Responsibilities

• Build models that reason over source code, textualized binary representations, logs, telemetry, images, video, and commit history to identify root causes of bugs

• Develop bug-inducing commit attribution — pinpointing the change that introduced a defect or crash

• Prototype and ship agentic systems for root cause analysis and automated repair that propose, validate, and iterate on hypotheses and candidate fixes

• Train and fine-tune frontier and open-source foundation models to reason over game- domain artifacts , code, engine state, binary assets, scripts, and runtime telemetry, with a bias toward generalization across game types, projects, engines, and codebases

• Research privacy-preserving and client-specific fine-tuning strategies that protect IP while enabling continuous improvement

• Design multi-dimensional embedding and retrieval systems — across root cause, subsystem, spatial, input, and performance contexts — for issue similarity and correlation

• Translate research prototypes into production code-intelligence services

  You may be a good fit if you
  
  

• Have deep understanding of modern deep learning — transformers, embeddings, and at least one of: code, multimodal, or graph models — with hands-on experience training them at scale on large datasets

• Have strong ML research publications, or a track record of taking research prototypes to production

• Are self-driven with strong research intuition and clear technical communication

• Operate effectively in a fast-paced research environment, and can scope and deliver projects end-to-end

• Enjoy collaborating across research, engineering, and domain experts

  Strong candidates may also have
  
  

• Experience adapting foundation models to specialized technical domains (code, scientific, multimodal)

• Background in software defect analysis, automated program repair, or transferable experience adapting general-purpose models to similar reasoning tasks

• Experience training RL agents to replay game sessions and reproduce bugs in games

• Federated learning or other privacy-preserving ML in production

• PEFT techniques (LoRA, QLoRA, adapters) for efficient fine-tuning

• RAG systems with vector databases

• Gaming or developer-tools background; familiarity with Perforce, JIRA, Unreal, or Unity

How We're Different

We believe that AI research has the potential to transform workplaces, improve human life, and increase efficiency, allowing people to spend their time on truly valuable activities. At LocusX, we prioritize innovation through collaborative cross-disciplinary initiatives within a workplace environment that fosters personal growth, mentorship, respect, and initiative. Our collaborative culture enables employees to reach their potential while delivering superior customer experiences and product excellence.

Why Join Us

• Real ownership and the chance to build something meaningful!

We encourage you to apply even if you do not believe you meet every single qualification. 

Not all strong candidates will meet every single qualification as listed. We place a strong emphasis on attitude and determination over excessive qualifications and value diverse perspectives on our team, so we urge you not to exclude yourself prematurely and to apply.