# Machine Learning Engineer

**Company:** [Firecrawl](https://hotfix.jobs/companies/firecrawl)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $210k – $240k/yr
**Experience:** 3+ years
**Skills:** Python, Learning-To-Rank, A/B Testing, MLOps, MLflow, Kubernetes, Embedding Models, Vector Retrieval, Llm Evaluation, Feature Stores, Model Registries, Spark, Data Pipelines, Query Understanding, Content Classification
**Posted:** 2026-08-20

> Build and operate production machine-learning systems for search ranking, relevance, extraction quality, and LLM-driven features. The role requires production ML ownership, ranking or relevance expertise, large-scale data experience, Python, and rigorous experimentation skills.

## Job Description

## Responsibilities
- Improve ranking and relevance for Firecrawl Search through feature engineering, model training, and production deployment.
- Build and tune learning-to-rank, query-understanding, recommendation, retrieval, and LLM-driven models.
- Extend machine learning across extraction quality, content classification, and evaluation of LLM-driven features.
- Analyze query logs and behavioral data to identify product strengths and failures.
- Build data pipelines that convert web-scale crawl and query data into training data and features.
- Collaborate with platform, search engineering, and cloud DevOps teams to deploy efficient production models.
- Design A/B testing frameworks and offline evaluation strategies.
- Define launch success metrics, run experiments, make evidence-based ship/no-ship decisions, and report post-launch performance.

## Requirements
- 3+ years building machine-learning or data-heavy systems in production.
- Experience deploying, monitoring, retraining, and owning ML models after launch.
- Ranking or relevance-modeling experience, including learning-to-rank, recommendations, or search quality.
- Experience working with large-scale query logs, pipelines, and datasets.
- Production-quality programming skills, including Python, and ability to work in backend codebases.
- Experience designing and analyzing A/B tests.
- Clear communication of technical results and recommendations.

## Nice to Have
- MLOps experience with MLflow, experiment tracking, model registries, or feature stores.
- Kubernetes experience.
- Experience building or standardizing experimentation frameworks.
- Experience with embedding models, vector retrieval, or LLM-based relevance evaluation.
- Experience evaluating LLM outputs at scale.
- Spark or similar large-scale data-processing experience.

## Compensation & Benefits
- Salary: **$210,000–$240,000 per year**.
- Competitive equity.
- 15 days mandatory PTO, with additional time available by request.
- 12 weeks of fully paid parental leave.
- $100/month wellness stipend.
- Up to $1,000/year for learning and development.
- Team offsites and a three-month paid sabbatical after four years.
- Medical, dental, and vision coverage for US-based full-time employees.
- Employer-paid short-term disability, long-term disability, and life insurance.
- Optional supplemental insurance, telehealth, 401(k), FSAs, commuter benefits, and pet insurance.
- San Francisco HQ perks and an e-bike transportation loaner for SF-based employees.
- Paid work trial at a contractor rate.

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