ML Engineer

Freed

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

Posted 10/31/2026full-timeSan Francisco • California • United StatesMid-LevelSenior💰 $220,000 - $300,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying machine learning models and production systems, with a strong foundation in Python and modern ML frameworks. Capable of improving model performance and reliability through experimentation and collaboration across product and engineering teams.

Highest-signal resume keywords

Machine Learning Model DevelopmentProduction ML PipelinesPython ProficiencyModel Performance EvaluationML Infrastructure Understanding

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningSoftware Engineering FundamentalsModel EvaluationExperimentationDeploymentMonitoringReliabilityFine-TuningInference OptimizationGenerative AI

Soft Skills

Clear CommunicationCollaborationIndependent WorkAdaptability

Industry Keywords

Healthcare TechnologyHigh-Reliability ProductsEarly-Stage Startup

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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
Nice to have: Experience with model evaluation, fine-tuning, or inference optimization
Nice to have: Familiarity with healthcare technology or other high-reliability product environments
Nice to have: Experience building ML products in an early-stage startup

Benefits

Comp & perks

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