Offer summary

Qualifications:

2 to 5 years of experience deploying ML models in production., Strong software engineering skills with experience in ML frameworks like PyTorch., Experience training custom ML models from scratch., Familiarity with MLOps practices, Kubernetes, and model optimization frameworks..

Key responsibilities:

Evaluate and deploy language models for browser AI features.

Design and optimize ML inference pipelines for performance and cost-efficiency.

Collaborate with engineering and product teams to integrate AI capabilities into the browser.

Develop privacy-preserving ML techniques and on-device models.

Job description

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.

Required profile