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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI solutions development, particularly in post-training strategies, model optimization, and deployment. Proficient in leading technical engagements and collaborating with cross-functional teams to drive successful AI implementations.
Highest-signal resume keywords
Post-Training StrategiesLoRA/PEFT ExpertiseDeep Learning FundamentalsPyTorch ProficiencySynthetic Data Pipelines
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningReinforcement LearningModel ArchitectureHyperparameter TuningInference OptimizationDataset CurationEvaluation SetsMulti-GPU SystemsLinuxPython
Soft Skills
Organizational SkillsComplex Decision-MakingCommunication SkillsProblem-Solving SkillsIntellectual Curiosity
Tools & Technologies
Hugging Face TransformersNemotronCosmosNVIDIA Software Stack
Industry Keywords
Applied AISolutions ArchitectureFDELarge-Scale Distributed TrainingAgentic Systems
Tech Stack
Tools & technologiesLinuxNode.jsPythonPyTorch
About the role
Key responsibilities & impact- Drive AI solutions from discovery and scoping through post-training, harness optimization, and deployment
- Develop post-training and harness strategies with appropriate methods, data, evaluation, and success criteria
- Design evaluations and build synthetic data pipelines
- Perform post-training with LoRA and other parameter-efficient methods, supervised fine-tuning, and synchronous and asynchronous reinforcement learning
- Make architecture and hyperparameter choices to improve results
- Tune prompting, tool use, orchestration, and context management for domain-specific performance
- Optimize inference for cost-effective production operation
- Lead technical engagements by scoping work, shaping statements of work, and driving delivery with partner engineering teams and joint customers
- Build reusable recipes, reference pipelines, and playbooks for partners
- Connect NVIDIA model research to partner and customer use cases
- Work with product, research, and engineering teams to bring field insights into Nemotron, Cosmos, and NVIDIA's software stack
Requirements
What you’ll need- Bachelor's, Master's, or PhD in Computer Science, Engineering, Machine Learning, or a related field, or equivalent experience
- 8+ years in ML or software engineering, applied AI, FDE, or solutions architecture
- 3+ years of hands-on LLM post-training and taking models to production
- Solid fundamentals in deep learning and machine learning
- Deep expertise in LoRA/PEFT, SFT, and reinforcement learning, including synchronous and asynchronous approaches
- Strong grasp of model architectures and hyperparameters
- Understanding of large-scale distributed training and inference performance optimization
- Ability to configure and tune workloads for memory efficiency, throughput, latency, and scaling across multi-GPU and multi-node systems
- Experience with synthetic data pipelines, dataset curation and filtering, and evaluation sets
- Experience with harness engineering for LLM and agentic systems
- Proficiency in PyTorch, Hugging Face Transformers, Python, and Linux
- Proven ability to scope engagements, shape statements of work, and lead delivery with partners and customers
- Excellent organizational skills and ability to drive complex decisions across teams and companies
- Ability to communicate complex technical ideas to engineers and C-level executives
- Strong problem-solving skills, intellectual curiosity, and commitment to partner and customer success
Benefits
Comp & perks- Equity
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