ML Engineer | Hybrid – San Francisco, CA

**DUE TO FEDERAL REGULATIONS, CANDIDATES MUST BE US CITIZENS**

Berkley Hunt has partnered with a fast-growing, Series B technology company that is redefining manufacturing engineering through cutting-edge AI solutions. As the demand for intelligent automation in manufacturing accelerates, they are tackling the complex challenges of deploying AI in real-world industrial systems - ranging from large language models for classification to optimizing cloud infrastructure for production-scale deployment. To drive this mission forward, they are seeking a product-focused AI Engineer to design, build, and deploy high-impact AI features that fundamentally transform the way engineers work.

About the Role:

As an AI Engineer, you will work closely with cross-functional teams to shape the AI features that power the platform.
You will have ownership over developing and deploying large language models (LLMs), collaborating with engineers to integrate AI capabilities into the platform’s infrastructure, and optimizing performance at scale.
This role requires a combination of strong expertise in NLP, MLOps, and a solid understanding of building scalable, production-ready machine learning systems in a cloud environment.

Responsibilities:

Develop and fine-tune large language models (LLMs) for classifying aerospace engineering text, categorizing, and linking requirements across the platform
Implement end-to-end ML features from product requirements to production deployment, including backend infrastructure
Collaborate cross-functionally with app engineers, infrastructure, and security engineers to integrate AI capabilities seamlessly into our platform
Design and maintain reproducible training pipelines ensuring model consistency across different environments
Optimize model training processes, inference performance, and associated cloud infrastructure costs
Establish MLOps best practices for versioning, monitoring, and maintaining AI systems in production
Mentor team members on AI concepts and best practices to build organizational knowledge

Expectations:

5+ years of professional experience developing AI/ML solutions in production environments
Strong expertise in NLP, particularly with transformer-based models (BERT, GPT, etc.)
Experience taking ML features from concept to production without extensive specialist support
Full-stack development capabilities to build complete AI features
Cross-functional collaboration skills and the ability to communicate complex AI concepts to non-specialists
Independent problem-solving abilities and resourcefulness when tackling novel AI challenges
Product thinking – ability to translate business requirements into pragmatic AI solutions

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