Machine Learning Engineer, Instacart

• Have a graduate degree in a technical field (artificial intelligence, machine learning, economics, statistics) or equivalent work experience
• Have 1-2 years of industry experience using machine learning to solve real-world problems with large datasets
• A deep understanding of machine learning algorithms and techniques, with fluency in modern research and literature
• Strong programming skills in software engineering (e.g., Python, Go), along with expertise in data manipulation using tools (SQL, Spark, and Pandas), and ML frameworks (XGBoost, Torch, Tensorflow)
• Strong analytical and problem-solving skills
• Excellent communication skills (both verbal and written) and can collaborate with a diverse group of stakeholders across all levels

Desirable

• As a Machine Learning Engineer, you will collaborate with senior engineers and product leaders to build the future of Instacart Ads
• You will explore and innovate novel machine learning solutions across the ads stack in areas such as ads targeting, bidding and pacing
• You will actively contribute to team initiatives, assisting in all stages from initial ideation to prototyping, experimentation, and final implementation
• Our Advertiser Optimization team is a passionate group of innovators focused on developing state-of-the-art optimization technologies that boost the efficiency and impact of our advertising ecosystem
• Through the use of sophisticated algorithms and machine learning models, the team helps our advertising partners achieve outstanding results while delivering a relevant, engaging experience for customers
• We're also at the forefront of integrating Large Language Models (LLMs), unlocking powerful new capabilities that are redefining how brands connect with consumers in the digital grocery space
• Design, develop, and deploy machine learning solutions including data pipelines, production models, and serving integrations to tackle practical challenges in the ads organization
• Collaborate closely with product managers, data scientists, and infrastructure engineers to deeply understand business needs and create impactful ML applications
• Actively engage with diverse stakeholders to ensure that solutions are well-integrated and aligned with business goals
• Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models