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.
Individual contributor role focused on causal inference and marketplace measurement work impacting billions of dollars in transaction value on a large travel platform.
Locations (2)
Seattle, Washington, United States
Austin, Texas, United States
Compensation
The total cash range for this position in Seattle is $119,000.00 to $166,500.00. Employees in this role have the potential to increase their pay up to $190,500.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role. The total cash range for this position in Austin is $119,000.00 to $166,500.00. Employees in this role have the potential to increase their pay up to $190,500.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.
Responsibilities
Build econometric models (panel data methods, DiD, RDD, IV) to help measure elasticity, changes in supplier behavior, and customer lifetime value
Support the development of long-term impact measurement approaches
Assist in designing and executing causal measurement frameworks for marketplace interventions, including geo experiments, difference-in-differences, synthetic controls, and instrumental variable approaches
Lead the enterprise analytics organization, delivering trusted business intelligence, operational reporting, executive dashboards, and advanced analytical solutions.
Compensation
The compensation range for this position is: $180,000.00 - $200,000.00 per year.
Responsibilities
Lead and develop teams of Analytics Engineers, Data Analysts, and Business Intelligence developers
Define and execute the enterprise analytics strategy
Partner with business leaders to identify reporting and analytics priorities
Deliver executive dashboards, operational scorecards, and self-service analytics solutions
Establish enterprise reporting standards, KPIs, and business metrics
Requirements
Bachelor's degree in Computer Science, Information Systems, Mathematics, Statistics, Business Analytics, or related field.
Join an industry-leading team of veterans building the pinnacle of animation pipelines and technologies. As a Technical Animator III, you'll bridge the gap between technical innovation and creative expression.
Responsibilities
Support cutting-edge animation pipelines and tools that empower multiple animation teams.
Collaborate with cross-functional teams to bring characters and vehicles from concept to polished in-game implementation in StarEngine.
Implement character interaction systems, transitions, and behaviours that delight players.
Pursue a cross-disciplinary research project in cross-scale learning from heterogeneous evidence (lab-scale to pilot-scale) relevant to the DOE Office of Critical Minerals and Energy Innovation.
Develop methods that combine evidence across different experimental scales, conditions, and modalities to make predictions at unseen physical or operational scales.
Develop innovative learning methods, including multi-modal and surrogate modeling approaches, transfer learning techniques, physics-informed ML, and/or statistical learning methods.
Work in multi-disciplinary teams and collaborative software development.
Partner with ML teams to help integrate causal inference into production systems, supporting ranking algorithms, pricing optimization, and recommendation engines
Translate econometric findings into clear, well-organized analysis and visualizations that support product decisions
