AI/LLM Engineering Intern

About WhoisXML API

For over a decade, WhoisXML API has been the leader in Domain, IP, DNS, and Cyber Threat Intelligence. We have collected exhaustive information on billions of historic Whois records, DNS records, and hundreds of millions of active websites, covering 99.6% of all IP addresses in use. Our data is parsed into unified formats and provided as domain/IP intelligence for the world’s most advanced threat intelligence and market researchers, enabling them to monitor activity across the entire international internet.

Our customer base includes Fortune 500 companies, threat intelligence/infosec companies, anti-malware/security vendors, cybercrime units, government agencies, brand protection agencies, domain registries/registrars, domain investors/brokers, banks, payment processors, telecom companies, marketing researchers, big data warehouses, web analytics firms, investment funds, web developers, and many more.

About the Role

We are seeking a highly motivated AI / LLM Engineering Intern for a learning-focused and experimental internship. In this role, you will support hands-on research, development, and optimization of large language model (LLM) workflows.

This internship is ideal for a student who wants to go beyond API usage and gain practical experience working with local models, inference optimization, prompt engineering, and modern deployment frameworks. You will explore how LLM systems are evaluated and improved in real engineering environments, with a focus on performance, efficiency, and system-level trade-offs.

You will work closely with senior engineers and researchers on guided experiments, benchmarking tasks, and internal documentation that support the team’s ongoing AI development efforts.

Key Responsibilities

• Work with local and open-source LLMs (e.g., LLaMA, Mistral, Mixtral, Qwen, etc.) in a sandbox or experimental environment
• Experiment with prompt engineering techniques to improve accuracy, consistency, and efficiency
• Learn and apply tokenization strategies and context window management techniques
• Explore input/output design patterns and evaluate their impact on latency and token usage
• Learn and experiment with LLM serving and deployment frameworks such as vLLM, Ollama, LM Studio, or similar tools

1. Evaluate and document trade-offs between:
2. Local vs hosted models
3. Quantized vs full-precision models

• Throughput vs latency
• Assist with benchmarking and performance testing of models under guidance from senior engineers
• Explore retrieval-augmented generation (RAG) concepts and prototype implementations
• Document findings, experiments, and best practices for internal learning and future reference
• Collaborate with engineering teams to support internal prototyping and evaluation efforts for potential product applications

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Required Skills & Experience

• Solid understanding of LLMs and generative AI concepts
• Experience running or experimenting with local models
• Familiarity with:

1. Tokenization
2. Context limits
3. Prompt structure and chaining

• Basic understanding of inference optimization
• Comfortable working in Linux-based environments
• Experience with Python (required)
• Ability to read and understand technical documentation and research papers
• Strong curiosity and willingness to experiment

Preferred Skills

• Preferred: Master’s or PhD in Computer Science or related field
• Hands-on experience with vLLM, Hugging Face Transformers, or similar frameworks
• Familiarity with quantization techniques (GGUF, GPTQ, AWQ, etc.)
• Experience with RAG pipelines, embeddings, or vector databases
• Understanding of GPU vs CPU inference trade-offs
• Exposure to Docker or containerized deployments
• Familiarity with API design (REST / FastAPI)

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• Background or interest in security, data analysis, or large-scale systems

What You’ll Gain

• Real-world experience deploying and optimizing LLM systems
• Exposure to production-level AI decision-making (performance, cost, scale)
• Mentorship from experienced engineers and researchers
• Experience contributing to internal prototypes, experiments, and technical documentation
• Strong resume-building experience in a fast-moving AI environment

Who This Role Is For

This role is not a good fit for someone who has only used ChatGPT through the UI.

 It is for someone who:

• Runs models locally
• Thinks about tokens, latency, and memory
• Enjoys breaking things to understand how they work
• Wants to go deeper than “prompting” into systems-level AI engineering

Location

• Fully remote
• Time Commitment: Up to 20 hours per week

“Whois API, INC is an Equal Opportunity Employer and Prohibits Discrimination and Harassment of Any Kind: Whois API, INC is committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment. All employment decisions at Whois API, INC are based on business needs, job requirements and individual qualifications, without regard to race, color, religion or belief, national, social or ethnic origin, sex (including pregnancy), age, physical, mental or sensory disability, HIV Status, sexual orientation, gender identity and/or expression, marital, civil union or domestic partnership status, past or present military service, family medical history or genetic information, family or parental status, or any other status protected by the laws or regulations in the locations where we operate. Whois API, INC will not tolerate discrimination or harassment based on any of these characteristics.”