# Machine Learning Engineer

**Company:** [Clay](https://hotfix.jobs/companies/clay)
**Location:** New York, NY, San Francisco, CA
**Role:** ML Engineering
**Salary:** $170k – $300k/yr
**Experience:** 5+ years
**Skills:** Machine Learning, LLMs, classical ml, Recommendation Systems, search ranking, personalization, Python, Data Pipelines, feature infrastructure, retrieval, model serving, Snowflake, dbt, Dagster, data lakes
**Posted:** 2026-08-14

> Build and ship production machine-learning systems that learn from customer data and behavior, including recommendations, LLM-powered features, evaluation systems, and ML infrastructure. The role requires 5+ years of ML engineering or ML-heavy software engineering experience and strong production systems expertise.

## Job Description

## Responsibilities
- Build learning loops into the product using user behavior and business data.
- Design and ship recommendation-first experiences from prototype through production.
- Build the ML and data platform, including data lake foundations and serving infrastructure.
- Evaluate tools that can accelerate the product vision.
- Collaborate with data science and data platform teams on a common data language.
- Build evaluation systems and online monitoring to ensure learning features are trustworthy and improve the user experience.
- Partner with product teams across the company to make product surfaces smarter.

## Requirements
- 5+ years of experience in machine learning engineering or ML-heavy software engineering.
- Experience shipping models and ML-powered features to production.
- Strong engineering fundamentals, including writing production-quality code and owning systems.
- Experience with LLMs in production, including prompting, evaluations, guardrails, or fine-tuning, and/or classical ML such as ranking, recommendations, or propensity models.
- Experience building data-intensive systems, including pipelines, feature infrastructure, retrieval, or serving.
- Pragmatic product sense and ability to optimize for user experience and business impact.
- Comfort working with ambiguity and building platforms from the ground up.
- Passion for the AI space and current innovations.

## Nice-to-haves
- Experience building recommendation systems, search ranking, or personalization.
- Experience designing evaluation frameworks for LLM or ML systems.
- Familiarity with Snowflake, dbt, Dagster, and data lake architectures.
- Experience in fast-moving startup environments.

## Compensation and Benefits
- Employees can work with world-class coaches who specialize in creativity, management, and more.

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