Staff Research Engineer, Applied ML
Job Summary
Job Description
- 8+ years software development experience
- 5+ years building large-scale ML systems
- 5+ years experience in distributed development
- Proficiency in C++ or Python
- Expertise in ML algorithms and techniques
- Lead a new team of ML engineers and researchers
- Experience with generative models
- Experience with RL algorithms and frameworks
- Experience leading high-performing teams
- Publications in ML/AI conferences or journals
9 skills required
Job Details
Minimum qualifications:
- Bachelor's degree or equivalent practical experience
- 8 years of experience in software development and with data structures/algorithms.
- 5 years of experience building and architecting large-scale, production quality Machine Learning (ML) systems.
- 5 years of experience in distributed development and large-scale data processing.
- Experience coding in either C++ or Python.
- Experience with ML fundamentals, algorithms, and techniques, including supervised, unsupervised, and reinforcement learning, and experience in areas like natural language processing (NLP), computer vision, and generative AI.
Preferred qualifications:
- Experience with generative models (e.g., diffusion models, GANs, transformers) for various media formats (e.g., text, image, video, audio), including prompt engineering, fine-tuning, and evaluation techniques.
- Experience with RL algorithms and frameworks, including policy gradient methods, Q-learning, and actor-critic architectures.
- Experience building and leading high-performing research or engineering teams, fostering a positive and inclusive culture.
- Experience being published in ML/AI conferences or journals, demonstrating a strong research background and ability to communicate complex technical concepts effectively.
- Familiarity with agent-based architectures, tool use, reinforcement learning, and techniques for evaluating and optimizing agent behavior.
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.
The Domain Applied ML team is an impactful group within Core ML, dedicated to accelerating the adoption of cutting-edge ML/AI across Google. We bridge the gap between research and production by developing standardized, efficient ML solutions in critical domains like parameter-efficient tuning, multimodal modeling, media generation, LLMs, and recommender systems.
The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Responsibilities
- Build and lead a new team of ML engineers and researchers in London.
- Collaborate with Google Research and DeepMind to identify and prioritize emerging research areas.
- Conduct applied research on emerging ML/AI topics and drive the adoption of new AI technologies across Google products.
- Develop and evaluate ML models for pilot projects and scalable solutions.
- Develop a strategic roadmap for translating research into practical solutions.
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