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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and customizing large language models and deep learning models, with a strong focus on training pipelines, model architecture, and customer collaboration. Proficient in Python and PyTorch, with a proven ability to manage large-scale model training and present complex results to diverse audiences.
Highest-signal resume keywords
Large Language Model DevelopmentDeep Learning Model CustomizationPython MasteryPyTorch ExpertiseDistributed Training Frameworks Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Large-Scale Model TrainingDense Transformers UnderstandingFine-Tuning Large ModelsData PreprocessingHyperparameter TuningLoss-Curve AnalysisModel Architecture SelectionContinuous Pre-TrainingSupervised Fine-TuningReinforcement Learning from Human Feedback
Soft Skills
Strong Collaborative SkillsEffective Communication SkillsAutonomous Work AbilityProject ManagementAdaptability
Tools & Technologies
Cerebras ClustersDistributed Data Processing PipelinesAI/ML ToolsTraining Pipelines
Industry Keywords
Machine LearningAI ApproachesModel Convergence AnalysisGradient StabilityCustomer Relationship Management
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- Develop and customize large language models and large-scale deep learning models for specific customer problems
- Collaborate with customer stakeholders to identify AI approaches for business problems
- Scope engagements through feasibility analysis, data quality, availability and readiness assessments, and model architecture selection
- Define project milestones, success metrics, and evaluation benchmarks
- Architect and execute end-to-end training recipes for custom models
- Design adaptation strategies including continuous pre-training, supervised fine-tuning, RLHF, and 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 multi-billion-parameter training workloads across Cerebras clusters
- Build and optimize agentic-system components for tool use, long-context reasoning, and multi-step planning
- Serve as an AI/ML subject matter expert during technical deep-dives
- Build and maintain customer relationships as their go-to AI/ML expert
- Represent customer needs to internal R&D and engineering teams
- Partner with ML and product teams on model architectures, training recipes, and internal case studies
- Convert successful customer projects into internal playbooks
Requirements
What you’ll need- Master’s or PhD in Computer Science, Machine Learning, or related fields
- Expert-level understanding of dense transformers, MoEs, multimodal and sequence models, scaling laws and training dynamics
- Proven track record of training and/or fine-tuning large models with 1B+ parameters
- Direct experience with challenges of large-scale model training
- Mastery of Python and PyTorch
- Experience with distributed training frameworks
- Experience with large-scale distributed data processing pipelines and tools
- Strong collaborative and communication skills
- Ability to work autonomously and within a team in a dynamic environment
- Ability to manage multiple projects and pivot as customer needs evolve
- Ability to present complex technical results to audiences ranging from C-level executives to research scientists
Benefits
Comp & perks- Build a breakthrough AI platform beyond the constraints of the GPU
- Publish and open source cutting-edge AI research
- Work on one of the fastest AI supercomputers in the world
- Job stability with startup vitality
- Simple, non-corporate work culture that respects individual beliefs
- Continuous learning, growth and support