ML Engineer | Hybrid – San Francisco or Los Angeles, CA
**DUE TO FEDERAL REGULATIONS, CANDIDATES MUST BE US CITIZENS**
Berkley Hunt has partnered with an innovative, VC-backed technology company focused on revolutionizing manufacturing engineering through cutting-edge AI solutions. As the demand for intelligent automation grows in manufacturing, they are addressing the complex challenges of integrating AI into real-world systems, from large language models for classification to optimizing cloud infrastructure for production deployment. To support this mission, they are looking for a product-focused AI Engineer to design, build, and deploy high-impact AI features that transform the way engineers work.
About the Role:
As an ML 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
- Experience with MLOps tools and practices (model versioning, experiment tracking, CI/CD for ML)
- Demonstrated ability to manage and optimize AWS ML infrastructure costs and performance
- Experience with containerization for ML workloads ensuring reproducibility across development and production environments
- Background in implementing retrieval systems, semantic search, or vector databases
- Adaptability and pragmatism – knowing when to use simple solutions versus building complex systems
