• You will help design and build end-to-end machine learning solutions. • You will be working in small and cross-functional product teams, with great opportunities for growth and ownership of projects. • You will be an active member of an internal community, including economists, data scientists, operations research scientists and machine learning engineers, sharing learnings, best practices and research across many domains. • You will develop high impact solutions to support Instacart's ambitious growth plans. • You will work closely with engineers, product managers, other teams, and both internal and external stakeholders, owning a large part of the process from problem understanding to recommending a solution and testing it in controlled experiments. • You will have the freedom to suggest and drive organization-wide initiatives.

• Set long-term technical direction for Kraken’s AI/ML strategy, influencing multiple teams and initiatives. • Own the architecture and evolution of core AI/ML systems, including training, feature management, serving, experimentation, and monitoring. • Lead cross-team efforts to standardize ML development and operational practices across the company. • Drive the design and delivery of complex, high-impact ML systems for use cases such as fraud, risk, personalization, and recommendations. • Partner with senior engineering, product, and data leaders to identify where ML can unlock step-change improvements in scalability and efficiency. • Balance hands-on technical contributions with architectural leadership and design review responsibilities. • Evaluate emerging ML and GenAI technologies and lead their adoption when they provide durable platform-level value. • Mentor Staff and Senior engineers, shaping technical culture and raising the overall quality of ML engineering at Kraken.

• Guide the technical direction of Bondora’s ML engineering stack by selecting, evaluating, and implementing technologies, tools, and processes that improve scalability and reliability. • Lead complex, high risk, or cross departmental projects that directly influence Data Science delivery, risk model performance, and production stability. • Act as the bridge between Data Science, Data Engineering, and Development to identify and solve systemic technical challenges. • Design and build advanced, production grade ML infrastructure and set the engineering standard for the team. • Ensure all ML solutions are secure, observable, resilient, and scalable, following governance, compliance, and operational best practices. • Mentor ML Engineers through code reviews, design sessions, and hands on technical leadership. • Identify weaknesses or inefficiencies in model or data infrastructure and drive company wide improvements. • Represent the ML Engineering team in technical discussions and communicate architectural decisions clearly to stakeholders.

• Design, build, and optimize algorithm in Vertex ai. • Develop scalable data models, Algorithm supporting user 360 views, churn prediction, and recommendation engine inputs. • Lead integration across data sources: MySQL, BigQuery, Redis, Kafka, GCP Storage, and API Gateway. • Implement CI/CD for data pipelines using Git, dbt, and automated testing. • Define data quality checks and auditing pipelines for ingestion and transformation layers. • Mentor and guide junior AI/ML engineers on data modeling, algorithm performance tuning. • Partner with Data Science, ML, and Backend teams to productionize machine learning features in Snowflake. • Work closely with Legal, Security, and Infrastructure teams to ensure compliance, privacy, and governance of user data (PII). • Collaborate with the Director of Data Platforms and product stakeholders to translate business requirements into technical specifications. • Tune algorithm performance. • Establish data partitioning, clustering, and materialized views for fast query execution. • Build dashboards and monitors for pipeline health, job success, and data latency metrics (e.g., via Looker, Tableau, or Snowsight). • Establish and enforce naming conventions, data lineage, and metadata standards across schemas. • Lead code reviews, enforce documentation standards, and manage schema versioning. • Contribute to the company’s evolving data mesh and streaming architecture vision.