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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Staff Machine Learning Engineer - **Company:** Grubhub Inc. - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $240,000.0 - $249,500.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Amazon Web Services, Encodings, Information Engineering, Data Infrastructure, Software Debugging, Apache Hive, Information Retrieval, Python (Programming Language), Machine Learning, Performance Tuning, Recommender Systems, Tensorflow, SQL Databases, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Deep Learning, Model Validation, Technical Debt, Pyspark, Information Technology, Low Latency, Machine Learning Operations, GPT, Natural Language Understanding - **Published:** August 5, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=87c8f793b925da74 ## About the Role * MS/PhD in a quantitative discipline (Computer Science, Math, Physics, Engineering, Statistics or other technical field) or equivalent experience * 8+ years building and shipping machine learning systems, including 3+ years operating at staff-level scope: setting technical direction across multiple teams, model families, or systems * Deep experience in recommendation systems, ranking, or information retrieval at scale, in production and under real latency and cost constraints * Proven track record with production deep learning in TensorFlow or PyTorch, including training, serving, and tuning runtime models on GPUs * Experience with Large Language Models and transformer-based architectures, including fine-tuning, embedding generation, and deploying them in latency-sensitive applications. Experience with language understanding over imperfect grammar (real-world search queries, menu and catalog text) is a strong plus * Strong data engineering fundamentals: PySpark, Hive/SQL, the Python data stack, and feature pipelines you can debug as well as build * Fluency with experimentation: designing A/B tests, choosing the right guardrails, and recognizing when an offline metric is misleading you * Experience with cloud ML infrastructure (AWS/SageMaker or equivalent), model deployment, and production monitoring and observability * Demonstrated technical leadership: mentoring engineers, driving design and architecture reviews beyond your own team, and influencing decisions without direct authority * Comfort communicating performance metrics, model behavior, and technical trade-offs to both deeply technical and non-technical audiences, up to and including executive stakeholders * Ability to keep up with the latest research publications and synthesize them into working production systems * Deep interest in self-motivated continuous learning ## Description * Own the architecture of our ranking and recommendation stack end to end: candidate retrieval, multi-objective ranking, calibration, and the ensemble that trades conversion against profitability. Make the cross-system design calls that no individual model owner can make alone. * Lead the evolution of our optimization objective from short-term conversion toward long-term diner value, including the offline evaluation and online experimentation work required to trust the result before it ships. * Bring state of the art research in information retrieval and recommender systems into our runtime environment: LLM-driven query and intent understanding, embedding-based retrieval, sequential user representations for cold start, and real-time inference. Assess rigorously what actually transfers to our traffic, and say no to what does not. * Raise engineering and operational standards across multiple teams: model evaluation and scorecards, reproducible training pipelines, safe deployment, SLOs and observability for tier-1 models, and proactive management of technical debt before it becomes urgent. * Partner with Product, Search Engineering, Ads, and Data Platform to shape roadmaps, surface risk early, and make sure the data and infrastructure exist before the model needs them. * Mentor senior and mid-level engineers, participate in hiring, and grow the technical depth of the team so that no critical system depends on a single person. * Translate technical trade-offs into business terms for product and executive stakeholders, and document the rationale clearly enough that decisions outlive the people who made them. * Question existing assumptions, look for the innovation we are not yet pursuing, and relentlessly analyze and improve the performance of our business. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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