This role pays less than
91%
of similar roles. Most pay
$197,981–$260,050
— the shaded band above.
At the midpoint, this role pays about
$158k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Instacart
Instacart (Maplebear Inc.) is a North American grocery technology company that operates an online marketplace and delivery service connecting customers with personal shoppers who pick and deliver orders from local retailers. It also sells retail e-commerce software and advertising services to grocers and brands.
Instacart currently has
49 open roles
on FindRole.
Listed pay typically runs
$196,000–$207,000
across 49 roles with salary data.
Machine Learning Engineer II, Ads - Response Prediction joins the Ads Response Prediction team to develop and enhance systems ensuring relevant and engaging experiences across various platforms. This role involves designing, developing, and deploying machine learning solutions including data pipelines, model architectures, and serving integrations for ads selection, ranking, bidding, and auction. The engineer will translate business observations into research directions, manage projects from prototyping to launch, and contribute to technical rigor through design reviews and paper sharing. Key responsibilities involve solving problems in search retrieval, sequential modeling, generative retrieval, pCTR models, and foundation models. Required skills include a graduate degree in a quantitative field, proficiency in SQL, Spark, and Pandas, and experience with classical machine learning and deep learning. Preferred expertise includes Transformer architecture, LLM integrations, agentic workflows, and large-scale online recommendation systems.
What does a Machine Learning Engineer earn in California?
Median $246394 from 172 postings across 28 companies.
Design, develop, and deploy machine learning solutions including data pipelines, model architectures, and serving integrations.
Formulate and scope ambiguous modeling problems from first principles into well-defined research directions.
Translate business observations like miss-calibration patterns and cold-start issues into technical ML goals.
Develop and refine systems for ads selection, ranking, bidding, and auction across consumer surfaces.
Build and optimize models for search retrieval, sequential modeling, and LLM integrations.
Improve operational efficiency by refining algorithms and machine learning models.
Present findings internally through design reviews, paper sharing, and experiment retrospectives.
What we're looking for
Must have a graduate degree (Master's or PhD) in machine learning, statistics, computer science, information retrieval, or a related quantitative field.
Must possess strong programming skills and fluency in data manipulation tools like SQL, Spark, and Pandas.
Must have proficiency in both classical machine learning and deep learning tools.
Must possess strong analytical skills and problem-solving abilities.
Must be a strong communicator capable of collaborating with diverse stakeholders across all levels.
Preferred: 1-2 years of industry experience using machine learning to solve real-world problems with large datasets.
Preferred: Knowledge of sequential modeling, Transformer architecture, Foundation Models, LLM integrations, and agentic workflows.
Preferred: Experience in building large-scale online recommendation systems.