Applied AI/ML Scientist

Develops and customizes large language and deep learning models for customer-specific applications, owning training, fine-tuning, evaluation, and agentic-system development. Requires advanced graduate education, hands-on experience with 1B+ parameter models, Python, PyTorch, and distributed training.

About the job

Responsibilities

Customer Use Case Discovery & Project Scoping

Collaborate with customer stakeholders to identify AI approaches for business problems.
Contribute to engagement scoping, including feasibility analysis, data quality and readiness assessments, and model architecture selection.
Define project milestones, success metrics, and evaluation benchmarks.

Custom Models and AI Systems

Architect and execute end-to-end training recipes for customer-specific models.
Design adaptation strategies including continuous pre-training, supervised fine-tuning (SFT), and post-training alignment with RLHF or DPO.
Own training pipelines from data preprocessing and tokenization through hyperparameter tuning and loss-curve analysis.
Analyze model convergence, loss dynamics, and gradient stability on specialized hardware.
Scale training workloads across Cerebras clusters for multi-billion-parameter models.
Build and optimize agentic-system components focused on tool use, long-context reasoning, and multi-step planning.

Technical Customer Leadership

Serve as an AI/ML subject-matter expert during technical deep dives.
Translate customer requirements into precise training recipes.
Build strong customer relationships and communicate technical results to executives and research scientists.

Internal Research and Engineering Collaboration

Represent customer needs to internal R&D and engineering teams.
Partner with ML and product teams on model architectures, training recipes, and case studies.
Create internal playbooks from successful customer projects.

Requirements

Master’s or PhD in Computer Science, Machine Learning, or a related field.
Expert understanding of dense transformers, mixture-of-experts models, multimodal models, sequence models, scaling laws, and training dynamics.
Proven experience training or fine-tuning large models with 1B+ parameters.
Mastery of Python and PyTorch.
Experience with distributed training frameworks and large-scale distributed data-processing pipelines and tools.
Strong interpersonal and communication skills.
Ability to work autonomously and collaboratively in a fast-paced, dynamic environment.
Ability to manage multiple projects and adapt as customer needs evolve.

Benefits

Work on a breakthrough AI platform and one of the fastest AI supercomputers in the world.
Opportunities to publish and open-source AI research.
Startup vitality with job stability.
Non-corporate work culture focused on individual beliefs, learning, growth, and support.

Skills

Python, PyTorch, Deep Learning, Transformers, Mixture-Of-Experts, Multimodal Models, Sequence Models, Scaling Laws, LLMs, Supervised Fine-Tuning, RLHF, Dpo, Distributed Training, Data Processing, Agentic Systems