Research Software Engineer, Google Research
What you'll need to apply
What this employer's standard application typically asks
About this role
Employer-provided description, formatted for easier reading.
Minimum qualifications
- 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.
- Experience in machine learning research, or quantitative research in fields such as mathematics, physical sciences, engineering, or economics.
- Experience with machine learning frameworks (e.g., JAX or PyTorch).
Preferred qualifications
- Master's degree or PhD in Computer Science or related technical fields.
- Publication in journals or conferences in machine learning or quantitative fields such as mathematics, physical sciences, engineering, or economics.
- Experience with sequence modeling, machine translation, speech/audio processing, diffusion language models, or reinforcement learning.
- Experience in building, scaling, and optimizing complex machine learning systems.
- Contribution to major open-source projects related to machine learning such as JAX, PyTorch, HuggingFace Transformers, Kaldi K2, ESPNet.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search.
We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day.
As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
Our team is pioneering new ways to interact with audio by building the next generation of foundational audio models. In this role, your mission is to deliver quality across practical audio tasks while achieving ultra-low latency and extreme cost-effectiveness as we are fundamentally rethinking the stack, focusing on novel sequence modeling architectures, new training methods, and advanced inference algorithms.
You will innovate at the foundational level as our work has the potential to expand across other modalities. You will as a tightly knit, milestone-oriented team within Google Research that relies on close collaboration to push the boundaries of machine learning and turn ambitious research into reality.
Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Responsibilities
- Design, implement, and rigorously test novel modeling and algorithmic improvements for foundational audio models.
- Solve complex, open-ended challenges in audio understanding architectures, sequence modeling, and fast inference.
- Write product or system development code to integrate and evaluate audio capabilities across the ecosystem.
- Participate in or lead design reviews with peers and stakeholders to decide amongst available technologies.
- Triage product or system issues and debug, track, and resolve them by analyzing the sources of issues and their impact on service operations and quality.