Lead in a player-coach capacity in execution for Stitch Fix’s next-gen Data, ML, and GenAI platforms
Contribute towards modernization of data and ML foundations to support unified signals, adaptive models, experimentation velocity, and scalable AI/ML workloads.
Provide foundational APIs, SDKs, frameworks, and self-service tools that make it easy for data scientists, ML engineers, analysts, and application teams to build and deploy AI solutions quickly, safely, and at scale.
Partner with Data Science, Engineering, and Product teams to translate Data/ML/GenAI platform capabilities into production-grade features and intelligent experiences that deliver measurable business value.
Drive responsible AI and data adoption by creating reusable templates, documentation, and enablement programs.
Contribute towards improving governance practices including data contracts, lineage, metric definitions, access policies, and responsible AI guardrails - for trust, safety, and compliance.
Ensure operational excellence through platform reliability, performance, observability, cost efficiency, and simplification of legacy systems.
Lead and develop high-performing engineering teams fostering a culture of clarity, excellence, and trust.
Balance speed of innovation with platform stability, ensuring engineering efforts are tightly aligned to business priorities and long-term client value.

Requirements

5+ years in software, data, ML, or platform engineering; 1+ years leading engineering individual contributors is a plus.
Demonstrated success contributing towards large-scale data platforms, ML platforms, or AI/GenAI platforms in cloud environments.
Experience delivering platform modernization, unification, and multi-year architectural transformation.
Strong software engineering foundation, with experience designing and building large-scale distributed systems and resilient, high-quality APIs and services using modern programming languages and cloud-native architectures.
Track record operating and evolving modern data infrastructure, including some of the following: distributed compute and storage technologies (Spark, Trino, Iceberg), real-time processing frameworks (Kafka/Flink), metadata / catalog systems, and Kubernetes-based orchestration.
Expertise across the ML lifecycle - feature engineering, training pipelines, model deployment and serving, monitoring, validation, fine-tuning, and MLOps best practices.
Proven capability in building self-service platform abstractions and tooling that enable teams to develop, experiment, and deploy data and ML products efficiently.
Experience with modern GenAI architectures - semantic retrieval, knowledge-grounded indexing, LLM orchestration, agent workflows, and evaluation frameworks.
Familiarity with modern ML frameworks like PyTorch and Ray is a plus.
Strategic thinker able to align platform investments with business priorities and emerging AI opportunities.
Potential to be a strong people leader with a track record of contributing to make inclusive, high-performing engineering teams.
Excellent communicator who can influence both technical and business stakeholders across domains.

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

data engineeringmachine learningplatform engineeringAPI developmentdistributed systemscloud-native architectureMLOpsfeature engineeringmodel deploymentreal-time processing

Soft Skills

leadershipstrategic thinkingcommunicationteam developmentinfluencecollaborationtrust buildingclarityexcellenceadaptability