Building agentic systems for Epiq AI Labs’ legal AI platform, including orchestration runtime, retrieval layers, and evaluation infrastructure.
Compensation
Compensation range for this role is $180,000 - $285,000 annually
Responsibilities
Design the agentic platform: orchestration of single agents and parallel sub-agents, durable state and memory across long-running jobs, MCP-based tool integration, and trace-level observability.
Build the safety layer agents run inside: tool registration and permissioning, sandboxing, guardrails and output validation, and defenses against prompt injection from retrieved content.
Build agent workflows with multi-step reasoning, tool use, human-in-the-loop checkpoints, and recovery from partial failure during long-running execution.
Evaluate developments in model capabilities, agent-design patterns, and evaluation practices, and translate them into platform architecture.
Design retrieval-augmented generation systems with hybrid search over legal corpora and citation grounding to specific sources.
Lead AI consulting engagements, advising clients on AI strategy, transformation, and adoption, while driving business outcomes and managing client relationships.
Responsibilities
Act as a trusted client advisor to identify AI transformation opportunities, business pain points, and value cases.
Shape AI strategy, target operating model, governance, adoption, responsible AI, and transformation roadmaps for clients.
Translate client needs into practical AI solutions in partnership with architects, FDE, delivery, and partner teams.
Develop proposals, business cases, ROI models, proof-of-concept plans, and executive-ready client materials.
Support opportunity qualification, deal shaping, solutioning, mobilization, and transition into delivery.
Lead the Foundational Models team to design, develop, evaluate, and productionize machine learning models that power personalization and recommendation systems at Stitch Fix.
Compensation
$200,000—$237,000 USD
Responsibilities
Design, develop, evaluate, and productionize machine learning models that improve Stitch Fix's personalization and recommendation systems.
Advance foundational modeling capabilities, including client and item representations, embeddings, retrieval, ranking, recommendation models, and assortment generation.
Explore and apply LLMs and machine learning techniques—including deep learning, representation learning, multimodal modeling, and generative approaches—to improve systems.
Own work across the full ML lifecycle, from problem formulation and data exploration through modeling, experimentation, deployment, monitoring, and iteration.
Design rigorous offline evaluations and online experiments to understand model performance, measure client and business impact, and communicate results to stakeholders and leadership.
Requirements
Bachelor’s Degree in a quantitative field such as Computer Science, Statistics, Physics, Mathematics, or a related field
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