Senior Machine Learning Engineer

Overview

This is a general posting for multiple Senior Machine Learning roles open across our 4-sided marketplace. You’ll get the chance to learn about the problems the different ML teams solve as you go through the process. Towards the end of your process, we’ll do a team-matching exercise to determine which of the open roles/teams you’ll join. You can find a blurb on each team at the bottom of this page.

Core Experience: The Core Experience organization at Instacart is at the forefront of applying cutting-edge AI technologies, including large language models (LLMs), to revolutionize how customers find products. Working with world-class engineers, data scientists, and product managers, the team builds sophisticated machine learning and AI systems that power the future of search and recommendations at Instacart.

By leveraging state-of-the-art transformer architectures, multimodal AI, and generative models, the team enhances the relevance across all shopping surfaces. They innovate with advanced neural retrieval methods, LLM-powered ranking algorithms, and AI-driven personalization systems, delivering highly contextual and intuitive results to users throughout the Instacart ecosystem.

The team tackles some of the most critical aspects of the business—helping customers connect with exactly the right products through AI. They solve complex, large-scale search challenges using the latest in deep learning, natural language understanding, and LLM fine-tuning techniques. Their dedication to AI innovation is reflected in their recent publications and contributions to the field.

About the Job

• Design, develop, and deploy advanced AI and machine learning solutions, including LLMs and neural networks, to solve complex challenges in our dynamic marketplace environment.
• Architect and implement state-of-the-art deep learning systems that leverage transformer models, multimodal AI, and generative techniques to create intelligent, adaptive solutions.
• Collaborate closely with product managers, data scientists, and backend engineers to translate business requirements into cutting-edge AI applications that deliver measurable impact.
• Pioneer foundation model applications, including prompt engineering and fine-tuning, to create AI systems with unparalleled understanding of context and user intent.
• Ensure ethical implementation and strong integration of AI solutions, aligning them with strategic business objectives.
• Drive continuous innovation by researching, testing, and implementing the latest advancements in machine learning, such as embeddings, vector databases, and RLHF (Reinforcement Learning from Human Feedback).
• Enhance operational efficiency through intelligent automation, predictive modeling, and algorithmic optimization driven by custom-trained AI systems.

About You

Minimum Qualifications

• Graduate degree (Master’s or PhD) in artificial intelligence, machine learning, or equivalent self-study and experience.
• 7+ years of industry experience using machine learning to solve real-world problems with large datasets.
• Strong programming skills in Python with fluency in data manipulation tools (SQL, Pandas) and machine learning frameworks (scikit-learn, XGBoost, Keras/TensorFlow).
• Strong analytical skills and problem-solving abilities.
• Excellent communicator, capable of collaborating with diverse stakeholders across all levels.

Preferred Qualifications

• Extensive expertise with modern deep learning frameworks (PyTorch, TensorFlow, JAX) and advanced LLM architectures, including transformer models and multimodal AI systems.
• Demonstrated experience implementing and fine-tuning large language models, including prompt engineering, embedding techniques, and optimizing inference in production environments.
• Strong foundation in AI fundamentals, including neural network architectures, generative models, and foundation model adaptation methodologies like PEFT, LoRA, and RLHF.
• Proven track record of designing and deploying sophisticated ML/AI systems in production environments that improve recommendations, search relevance, and user engagement metrics.
• Experience optimizing AI model performance across the stack, from architecture design and training workflows to distributed inference and serving infrastructure.
• Self-motivated and innovative with a strong sense of ownership, capable of navigating the rapidly evolving AI landscape to evaluate and implement novel techniques.
• Passionate about applying cutting-edge AI research to real-world problems while considering the practical aspects of building responsible, efficient AI systems at scale.