---
title: ML Engineer at Freed in San Francisco, CA
description: Freed is hiring for the ML Engineer role in San Francisco, CA. Pays $220K–$300K
  per year. See the full description and apply.
type: job
url: https://www.foundrole.com/jobs/ml-engineer-at-freed-01a18eea-ba1a-707d-b534-cf1c9eb31ac3
date: 2026-11-15T01:26:44Z
og_description: Join Freed as ML Engineer in San Francisco, CA. Pays $220K–$300K per year. Full-time,
  hybrid role. See the full description and apply.
og_image: https://www.foundrole.com/og/7m0ifk.png
breadcrumbs:
  - label: Home
    url: https://www.foundrole.com/
  - label: Search
    url: https://www.foundrole.com/jobs
---

| | |
|---|---|
| **Company** | Freed |
| **Location** | San Francisco, CA (Hybrid) |
| **Salary** | $220K/yr - $300K/yr |
| **Type** | Full Time |
| **Posted** | Oct 30, 2026 |

## Is this job real?

**Ghost job check:** A- · Low risk. Probably a real opening.

## Visa sponsorship

- **H-1B:** Freed sponsors H-1B visas.
- **E-Verify:** No E-Verify enrollment on record for Freed.

## Pay for similar jobs

Most similar jobs pay $205,000 – $251,000 a year (median $230,000).

## Description

# **Machine Learning Engineer**

**Location:** San Francisco, CA  
**Team:** Engineering  
**Employment Type:** Full-time

## **About Freed**

Freed is building AI-powered tools that reduce the administrative burden on healthcare professionals, giving clinicians more time to focus on patient care.

We’re looking for a Machine Learning Engineer to help develop, improve, and scale the AI systems powering our products.

## **About the Role**

As a Machine Learning Engineer at Freed, you’ll work at the intersection of applied machine learning, software engineering, and product development.

You’ll help take ML solutions from experimentation into production, working closely with engineers and cross-functional partners to build reliable, high-quality AI experiences for clinicians.

This role is ideal for someone who enjoys solving complex technical problems, moving quickly, and seeing their work make a tangible impact.

## **What You’ll Do**

- Develop, evaluate, and improve machine learning models and AI-powered product capabilities.

- Build and maintain production ML pipelines, services, and supporting infrastructure.

- Design experiments and evaluation frameworks to measure model quality, reliability, and performance.

- Collaborate with product and engineering teams to translate user needs into practical technical solutions.

- Improve the scalability, efficiency, and reliability of ML systems in production.

- Investigate model failures, identify opportunities for improvement, and iterate using real-world feedback.

- Contribute to technical decisions and engineering best practices across the ML lifecycle.

## **What We’re Looking For**

- Strong software engineering fundamentals and experience building production systems.

- Hands-on experience developing and deploying machine learning solutions.

- Proficiency in Python and familiarity with modern ML frameworks and tooling.

- Experience evaluating model performance and improving systems through experimentation.

- Understanding of ML infrastructure, deployment, monitoring, and production reliability.

- Ability to work independently, communicate clearly, and collaborate across disciplines.

- Comfort operating in a fast-moving environment with evolving technical and product requirements.

## **Nice to Have**

- Experience with large language models, generative AI, or speech-related ML systems.

- Experience with model evaluation, fine-tuning, or inference optimization.

- Familiarity with healthcare technology or other high-reliability product environments.

- Experience building ML products in an early-stage startup.

## **Why Freed?**

At Freed, you’ll work on meaningful technical challenges with a direct connection to the people using what you build.

You’ll have the opportunity to take ownership, influence technical direction, and help shape AI products that make clinicians’ lives easier.
## Skills

- Software Deployment
- Python
- Machine Learning Model Fine-Tuning
- Model Evaluation (Machine Learning)
- Product Requirements Document (Prd)
- Inference Optimization
- Scalability
- Generative AI
- Large Language Models (Llm)
- Ability to Work Independently
- Machine Learning
- Generative AI Foundations

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