Instacart
Machine Learning Engineer, PhD Intern (Fall)
Remote Machine Learning role with clear candidate location fit.
PostedRecently added
Eligible countries1 accepted country
Seniority signalJunior
Work settingRemote
Accepted candidate locations
USA
Role overview
Machine Learning Engineer, PhD Intern (Fall)
Requirements and responsibilities
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Details
- We build state-of-the-art models powering Search, Discovery, and Ads, combining generative AI and traditional machine learning to create best-in-class recommendations
- We build rich product and knowledge graphs from catalog data imported from hundreds of retailers, applying them in recommendations and other user experiences
- We redefine traditional domains across the company with AI, such as hyperpersonalized marketing and 0 → 1 meal planning products
- Query understanding: Using cutting-edge AI and LLM-based techniques to understand user intent, refine queries, and support downstream retrieval and ranking.
- Search relevance and ranking: Improving search relevance by incorporating signals from user behavior, catalog knowledge, and generative models, including hybrid retrieval and ranking systems.
- Generative recommendations: Pushing the boundaries of where generative and traditional models intersect across retrieval and ranking systems; developing scalable feedback and reward modeling approaches for closed-loop learning (RFT).
- LLM evaluation and AIQA systems: Building LLM-based evaluation frameworks (e.g., LLM-as-a-Judge, self-critique) to improve the quality and reliability of generative and agentic systems.
- Low-latency and scalable LLM systems: Researching techniques to deploy LLMs in high-traffic, latency-sensitive production environments, balancing quality, cost, and latency through cascading, distillation, and selective generation.
- Knowledge graphs: Working on graph data management and knowledge discovery over one of the world’s largest grocery catalogs, and integrating structured knowledge with LLM-based reasoning and natural language interfaces.
- Sequence modeling: Building temporal models for user behavior prediction.
- Ph.D. student in computer science, mathematics, statistics, economics, or related areas.
- Strong programming (Python, Golang) and algorithmic skills.
- Solid foundations in machine learning, algorithms, or optimization
- Curious, self-motivated, and comfortable working on open-ended problems
- Ph.D. student at a top tier university in the United States
- Hands-on experience with generative or traditional modeling frameworks (PyTorch, Tensorflow, vLLM)
- Prior industry or research internship in machine learning or AI
- Interest and experience in translating research ideas into scalable production systems
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