Research Engineer

• Design, prototype, and implement agentic AI systems that perform reliably across complex enterprise workflows
• Build compound AI architectures that combine planning, tool use, retrieval, memory, evaluation, orchestration, and execution
• Investigate how agents reason, coordinate, recover from errors, and interact with external systems under real-world constraints
• Develop evaluation frameworks that measure agent behavior, task completion, reliability, robustness, and failure modes
• Create tools and abstractions that make agent behavior easier to observe, debug, test, and improve
• Partner with AI Researchers to explore new agent architectures and with AI Engineers to harden successful approaches for production use
• Integrate agents into customer APIs, applications, data platforms, and operational workflows
• Communicate clearly with internal teams and customer stakeholders about agent capabilities, limitations, tradeoffs, and risks

• Design structured experiments with real controls and statistical rigor
• Build regression test coverage to know when a global prompt change is breaking things across workflows
• Create a pipeline of validated, high-confidence improvements for engineering
• Work with the team to prioritize what gets shipped based on experiment results
• Own prompt design across Mia's agent workflows
• Stay current on prompting research, bring back ideas worth testing
• Prototype new approaches locally
• Collaborate with Product and Engineering teams
• Translate experiment results into clear recommendations
• Build out evaluation templates and frameworks

AI Research Engineer

• Conduct advanced research in wireless communication systems, including PHY/MAC layers, radio architectures, networking protocols, and end-to-end system design
• Design and evaluate AI/ML-driven wireless algorithms (e.g., semantic communications, intent based and agentic AI communications, GenAI-assisted network optimization, resource allocation, edge intelligence)
• Develop and implement link-level and system-level simulations using tools such as MATLAB, Python, Sionna, or equivalent frameworks
• Build proof-of-concept prototypes and validate concepts on practical platforms or testbeds
• Collaborate with cross-functional research teams across wireless, AI, standards, and prototyping groups

Research Engineer – Wireless, AI/ML

• Lead research and engineering efforts to improve core conversational capabilities in production, including instruction following, retrieval, memory, and long-horizon task completion
• Build and iterate on end-to-end models and pipelines that optimize for quality, efficiency, and user experience
• Partner with platform and product engineers to integrate new models into production systems
• Break down ambiguous research ideas into clear, iterative milestones and roadmaps.
• Mentor other researchers/engineers, set technical direction, and establish best practices for applied research and engineering

Staff Research Engineer, Voice – Speech

• Lead adversarial testing of AI by designing, scoping, and executing red team assessments against our AI ecosystem using a risk-based prioritization approach to discover and address vulnerabilities before they can be exploited.
• Innovate in AI attack techniques by combining cutting-edge academic research with proven offensive security methods to establish new Tactics, Techniques, and Procedures, and operationalize emerging research to keep assessments aligned with the state of the art.
• Build and scale security tooling using an automation-first philosophy, driving initiatives to shift security testing left by sharing purpose-built tools with AI security stakeholders across Engineering, Research, and Ethics.
• Serve as a strategic partner across the company, providing an offensive security perspective to guide product development, support corporate governance, and contribute to policies such as Salesforce's Generative AI Security Standard.

Adversarial AI, Research Engineer

• Designing and implementing new information architecture strategies
• Performing finetuning and reinforcement learning for language models
• Building knowledge base eval sets to identify failure modes of language models
• Designing and evaluating advanced agentic search capabilities

Research Engineer, Knowledge Team

• Own a research direction for search post-training problems
• Build instrumentation for controlled experiments
• Design evaluations to distinguish reasoning over evidence
• Drive optimization rigor across the stack
• Collaborate with researchers across various teams
• Set the bar for experimental standards

Research Engineer, Search and Knowledge

• Build and improve RL training infrastructure
• Identify and remove bottlenecks across the RL stack
• Partner with researchers and engineering teams
• Own reliability and performance of research runs

Research Engineer, Machine Learning – RL Velocity

• Build & Deploy Product Analytics Infrastructure: Design and implement scalable data pipelines and analytics systems that transform customer feedback and product traces into actionable insights across AI-powered legal products.
• Enable AI Evaluation at Scale: Build data workflows to enable the deployment of automated evaluation metrics as production analytics to continuously track product quality, detect errors, and alert teams to regressions before they impact customers.
• Establish Data Governance & Quality Standards: Develop technical governance infrastructure for manual and automated review of AI product data, particularly for small and medium law firms, ensuring data quality, security, and compliance.
• Drive Metric Development: Analyze product traces and customer feedback to identify quality issues and patterns that inform the development of new evaluation metrics and feed into product roadmap decisions.
• Support Cross-Functional Teams: Partner closely with Product Scientists, Research Engineers, and Subject Matter Experts to implement configurations, build reporting dashboards, and create self-service tools for metric implementation.
• Advance AI Evaluation Best Practices: Contribute to the development and scaling of automated evaluation capabilities and establish best practices for AI evaluation and analytics across Thomson Reuters pillars (Legal, Tax & Accounting, and Reuters News).

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