Manager, Research Engineering (Foundational Research)

Foundational Research is the dedicated core Machine Learning research division of Thomson Reuters. We focus on advanced algorithms and training techniques for Large Language Models (LLMs) and Data-Centric Machine Learning. While our Research Scientists push the boundaries of what models can do, the Research Engineering team defines how we build, train, and scale them. We are looking for an Engineering Manager who can lead a team of high-performing engineers to build the "nervous system" of our lab, designing the distributed training infrastructure, LLMOps pipelines, and experimental frameworks that power our state-of-the-art research. About the Role: As the Manager of Research Engineering , you will sit at the critical intersection of cutting-edge academic research and robust software engineering. You will lead the team responsible for turning experimental code into scalable assets and ensuring our researchers have the compute and tooling required to compete with the world’s top AI labs. Engineering Leadership: Manage, mentor, and grow a team of Research Engineers. You will foster a culture of engineering rigor (code quality, testing, CI/CD) within a fast-paced, experimental research environment. Infrastructure & LLMOps Strategy: Own the technical strategy for our LLM training and inference infrastructure. This includes managing distributed compute clusters (LambdaLabs/AWS), orchestration platforms (e.g., ClearML, Kubernetes), and data pipelines. Vendor & Governance Management: Lead the evaluation and onboarding of external technology vendors. You will act as the primary liaison with Sourcing, Procurement, Risk, and Privacy teams to ensure our tooling infrastructure is compliant, secure, and procured efficiently, unblocking the research team from administrative overhead. Bridge Research & Production: Act as the primary translator between the Foundational Research team and the wider Platform/Engineering organizations. You will ensure that research innovations are architected in a way that allows them to be successfully handed off to production teams. Product-Minded Engineering : Drive a product-oriented mindset within the research engineering team, ensuring that infrastructure, tooling, and experimental frameworks are designed not just for technical excellence but with clear user outcomes in mind. Champion practices like defining success metrics for internal platforms, gathering feedback from product partners, and prioritizing work based on impact to downstream product value. Operational Rigor: Remove ambiguity for your team by translating high-level research goals into concrete engineering roadmaps. You will implement observability, alerting, and resource management strategies to ensure efficient use of our massive compute budget. Hands-on Contribution: While primarily a leader, you are willing to roll up your sleeves to review code, debug distributed training failures, and architect complex system integrations. About You You are not just a manager; you are a builder who understands the unique challenges of Deep Learning infrastructure. You bring a product-oriented lens to engineering — you've led teams that didn't just ship features but owned outcomes, defined success criteria, and iterated based on user feedback, whether those users were internal researchers or external customers. Required Qualifications: Education: BSc, MSc, or PhD in Computer Science, Software Engineering, or a related field. Experience: 7+ years of software engineering experience, with at least 3+ years leading or managing engineering teams. Product-Oriented Leadership: Demonstrated experience leading engineering efforts with a product mindset, defining roadmaps tied to user/business outcomes, working cross-functionally with product and business stakeholders, and making build-vs-buy decisions grounded in impact rather than purely technical preference. Technical Expertise: Deep proficiency in Python and modern software development practices. Hands-on experience with Distributed Training infrastructure (Multi-node GPU training, Kubernetes, vLLM). Familiarity with Deep Learning frameworks (PyTorch). Experience with MLOps tools and experiment tracking (e.g., ClearML, MLFlow, Weights & Biases). Research Fluency: Ability to read technical research papers and translate them into engineering requirements. You don't need to write the paper, but you need to understand the architecture required to support it. Operational Mindset: Experience managing cloud resources (AWS/Azure/GCP) and optimizing for cost/performance. Preferred Qualifications: Experience working in a Research Lab or "0-to-1" innovation environment. Experience owning the end-to-end lifecycle of an internal developer platform or ML tooling product, including defining adoption metrics and iterating based on user research. Background in Platform Engineering. Experience contributing to open-source LLM or NLP libraries. As part of the application process, please include a brief written blurb (300–500 words) describing a sufficiently complex or technically interesting project that you have led or played a significant leadership role in delivering. This should be a project that spanned at least three to six months of active development — not a weekend hack or a single-sprint feature. In your blurb, outline the problem you were solving, the high-level architecture or approach your team took, the key trade-offs or compromises you navigated, and the outcome (successful or otherwise). We are equally interested in projects that didn't go as planned — what matters is your ability to articulate the complexity, the decisions you made, and what you learned. This blurb will serve as the basis for a deep-dive discussion during the interview process, so choose a project you are comfortable exploring in detail. There is no need to share proprietary or confidential information; a high-level description with anonymized details is perfectly fine.