Remote / Hybrid preferred
About Brave
Brave is on a mission to protect the human right to privacy online. We’ve built a free web browser that blocks creepy ads and trackers by default, a private search engine with a truly independent index, a browser-native crypto wallet, and a private ad network platform that directly rewards you for your attention. And we’re just getting started. 90 million people have switched to Brave for a faster, more private web. Millions more switch every month.
Summary
Join Brave’s mission to revolutionize web browsing through AI. We’re looking for an experienced ML Engineer to build next-generation features that serve nearly 100 million users worldwide. You’ll work with state-of-the-art language models, collaborating across teams to ship innovative AI capabilities that make the browser smarter and more capable—all while maintaining our privacy-first principles.
Core Responsibilities
- Evaluate, integrate, and deploy state-of-the-art language models for Leo and other browser AI capabilities, including both cloud-based and on-device deployment scenarios
- Design, optimize, and maintain ML inference pipelines for browser-integrated AI features, with focus on reducing deployment costs and improving model performance
- Develop and train custom ML models for browser-specific use cases such as content classification and search optimization using techniques like LoRA and DPO, including distributed training setups
- Generate synthetic data for training data augmentation and model evaluation
- Collaborate with browser engineering teams to seamlessly integrate AI capabilities into core product features while maintaining performance and privacy standards
- Collaborate with product and design teams to define, prototype, and ship new AI-powered features including text-to-speech, image generation, and enhanced tool calling capabilities
- Implement and optimize model serving infrastructure using frameworks like vLLM, ONNX Runtime, and Nvidia Triton to achieve production-scale performance requirements
- Collaborate with DevOps teams on MLOps infrastructure including model monitoring, load testing, caching optimization, and automated CI/CD pipelines for model deployments
- Contribute to privacy-preserving ML approaches and on-device model implementations that align with Brave’s privacy-first mission
Required Qualifications
- 2 to 5 years of experience optimizing and deploying ML models in production environments
- Strong software engineering background with production experience
- Extensive experience with PyTorch or other modern ML frameworks
- Experience training custom models from scratch
- Experience with model optimization and inference frameworks (e.g., vLLM, ONNX Runtime, Nvidia Triton)
- Familiarity with MLOps practices & Kubernetes and ability to collaborate with DevOps teams on model monitoring, load testing, and CI/CD pipelines
- Experience shipping ML-powered features or systems (consumer applications preferred)
Preferred Qualifications
- Master’s degree in Computer Science, Machine Learning, or related field
- Familiarity with LLM serving frameworks (vLLM, TGI, Ray Serve) and GPU optimization
- Experience with embeddings, vector databases, semantic search implementations, model training workflows, and data pipeline development
- Experience integrating LLMs with tool calling/MCP
- Knowledge of privacy-preserving ML techniques and on-device model deployment
- Previous work on cost optimization and performance tuning of ML systems at scale
What We’re Looking For
- Deep curiosity about emerging AI models and their practical applications
- Strong problem-solving skills with ability to work in ambiguous environments
- Excellence in cross-functional collaboration and technical communication
- Drive to make AI technology more accessible through the browser
- Pragmatic approach to balancing innovation with shipping products
What We Offer
- Opportunity to shape the future of AI-powered browsing experiences
- Work with cutting-edge technology and state-of-the-art ML tools
- Competitive compensation with room for growth
- Great international exposure and team atmosphere
- Flexible work location with preference for London office
While we prefer candidates who can work from our London office, we’re open to remote candidates in compatible time zones. We offer flexible working arrangements to support a healthy work-life balance.
Compensation
£100,000 to £125,000 (USD$125,000 to USD$155,000) – Depends on Location, Market Rate and Experience.
