Senior Machine Learning Engineer

Overview

About the Role:

This is a general posting for multiple Sr. 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.

About the Team:

Search Discovery ML: The Search and Discovery ML team at Instacart is at the forefront of applying cutting-edge AI technologies, including large language models (LLMs), to revolutionize how customers find products. Working alongside world-class engineers, data scientists, and product managers, we’re building sophisticated machine learning and AI systems that power the future of search and recommendations at Instacart. Our team leverages state-of-the-art transformer architectures, multimodal AI, and generative models to enhance the relevance across all shopping surfaces. We’re constantly innovating with advanced neural retrieval methods, LLM-powered ranking algorithms, and AI-driven personalization systems that deliver highly contextual and intuitive results to users throughout the Instacart ecosystem. As part of our team, you’ll tackle one of the most critical aspects of the business—helping customers connect with exactly the right products through AI. We’re solving complex, large-scale search challenges using the latest in deep learning, natural language understanding, and LLM fine-tuning techniques to create intelligent systems that truly understand user intent and shopping behavior. Our commitment to AI innovation is reflected in our recent publications and research contributions to the field (Recent publications 1, 2, 3, 4, 5).

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 the application of foundation models, prompt engineering, and fine-tuning methodologies to create AI systems that understand context and user intent at unprecedented levels.
• Engage with diverse stakeholders to ensure our AI solutions are ethically implemented, well-integrated with existing systems, and fully aligned with strategic business objectives.
• Drive continuous innovation in our AI infrastructure by researching, testing, and implementing the latest advancements in machine learning, from embeddings and vector databases to reinforcement learning from human feedback (RLHF).
• Push the boundaries of operational efficiency through intelligent automation, predictive modeling, and algorithmic optimization powered by our custom-trained AI systems.

About You:

Minimum Qualifications:

• Have a graduate degree (masters or PhD) in artificial intelligence, machine learning or equivalent self study and experience
• Have 7+ years of industry experience using machine learning to solve real-world problems with large datasets
• Have strong programming skills in Python and fluency in data manipulation (SQL, Pandas) and Machine Learning (scikit-learn, XGBoost, Keras/Tensorflow) tools
• Have strong analytical skills and problem-solving ability
• Are a strong communicator who can collaborate 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, attention mechanisms, and multimodal AI systems.
• Demonstrated experience implementing and fine-tuning large language models, including prompt engineering, embedding techniques, and efficient inference optimization for 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 designing and deploying sophisticated ML/AI systems in production environments that drive measurable business impact through improved recommendations, search relevance, and user engagement metrics.
• Experience optimizing AI model performance across the full stack, from model architecture and training workflows to distributed inference and serving infrastructure.
• Self-motivated innovator with a strong sense of ownership who can navigate the rapidly evolving AI landscape, evaluate emerging techniques, and implement novel approaches to solve complex business challenges.
• Passion for applying cutting-edge AI research to real-world applications and a keen understanding of the practical considerations in developing responsible, efficient AI systems at scale.

Originally posted on Himalayas