Research Engineer: LLMs, ML Systems & Production

Socket.dev; Mountain View (CA); On-site; USD 174,000 - 252,000; Full time

Benefits offered by this job

Equity

Bonus target

Benefits

Job summary

Google DeepMind and Google are recruiting research-focused Software Engineers embedded across the company to run large-scale experiments, prototype architectures, and deploy promising ideas quickly and broadly.

Engineers contribute to exciting AI challenges, including natural language processing, data mining, and performance analysis, while staying connected to research through collaborations and publications.

Qualifications

• Bachelor's degree in Computer Science, Data Science, AI, ML, or related field, or equivalent practical experience.
• 3 years of experience in machine learning, focusing on LLMs or information retrieval.
• 3 years of experience with software engineering in Python, C++, JAX, or PyTorch, including multi-stage training pipelines.

Responsibilities

• Uncover strategic opportunities at the intersection of Gemini, Search, factuality, continual learning, deep research, and domain internalization.
• Analyze models and model-driven products beyond current leaderboards—understand complex problem spaces and create new ways to measure ideal behavior.
• Use empirical findings to develop practical interventions and modeling innovations to improve our models across downstream surfaces.
• Collaborate with product teams like Search and YouTube to scale research innovations into production environments, optimizing for quality and efficiency.

Skills

Machine learning

Python

C++

LLMs/IR research

Education

Bachelor's degree or equivalent experience

Tools

JAX

PyTorch

Multi-stage training pipelines

Job description

Google DeepMind and Google are recruiting research-focused Software Engineers embedded across the company to run large-scale experiments, prototype architectures, and deploy promising ideas quickly and broadly.

Engineers contribute to exciting AI challenges, including natural language processing, data mining, and performance analysis, while staying connected to research through collaborations and publications.