Machine Learning Engineer

About this role

Build and ship machine learning models powering Firecrawl's search, ranking, and data extraction products. You'll own the full lifecycle—from feature engineering and model training through production deployment and A/B testing—working on learning-to-rank, LLM-driven retrieval, and experimentation frameworks across a fast-growing AI infrastructure platform.

What you'll do

• Train and deploy ranking and relevance models for Firecrawl Search, from feature engineering to production
• Build learning-to-rank, query understanding, and LLM-driven retrieval models
• Design and implement A/B testing frameworks and offline evaluation strategies for product launches
• Extract insights from query logs and behavioral data at scale to identify product gaps and opportunities
• Develop data pipelines converting web-scale crawl and query data into training features
• Collaborate with platform and DevOps teams to optimize model performance and cost in production

What they're looking for

• Machine learning model training and deployment (3+ years production experience)
• Learning-to-rank, recommendation systems, or search quality modeling
• Python and production-quality backend code
• Large-scale data systems and pipelines
• A/B testing design and statistical analysis
• MLOps tools (MLflow, feature stores, model registries)
• Embedding models and vector retrieval
• SQL and large-scale data processing (Spark preferred)

Benefits

• Competitive equity package
• Hybrid work arrangement (San Francisco, CA with on-site required)
• Small team with direct product and company ownership
• Frontier AI infrastructure role supporting superintelligence development
• Fast-moving environment with high autonomy and low process overhead