AI Research Engineer Intern

AZBio Board; Scottsdale (AZ); On-site; USD 28,000 - 41,000; Full time

Benefits offered by this job

Paid internship

Job summary

SPOC Biosciences in Scottsdale, AZ is offering an AI Research Engineer Intern role focused on building transformer-based and multimodal foundation models trained on large biological datasets. You will work with our research team to advance scalable AI systems for scientific data and potentially contribute to publications.

The ideal candidate is a student (undergrad, master’s or PhD) with deep ML fundamentals in transformers, PyTorch/JAX, and Python, and a strong inclination toward

Qualifications

• Strong foundations in ML, transformers and deep learning systems.
• Experience with PyTorch or JAX for research.
• Comfort with Python and software engineering practices.

Responsibilities

• Develop transformer-based and multimodal foundation models.
• Design representation-learning and self-supervised methods.
• Integrate sequence data with experimental measurements and metadata.
• Adapt language-model architectures to scientific datasets.
• Explore model interpretability and active learning.
• Collaborate with researchers on datasets and AI-enabled discovery systems.

Skills

Transformers

Attention mechanisms

Large language models

PyTorch

JAX

Python programming

Research instincts

Software engineering practices

Education

Undergraduate/Master/PhD candidate

Tools

Git

Job description

Transformers, Foundation Models and Scientific AI

About SPOC Biosciences
SPOC Biosciences is developing an on-chip experimental platform that generates high-resolution biological data at scale. Our goal is to combine large-scale experimentation with advanced AI models to improve how biological molecules are designed, evaluated and optimized.

We are looking for an AI Research Engineer Intern with strong foundations in machine learning, transformers and modern deep learning systems. Prior experience in biology, protein design or drug discovery is not required. We are primarily looking for candidates with deep technical ability, strong research instincts and an interest in building foundational AI models for complex scientific data.

What You Will Work On

The intern will contribute to development of transformer-based and multimodal foundation models trained on large, structured experimental datasets.

Potential projects include:

• Designing and training transformer architectures for sequence, numerical and multimodal data
• Developing representation-learning and self-supervised learning methods
• Building models that integrate sequence information with experimental measurements and metadata
• Adapting language-model architectures to scientific and biological datasets
• Developing embedding, ranking, prediction and generative modeling approaches
• Fine-tuning and evaluating open-source foundation models
• Improving model training efficiency, inference performance and scalability
• Building data pipelines, training workflows and evaluation frameworks
• Exploring model interpretability, uncertainty estimation and active learning
• Contributing to research that may lead to publications, patents and production AI systems

Ideal Background

We welcome applications from undergraduate, master’s and PhD students in computer science, artificial intelligence, electrical engineering, applied mathematics, statistics or related fields.

• Transformers, attention mechanisms and large language models
• Deep learning using PyTorch, JAX or similar frameworks
• Representation learning, self-supervised learning or contrastive learning
• Generative models, diffusion models, autoregressive models or graph neural networks
• Distributed training and GPU-based model development
• Training and evaluating models on large or complex datasets
• Strong Python programming and software engineering practices
• Mathematical foundations of machine learning, optimization and probability

What We Value

• Strong understanding of core AI concepts rather than experience applying existing APIs
• Ability to implement and modify model architectures from research papers
• Curiosity about how foundational models can be extended beyond natural language
• Comfort working on open-ended research and engineering problems
• Ability to move between mathematical reasoning, experimentation and production-quality code
• Independent thinking and willingness to challenge existing approaches
• Experience in computational biology, protein design or drug discovery is welcome but not required. We expect to provide the necessary scientific context.

Internship Structure

• Type: Paid internship
• Duration: Approximately 3-6 months, with potential for extension
• Full Time Position: Potential for conversion to a full time position during or at the end of the internship
• Commitment: Full-time preferred; part-time arrangements may be considered during the academic year
• Location: Scottsdale, Arizona. Remote arrangements may be considered for exceptional candidates.
• Start date: Flexible
• Interns will work directly with SPOC’s technical and scientific leadership and collaborate with researchers developing experimental datasets and AI-enabled discovery systems.