AI Research Engineer Intern

Transformers, Foundation Models and Scientific AI

About SPOC Biosciences
SPOC Biosciences is developing an on-chip experimental platform that generates high-resolution biological data  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.

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

• 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

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

• 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.

• 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.