AI Researcher Intern – Winter / Spring 2027 (PhD)

Akoncagua AI; Lakeland (FL); Presencial; USD 34.000 - 55.000; A tiempo parcial

Descripción de la vacante

Akoncagua AI is seeking AI Researcher Interns for Winter/Spring 2027 (PhD). The role blends ML research, systems engineering, and product development to build secure AI agents and automated workflows.

You will apply deep-learning theory to train, evaluate, and optimize AI systems at scale, across CV, NLP, and multimodal tasks. Ideal candidates are pursuing a PhD, with independent problem-solving skills and hands-on PyTorch experience.

Formación

• Currently pursuing a PhD in computer science, artificial intelligence, electrical engineering, applied mathematics, or a closely related field.
• Completion of at least two years of PhD study before the internship begins.
• Strong command of machine learning fundamentals, including probability, statistics, linear algebra, optimization, and experimental design.
• Broad and rigorous understanding of deep learning across computer vision, natural language processing, and generative AI, including diffusion models and large language models.
• Strong understanding of transformers, attention mechanisms, language modeling, tokenization, embeddings, and modern model architectures.
• Hands‑on experience training or fine‑tuning models with PyTorch or a comparable framework.
• Experience independently solving open‑ended research or engineering problems.

Responsabilidades

• Brainstorm, develop, and apply machine learning solutions to complex problems across a range of engineering industries.
• Train, fine-tune, and evaluate large language models and vision-language models.
• Research long-context learning, multimodal reasoning, post-training methods, and model reliability.
• Analyze GPU memory usage and training throughput to improve the efficiency of large-scale training.
• Develop data pipelines, training curricula, evaluation suites, and reproducible research workflows.

Conocimientos

ML fundamentals

Deep learning

Transformers

PyTorch

Research problem solving

NLP/CV/GenAI understanding

Model architectures

Educación

PhD in computer science, AI, EE, applied mathematics, or related field

Herramientas

PyTorch

Hugging Face Transformers

Descripción del empleo

AI Researcher Intern – Winter / Spring 2027 (PhD)

Akoncagua AI builds domain-specialized AI systems for technically demanding industries. Our work includes LLMs, multimodal AI, agentic automation, RAG, evaluation, and production AI infrastructure. We are seeking AI Researcher Interns to help build secure, reliable AI agents and automated workflows.

What you'll work on

This role sits at the intersection of machine learning research, systems engineering, and product development. You will apply strong theoretical foundations in deep learning to build, train, adapt, evaluate, and optimize modern AI systems at scale.

• Brainstorm, develop, and apply machine learning solutions to complex problems across a range of engineering industries.
• Train, fine-tune, and evaluate large language models and vision-language models.
• Research long-context learning, multimodal reasoning, post-training methods, and model reliability.
• Analyze GPU memory usage and training throughput to improve the efficiency of large-scale training.
• Develop data pipelines, training curricula, evaluation suites, and reproducible research workflows.

Required qualifications

• Currently pursuing a PhD in computer science, artificial intelligence, electrical engineering, applied mathematics, or a closely related field.
• Completion of at least two years of PhD study before the internship begins.
• Strong command of machine learning fundamentals, including probability, statistics, linear algebra, optimization, and experimental design.
• Broad and rigorous understanding of deep learning across computer vision, natural language processing, and generative AI, including diffusion models and large language models.
• Strong understanding of transformers, attention mechanisms, language modeling, tokenization, embeddings, and modern model architectures.
• Hands‑on experience training or fine‑tuning models with PyTorch or a comparable framework.
• Experience independently solving open‑ended research or engineering problems.

Preferred qualifications

• Publications at leading conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, or MLSys, or evidence of comparable applied research contributions.
• Research experience with LLMs, vision‑language models, long‑context models, or other large neural networks.
• Experience fine‑tuning and optimizing LLMs with tools such as Hugging Face Transformers, TRL, PEFT, or Unsloth.
• Experience with distributed training frameworks such as PyTorch FSDP, DeepSpeed, or Megatron.
• Strong understanding of multi‑GPU Linux server environments, with hands‑on experience configuring, managing, and effectively utilizing GPU resources.
• Understanding of distributed training and parallelism techniques, including data, tensor, pipeline, context, and sequence parallelism.
• Experience with post‑training methods such as instruction tuning, preference optimization, reinforcement learning, distillation, or synthetic data generation.

How we work

We care about researchers and engineers who understand and can defend the design decisions in their code. AI coding tools are welcome here, but you should be able to explain why a system works the way it does, what evidence supports it, and where it breaks.

Our work connects model research with the systems required to run it. You will have room to explore new ideas, but you will also be expected to build working implementations, measure them carefully, and communicate their limitations clearly.