AI Engineer

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