java, XGBoost4J, Cassandra, Kafka, Flink

Hybrid Work Requirement

• The hybrid model requires 3 days per week in the office.
• Candidates may be in the office up to 5 days per week if it works for them.

Compensation

• New York: $208,500 - $216,500 per year
• Wonder uses geographic‑specific salary structures. The final offer may consider factors including skills, education, training, credentials, and experience.

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
• 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

Additional Experience (Preferred)

• 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 clouds

Policy / Eligibility

Wonder does not sponsor applicants for employment visa status for this role.