2 hours ago
San Francisco, CA, USAMid Level
Base Salary
$210k - $240k/yr
Responsibilities
- Improve Firecrawl Search ranking and relevance through feature engineering, model training, and production deployment.
- Build and tune learning-to-rank, query-understanding, and LLM-driven retrieval models.
- Extend machine learning across extraction quality, content classification, and evaluation of LLM-driven features.
- Mine query logs and behavioral data at scale to identify product successes and failures.
- Build data pipelines that convert web-scale crawl and query data into training data and features.
- Deploy and optimize models in production with platform, search engineering, and cloud DevOps teams.
- Design A/B testing frameworks and offline evaluation strategies.
- Define launch success metrics, run experiments, make ship/no-ship decisions, and report post-launch results.
Requirements
- At least 3 years of experience building ML or data-heavy systems in production.
- Experience shipping, deploying, monitoring, and retraining machine learning models after launch.
- Hands-on ranking or relevance-modeling experience, such as learning-to-rank, recommendations, or search quality.
- Experience working with large query logs, data pipelines, and datasets that do not fit in memory.
- Production-quality coding ability in Python and the ability to work within a backend codebase.
- Experience designing and analyzing A/B tests and evaluating whether measured improvements are meaningful.
- Ability to communicate shipped work, measured outcomes, and next steps clearly.
- Preferred: MLOps experience with experiment tracking, model registries, or feature stores.
- Preferred: Kubernetes, experimentation-framework development, embedding models, vector retrieval, LLM-based relevance evaluation, large-scale LLM output evaluation, or Spark experience.
Benefits
- Hybrid role based in San Francisco with on-site work required.
- Full-time employment with medical, dental, and vision coverage for US-based employees.
- 15 days of mandatory PTO, with additional time encouraged after 24 days, excluding holidays.
- 12 weeks of fully paid parental leave for all parents.
- $100 monthly wellness stipend.
- Up to $1,000 per year for professional learning and development.
- Team offsites and a three-month paid sabbatical after four years.
- Employer-paid short-term disability, long-term disability, and life insurance, plus optional supplemental insurance.
- 401(k), FSA, commuter benefits, pet insurance, and Doctegrity telehealth for eligible US-based employees.
- San Francisco employees receive office snacks, drinks, team lunches, and a loaner electric bike.
- Paid 1–2 week work trial at a contractor rate, remote-friendly and flexible around current commitments.
Tech Stack
Categories
Data EngineeringML Engineering