Software Engineer III, AI/ML, Core
In most instances, this position requires in-person interviews as part of the hiring process.
In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:
US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Responsibilities
Learn more about benefits at Google .
In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:
- Health, dental, vision, life, disability insurance
- Retirement Benefits: 401(k) with company match
- Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
- Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
- Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
- Baby Bonding Leave: 18 weeks
- Holidays: 13 paid days per year
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
- 1 year of experience with one or more of the following: LLM systems, speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 1 year of experience with AI/ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures or algorithms.
- Experience developing accessible technologies.
US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Responsibilities
Learn more about benefits at Google .
- Design, develop, test, and deploy robust, production-grade ML models and infrastructure.
- Build, own and maintain scalable data pipelines, feature stores, and automated training/evaluation workflows to support continuous model iteration.
- Partner closely with Product Managers, Data Scientists and other ML Engineers to translate requirements into concrete technical solutions.
- Monitor, benchmark, and optimize production ML models for latency, throughput, and cost-efficiency without compromising prediction quality.
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