Mid
Implement specialized ML solutions — speech/audio, RL, or ML infra — inside Google Research
A Software Engineer role in Google Research, implementing solutions in a specialized ML area (speech/audio, reinforcement learning, or ML infrastructure) and contributing to model optimization and data processing at a team that publishes research, open-sources projects, and feeds work into Google products. Requires 2 years of software development experience plus 1 year in a specialized ML area and 1 year with ML infrastructure (deployment, evaluation, optimization, debugging).
Step into this interview
Free · a live voice mock calibrated to this exact role
Likely format
Standard Google SWE loop: recruiter screen, 1-2 coding phone screens (DS&A), then onsite/virtual onsite with coding, ML/specialization-focused technical rounds, and a Googleyness/leadership behavioral round.
What this interview tests
- Specialized ML area depth: speech/audio, reinforcement learning, or ML infra
- Model deployment, evaluation, and debugging in production or research pipelines
- Data processing and model optimization
- Turning research code into reusable, robust software
- Core data structures and algorithms
Common question themes
Describe a speech/audio, RL, or ML infra system you built and how you evaluated it
Walk through deploying and debugging a model in a production or research pipeline
How have you optimized data processing or model performance under real constraints
Tell me about productionizing research code for reuse by others
Coding: data structures and algorithms
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