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Lead Research Engineer

LinkedIn Thomson Reuters Eagan, MN
Not Applicable Posted March 26, 2026 Job link
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Requirements
  • You are a fit for the position of Lead Research Engineer if your background includes:
  • At least 7 years of software engineering experience.
  • At least 2 years of building large scale data processing pipelines, distributed search and retrieval systems.
  • At least 2 years of working on Deep Learning or LLM related products or solutions.
  • Demonstrate ability to provide technical leadership and influence without formal authority
  • Have a deep understanding of Python software development stacks and ecosystems.
  • Demonstrate ability to write clean, reusable, maintainable and well-tested code
  • Demonstrate a desire to learn and embrace new and emerging technology
  • Ability to collaborate, communicate effectively, and work as part of a multi-disciplinary team.
  • Keen interest in real-world applications and impact.
  • Strong Experience With Cloud Computing Development (AWS Preferred).
  • Have experience with Agile Methodologies.
  • Have experience implementing CI/CD, DevOps, and SDLC concepts in the development of an application.
Preferred Skills
  • Have experience integrating Deep Learning and LLM solutions into production-grade software and have an understanding of ModelOps and MLOps principles
  • Had previous exposure to Natural Language Processing (NLP) problems and have familiarity with key tasks such as Named Entity Recognition (NER), Information Extraction, Information Retrieval, etc.
  • Can understand and translate between language and methodologies used both in research and engineering fields
  • Hands-on experience in other programming language/scripting langugage and development stack (Java, Rust, Scala, Typescript, etc.)
  • Expertise with modern deep learning frameworks for distributed training (Transformers, Accelerate, DeepSpeed, FSDP, PyTorch DDP, TorchTune, LLamaFactory), optimizing large-scale model training on multi-GPU/multi-node clusters.
  • Experience identifying and resolving bottlenecks in training and inference pipelines, familiarity with GPU memory management, OOM debugging and compute-communication overlap
  • Experience optimizing LLM inference for throughput and latency, hands-on with inference frameworks (vLLM, TensorRT, TensorRT-LLM, DeepSpeed Inference) and knowledge of quantization techniques (INT8, GPTQ, bitsandbytes)
  • Experience with graph database technologies like Neo4j, Amazon Neptune
Education
  • (Not required) – A bachelor’s degree in computer science, Related Field, or Equivalent Experience
  • (Not required) – At least 7 years of software engineering experience.