At Sentient, we’re pioneering the decentralized artificial general intelligence (AGI) frontier, breaking free from the constraints of centralized AI models. Our cutting-edge platform is designed to democratize AI development, empowering communities to collaboratively train and control AI models in a truly open and accessible ecosystem.

In Short

Work on cutting-edge generative AI research in a fast-paced environment
Design new agent architectures to improve end-to-end performance of AI workflows
Design, run, and evaluate experiments to improve LLMs on various benchmarks
Execute data engineering tasks to curate data for LLM pre-training, fine-tuning, RAG
Integrate and evaluate models with multi-modal capabilities for different verticals
Read conference papers on generative AI and knowledge retrieval to understand and evaluate new research in the space
Replicate, evaluate, and integrate theoretical data-curation approaches, fine-tuning algorithms, and agent architectures from research papers into real products
Set up fine-tuning and evaluation pipelines on AWS, GCP, and other compute providers
Manage AI workload compute resources and monitoring, keeping track of experiments and assessing results

Requirements

3+ years hands-on experience in generative AI research and/or engineering, whether in industry or through academic work during the Master’s / PhD degree with corresponding published work
Demonstrated expertise in deep learning and transformer models
Mastery of Python (PyTorch, numpy, agentic frameworks) for building AI workflows, fine-tuning models, and writing evaluations
Strong foundation in data structures, algorithms, and software engineering principles
Familiarity with methods for training LLMs (distillation, supervised fine-tuning, policy optimization)
Excellent problem-solving and analytical skills, with a proactive approach to challenges