Senior Machine Learning Engineer
$158,500–$191,000 year
Hybrid · Chicago, Illinois, United States or New York City, New York, United States
Job Summary
Senior Machine Learning Engineer role in Grubhub's Discovery and Foundation team focusing on high-quality restaurant recommendations and IR/retrieval-model improvements. Responsibilities include developing and validating models, designing A/B tests, building metrics dashboards, mentoring junior data scientists, creating automated training jobs, and collaborating with Product and Engineering to align models with business goals. The role involves working with deep neural networks, transfer learning from pretrained models, transformer-based architectures, and large language models; experience with GPUs, PyTorch/TensorFlow, and data engineering tools (pyspark, hive) is required. The position supports a hybrid work model with 3 days/week in the office and potential for more in New York, Chicago, or Denver. Requires MS/PhD in a quantitative field or equivalent experience, 4+ years in data analytics/ML, and 2+ years in predictive modeling and information retrieval/recommendation systems; strong communication skills and a commitment to continuous learning are expected.
Required Qualifications
- MS/PhD in quantitative discipline or equivalent experience
- 4+ years experience in data analytics, machine learning, or related field
- 2+ years experience in applied predictive modeling with TensorFlow
- 2+ years experience in information retrieval or recommendation systems
- Experience with language models, especially on imperfect grammars
- Experience with Large Language Models (LLMs), including fine-tuning and deploying transformer-based architectures in real-world applications
- Experience tuning runtime models using GPUs
- Data engineering and feature preparation in pyspark, hive, and the python data stack
- Comfort communicating performance metrics, model details, and features specifications to technical and non-technical audiences
- Ability to keep up with the latest publications and synthesize research into working models
- Hybrid work arrangement with 3 days in office per week; in-office up to 5 days/week encouraged
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