Research Engineer, Information Quality
Apply at source. Google DeepMind handles the application directly; Houtini doesn't take a fee from candidates or companies. We curate which companies appear; the listings come from yubhub.
What the team is looking for.
Job Title
Research Engineer, Information Quality
Summary
At Google DeepMind, our research team is dedicated to tackling the most complex challenges in online information quality. We strive to advance the state of the art by developing innovative solutions to detect manipulated media and misleading narratives, ensuring the integrity of digital discourse.
Responsibilities
To succeed in this role, you will need to be passionate about advancing information literacy using machine learning and other computational techniques. You'll join an interdisciplinary team of domain experts, ML researchers, and engineers to research and build systems and tools to assess the trustworthiness of media (images, audio, and videos) on the internet.
Key responsibilities:
- Plan and perform rapid prototyping of machine learning techniques applied to determining authenticity of media information.
- Undertake exploratory analysis to inform experimentation and research directions.
- Engage with product teams to drive the development of our research.
- Implement tools, libraries, and frameworks to speed up and enable new research.
- Report and present research findings, software developments, experimental results, and data analysis clearly and efficiently.
- Collaborate with internal and external scientific domain experts.
Requirements
In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:
- Master’s degree in Computer Science, Electrical Engineering, Science, or Mathematics, or equivalent experience.
- Applied experience with machine learning, preferably modern deep learning techniques (e.g., Transformers, Diffusion, LLMs).
- Programming experience.
- Quantitative skills in math and statistics.
- Experience exploring, analysing and visualising data.
Preferred Qualifications
In addition, the following would be an advantage:
- Experience in multimodal learning, including the training and deployment of large-scale models.
- Experience developing AI agents.
- Experience with Large Language Models, prompt engineering, few-shot learning, post-training techniques, and evaluations.
- A proven track record of research or engineering achievements, such as publications in peer-reviewed conferences or journals.
Benefits
The US base salary range for this full-time position is between $174,000 USD - $252,000 USD + bonus + equity + benefits.
- Machine Learning
- Deep Learning
- Python
- Quantitative Skills
- Data Analysis
- Multimodal Learning
- AI Agents
- Large Language Models
- Prompt Engineering
- Few-Shot Learning
Other roles you might consider.
Filtered through the same AI-companies allowlist.
Member of Technical Staff (AI Policy and Strategic Initiatives)
Perplexity
Member of Technical Staff (AI Software Engineer, Agents)
Perplexity
Senior/Staff Applied AI Engineer, Fullstack
Mistral AI
Applied Scientist / Research Engineer
Mistral AI
Applied AI, Machine Learning Engineer
Mistral AI
Applied AI Engineer, Fullstack
Mistral AI
New to AI work? Start with these.
Six pieces of orientation. Most AI-company job specs assume you've done this kind of hands-on work already. If you haven't, an afternoon with one of these is the cheapest way to close the gap.
Claude Desktop, from zero.
The agentic-AI assistant most of the people you'd be working alongside use every day. Install, configure, first useful prompts.
What MCPs areThe best MCPs for Claude Desktop.
MCP servers extend an AI assistant with tools and data. The catalogue most teams use. Useful technical context for any AI-engineering role.
Code with AIClaude Code, the complete beginners' guide.
The CLI for AI-paired development. Required reading if you're applying for any engineering role that mentions agents, or any role full stop.
Run a local modelHow to set up LM Studio.
Running a model on your own machine teaches you more about how AI products work in three hours than a year of using ChatGPT will.
The hardware realityBeginner's guide to AI hardware.
What the infrastructure under the model actually looks like. Useful context for infrastructure, applied-AI and hardware roles.
Browse the stackMCP catalogue.
Eleven MCP servers Houtini maintains or recommends. Each detail page describes a real piece of working AI infrastructure.