AI Research and Development Engineer (LLM/VLM)

Location: San Jose, CA, US; business travel may be required as needed.

Base annual salary: $162,750–$238,700.

Company

hirify.global is a global technology leader developing semiconductor manufacturing and inspection technologies.

What you will do

• Own end-to-end LLM/VLM workflows, including data curation, preprocessing, model selection, training or fine-tuning, evaluation, inference optimization, deployment, and iterative improvement.
• Design adaptation strategies such as supervised fine-tuning, instruction tuning, parameter-efficient fine-tuning, preference optimization, and continued pretraining.
• Build training and evaluation datasets through cleaning, labeling, synthetic-data generation, quality checks, and contamination controls.
• Establish benchmarks for model quality, task performance, robustness, hallucination, safety, latency, throughput, and cost.
• Develop Agentic AI solutions integrating models with tools, planning, memory, and workflow orchestration for semiconductor inspection and complex engineering applications.
• Collaborate with cross-functional teams and customers to define research opportunities, validate technologies, and transition prototypes into reusable platforms and potential products.

Requirements

• Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related field; equivalent practical experience may be considered.
• Hands-on end-to-end experience training or fine-tuning LLMs and/or VLMs, including data preparation, training execution, evaluation, performance optimization, and deployment or inference.
• Practical experience with Python, PyTorch, modern deep-learning frameworks, and model-development tooling.
• Strong understanding of transformer-based models, tokenization, optimization, training dynamics, evaluation methods, and common model failure modes.
• Experience with distributed training or multi-GPU environments and diagnosing instability, overfitting, data-quality problems, and performance regressions.
• Ability to design experiments, interpret results, and make evidence-based trade-offs across architecture, training data, quality, compute, latency, and cost.
• Strong programming skills for independently designing, implementing, testing, and evaluating AI algorithms and prototypes.
• Excellent analytical thinking, independent problem-solving, technical reasoning, teamwork, adaptability, communication, and presentation skills.
• Prompt engineering, RAG implementation, or hosted-model API integration alone does not satisfy the primary model-training requirement.

Nice to have

• Experience developing Agentic AI systems with tool-using agents, planning, memory, multi-agent systems, agent orchestration, and intelligent workflows.
• Experience with LoRA, QLoRA, supervised fine-tuning, preference optimization, reinforcement learning, or related LLM/VLM adaptation and alignment methods.
• Experience with LLM/VLM data pipelines, synthetic-data generation, experiment tracking, model registries, distributed training, optimized inference, quantization, or model serving.
• Experience evaluating LLM/VLM systems for factuality, robustness, safety, latency, throughput, and cost in real application workflows.
• Knowledge of semiconductor manufacturing, voltage-contrast inspection, scanning electron microscopy, e-beam inspection, or defect analysis. Prior SEM experience is not required.

Culture & Benefits

• Work on next-generation LLM/VLM technologies and Agentic AI solutions for advanced engineering applications.
• Join a dynamic, collaborative, startup-like team within a global technology leader.
• Receive opportunities for multidisciplinary technical growth and influence on future products.
• Benefits include medical, dental, vision, and basic life insurance, plus participation in the company’s 401(k) plan.
• Receive eight hours of vacation leave per month and 13 paid holidays annually.

Hiring process

• Candidate evaluation emphasizes demonstrated end-to-end LLM/VLM training and tuning accomplishments, technical depth, and research capabilities rather than years of professional experience.
• Individual pay is determined through interviews and assessment of job-related skills, education, experience, certifications, abilities, and internal pay alignment.