Staff ML Engineer: LLM Fine-Tuning for RTL/Verilog

Job Title

Staff Machine Learning Engineer — LLM Fine-Tuning for RTL/Verilog

Role Summary

Lead fine-tuning and deployment of large language models for code-focused workflows, including RTL/Verilog, in secure production environments. Collaborate with engineering, security, and infrastructure teams to design ML pipelines, ensure inference performance, and deliver production-ready solutions.

Experience Level

Senior — requires over 10 years of engineering experience and proven leadership and mentorship on technical teams.

Responsibilities

Key responsibilities include:

• Lead fine-tuning of LLMs for code and RTL/Verilog tasks; oversee dataset preparation and model selection.
• Design and implement end-to-end ML training and inference pipelines on AWS with reproducible workflows.
• Deploy models to production in secure environments, ensuring reliability, monitoring, and scalability.
• Optimize inference performance (quantization, efficient serving, accelerator utilization).
• Mentor engineers, conduct design and code reviews, and promote MLOps best practices.
• Collaborate with cross-functional teams to integrate ML features into developer and verification workflows.
• Define evaluation metrics, testing frameworks, and validation processes for code-focused model outputs.

Requirements

Must-have technical skills and experience:

• 10+ years software or ML engineering experience, including shipping production systems.
• Hands-on experience with PyTorch and large-model fine-tuning.
• Practical experience deploying ML workloads on AWS (EC2, S3, EKS/SageMaker or equivalent).
• Familiarity with code and RTL workflows and demonstrated exposure to Verilog or hardware-description workflows.
• Experience with inference optimization and model serving (quantization, ONNX/TensorRT/Triton or similar).
• Proven ability to mentor and lead engineering teams.
• Experience operating in secure or compliance-sensitive environments.

Nice-to-have:

• Experience with containerization and orchestration (Docker, Kubernetes).
• Background in compiler toolchains, static analysis, or program synthesis for code.
• Familiarity with MLOps tooling, CI/CD for ML, and monitoring/observability stacks.

Education Requirements

Not specified.

About the Company

Company: Highbrow Technology

Headquarters: San Jose, CA, United States

Highbrow Technology is a California-based technology company specializing in machine learning and engineering solutions for code workflows, secure environments, and production ML deployments.