Senior Machine Learning Engineer II, Search & Recommendations Ranking

About the role

This position focuses on developing ranking systems for Instacart search, guiding items from initial user input to final delivery. Professionals in this role will manage the full lifecycle of search relevance, working directly with data, infrastructure, and business teams. The position requires a deep commitment to improving user satisfaction and operational efficiency. You will architect solutions that transform how users discover and select products on the platform. The work demands a balance of creativity and rigorous analysis to solve complex problems. Daily responsibilities include iterating on models that directly impact the customer experience. You will be accountable for driving innovation within the search and recommendations domain. This role is pivotal in shaping the future of how Instacart matches shoppers with their desired items.

Value-Aware Model Development

A core responsibility is designing value-aware models that balance long-term customer retention with immediate business outcomes. This involves creating systems that prioritize sustainable value, not just immediate clicks. Daily work includes refining these models to ensure they meet evolving business needs. The objective is to build a framework that understands the broader context of each interaction. You will analyze intricate patterns in user behavior to inform model adjustments. The goal is to create algorithms that foster loyalty and trust. This approach ensures the platform grows sustainably. Every feature and parameter will be scrutinized for its contribution to long-term value.

Architectural Leadership

The role involves shaping architecture that unifies query understanding, personalization, and merchandising into a single, adaptive platform. This integration is critical for creating a seamless user journey. The work ensures that all components of the shopping experience speak with one coherent voice. Collaboration is central to defining and maintaining this structure. You will lead discussions on system design and technical trade-offs. This leadership extends to ensuring the architecture is scalable and maintainable. The position requires influencing technical direction across large, cross-functional teams. You will be a key architect of the core ranking infrastructure.

Inference and Optimization

Guiding inference layer decisions is a key duty. This includes implementing goal-aware re-ranking and enforcing strict quality constraints. Teams must consistently meet millisecond latency targets to ensure a smooth user experience. This requires a careful balance of technical precision and performance awareness. Every decision in this layer impacts the final outcome. You will optimize the pipeline for speed and accuracy without compromise. Profiling and debugging performance bottlenecks will be a regular task. The work ensures that complex models translate into real-time, actionable results. You will validate that the serving layer meets all operational standards.

Evaluation and Experimentation

Championing robust evaluation practices is essential for success. This includes utilizing online experiments, counterfactual checks, and long-horizon cohort analysis. The focus is on measuring incremental GTV and understanding true business impact. These practices inform future iterations and validate strategic choices. Data drives all optimization efforts. You will design rigorous experiments to test new ranking hypotheses. Analysis of results will guide the direction of future model development. Establishing clear metrics for success is a primary task. You will ensure that evaluation methodologies are statistically sound. This discipline prevents wasted effort and misaligned features.

LLM-Driven Feature Engineering

Advancing feature engineering involves leveraging LLMs to enrich queries and items. This work is vital for improving long-tail coverage and reducing cold-start problems. LLMs help generate features that capture nuanced meaning and intent. The insights gained directly improve the accuracy of the ranking models. This represents a forward-looking approach to data enrichment. You will explore novel techniques for integrating LLM outputs into the model pipeline. This work pushes the boundaries of traditional feature engineering. The aim is to harness generative AI for practical ranking improvements. You will assess the reliability and scalability of these new features. This role sits at the intersection of cutting-edge AI and production systems.

Cross-Functional Partnership

Partnering with product, ads, and infrastructure teams is a fundamental part of the job. The role translates high-level business goals into specific ranking policies and clear ROI. This requires fluency in both technical and commercial language. Success is measured by the ability to align technical output with strategic priorities. These partnerships ensure solutions are practical and impactful. You will act as a liaison between engineering and business stakeholders. Communication skills are as important as technical acumen in this role. You will gather requirements and translate them into technical specifications. This collaboration ensures that the final product meets user and business needs.

Team Development and Mentorship

Mentoring engineers on ranking, causal inference, and scalable serving patterns is a core responsibility. The goal is to elevate the entire team's expertise in these areas. Sharing knowledge strengthens the organization's technical foundation. This mentorship role helps build a resilient and skilled engineering culture. It ensures best practices are documented and disseminated. You will lead code reviews and technical design sessions. Your guidance will help junior engineers grow their careers. Fostering a learning environment is a key part of the position. You will contribute to the professional development of your peers. This role involves both leading and following within the team dynamic.

Qualifications and Next Steps

The ideal candidate brings extensive experience applying machine learning at scale. A proven track record in improving ranking systems is mandatory. Candidates must demonstrate leadership in technical environments. Detailed information regarding specific requirements and expectations is available All candidates are encouraged to review this documentation thoroughly.

About the company

Instacart is an American delivery company that operates a grocery delivery and pick-up service via an app and website. Founded in 2012, Instacart partners with over 1,400 retail banners across North America.