# Machine Learning Engineer

**Company**: Firecrawl
**Location**: San Francisco, CA
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: Full time
**Salary**: $210,000–$240,000/year
**Category**: Engineering
**Industry**: Technology

**Apply**: https://jobs.ashbyhq.com/firecrawl/72f9dc1d-65db-48c9-b3d9-c6ccdb997006?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_ac8731bd-c2e

## Description

## Compensation

You will receive a salary ranging from $210,000 to $240,000 per year, along with competitive equity.

## Job Overview

As a Machine Learning Engineer at Firecrawl, you will build and maintain the machine learning models and systems behind Firecrawl's products. This includes training and deploying ranking and relevance models, extending ML capabilities across various products, and developing experimentation frameworks.

## What You'll Do

- Improve ranking and relevance for Firecrawl Search, including feature engineering, model training, and production deployment.

- Develop and fine-tune models for learning-to-rank, query understanding, and LLM-driven retrieval.

- Expand ML capabilities across Firecrawl's products, focusing on extraction quality, content classification, and LLM-driven features.

- Analyze query logs and behavioral data to identify areas for improvement.

- Design and implement data pipelines to convert web-scale crawl and query data into training data and features.

- Collaborate with platform, search engineers, and cloud DevOps to optimize model performance in production.

- Develop and execute testing strategies, including A/B testing frameworks and offline evaluation.

- Partner on product launches, defining success metrics, running experiments, and making data-driven decisions.

## What We're Looking For

- Proven experience shipping ML models into production systems and owning them post-launch.

- Real-world experience with ranking or relevance modeling, including learning-to-rank, recommendations, or search quality.

- Comfort working with large, data-heavy systems, including query logs, pipelines, and datasets.

- Proficiency in writing production-quality code, preferably in Python.

- Strong understanding of measurement and analysis, including designing and interpreting A/B tests.

## Nice to Have

- Experience with MLOps tools like MLflow, experiment tracking, model registries, or feature stores.

- Background in building or standardizing experimentation frameworks.

- Familiarity with embedding models, vector retrieval, or LLM-based relevance evaluation.

- Experience evaluating LLM outputs at scale.

## Benefits & Perks

- Salary range: $210,000 - $240,000 per year

- Competitive equity

- Generous PTO policy

- Parental leave: 12 weeks fully paid

- Wellness stipend: $100/month

- Learning & Development: up to $1,000/year for professional growth

- Team offsites and sabbatical opportunities

- Comprehensive health insurance, life & disability insurance, and 401(k) plan for US-based employees

## Interview Process

The interview process includes:

- Application review

- Intro chat

- Technical chat

- Founder chat

- Paid work trial

## Skills

### Required
- machine learning
- ranking and relevance models
- large-scale data systems
- Python programming
- A/B testing

### Nice to have
- MLOps
- experimentation frameworks
- embedding models
- LLM-based relevance evaluation
- Spark or similar large-scale data processing

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Source: [Apply at jobs.ashbyhq.com](https://jobs.ashbyhq.com/firecrawl/72f9dc1d-65db-48c9-b3d9-c6ccdb997006?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)