Principal AI Researcher
Job Summary
The Principal AI Researcher at Agentforce Operations will design, implement, and train novel deep learning models on large-scale GPU clusters. This role bridges the gap between cutting-edge research and customer value by developing business intelligence solutions. The position requires strong expertise in transformer architectures, production-grade Python, and distributed systems.
Responsibilities
- Design and train deep learning models on GPU clusters
- Prototype new architectures for enterprise data
- Partner with engineering and product teams to ship production features
- Stay current with AI research and advise on AI strategy
Required Skills
- PhD or Master's in CS/Math/Quantitative field
- Deep understanding of Transformer architectures
- Production-grade Python and ML frameworks (PyTorch/JAX/TensorFlow)
- Experience with distributed GPU training
- Track record of peer-reviewed research
Job Details
What You'll Actually Be Doing
- Design, implement, and train novel deep learning models on large-scale GPU clusters.
- Prototype new architectures and algorithms for enterprise data, such as tabular, relational, and graph-structured data.
- Stay current with AI research, apply relevant breakthroughs, and advise on internal AI strategy - wearing multiple hats as part of a startup-style team that values experimental rigor and shipping well-engineered systems.
- Partner with engineering, product, and design teams to turn research into functional, production-ready features that create immediate, tangible customer value.
You're Our Person If...
- You have a Master's or PhD in Computer Science, Mathematics, or a highly quantitative field with an AI/ML research focus.
- You possess experience developing, deploying, and evaluating machine learning models in production or top-tier research environments and have a deep understanding of transformer architectures and advanced retrieval mechanisms.
- You design novel, non-standard AI solutions by introducing core architectural changes or applying foundational mathematical disciplines such as stochastic modeling, graph theory, or optimization algorithms, and have a track record of peer-reviewed publications demonstrating this work.
- You have production-grade Python skills, including experience with automatic differentiation frameworks like PyTorch, JAX, or TensorFlow, and experience training large-scale models across distributed GPU clusters using frameworks like DeepSpeed or TorchTitan.
Even Better If...
- You have experience building production-grade ML pipelines using tools like Kubeflow, Airflow, or MLflow for tracking, versioning, and automated retraining.
- You've built robust, distributed data pipelines for cleaning and analyzing massive datasets using tools like PySpark.