FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and optimizing scalable AI training solutions using NVIDIA hardware and software, with a strong focus on end-to-end training workflows and large-scale AI systems. Proven ability to lead technical engagements and communicate complex technical concepts effectively to diverse audiences.
Highest-signal resume keywords
Large-Scale AI SystemsDeep Learning Workloads OptimizationPython ProgrammingNVIDIA NeMo FrameworkMulti-GPU Training
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Solutions ArchitectureMachine Learning EngineeringReinforcement LearningData PreparationTraining ConfigurationEvaluation and TroubleshootingDistributed TrainingCheckpointingMemory OptimizationProfiling and Optimizing Workloads
Soft Skills
Problem SolvingTechnical CommunicationLeadershipCollaboration
Tools & Technologies
AWSGCPAzurePyTorchMegatron CoreMegatron BridgePyTorch FSDPDeepSpeedContainersWorkload Orchestration
Industry Keywords
AI Training SolutionsFoundation Model Pre-TrainingPost-TrainingGPU ComputeCloud Computing
Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformNode.jsPythonPyTorch
About the role
Key responsibilities & impact- Work with Cloud Service Providers to design, build, and demonstrate scalable AI training solutions using NVIDIA hardware and software
- Lead technical engagements across foundation model pre-training and post-training, helping customers move from architecture and proofs of concept to production adoption
- Develop and optimize end-to-end training workflows spanning data preparation, distributed training, checkpointing, and evaluation, including reinforcement learning pipelines
- Build customer-specific proofs of concept addressing critical technical and business needs
- Help customers diagnose and resolve bottlenecks across GPU compute, memory, communication, networking, storage, and workload orchestration
- Partner with Sales Account Managers and Developer Relations Managers to identify opportunities and drive adoption of NVIDIA platforms for AI training
- Prepare and deliver technical presentations, workshops, and architecture reviews for researchers, engineers, technical leaders, and executives
- Establish close technical relationships with customers, lead roadmap and architecture discussions, and collaborate with NVIDIA engineering and product teams to resolve issues and inform future capabilities
Requirements
What you’ll need- BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Statistics, Physics, or a related technical field, or equivalent experience
- 5+ years of relevant professional experience in solutions architecture, software engineering, ML engineering, research engineering, or related technical roles
- 3+ years of hands-on experience with large-scale AI systems or distributed training
- Strong programming and debugging skills in Python
- Hands-on experience developing and optimizing deep learning workloads using PyTorch
- Practical experience with foundation model pre-training or post-training
- Strong understanding of the broader model development lifecycle
- Demonstrated ownership of an end-to-end training workflow, including data preparation, training configuration, execution, evaluation, and troubleshooting
- Experience with multi-GPU and multi-node training, including distributed parallelism, mixed precision, memory optimization, and checkpointing
- Track record of deploying GPU workloads in cloud computing environments such as AWS, GCP, or Azure
- Knowledge of containers and workload orchestration
- Ability to lead technical engagements, solve complex problems, and communicate technical ideas and tradeoffs clearly to technical and non-technical audiences
- Hands-on experience spanning both pre-training and post-training of large language or multimodal models, including supervised fine-tuning and reinforcement learning
- Experience with reinforcement learning from human feedback or verifiable rewards, including reward design, rollout generation, policy optimization, and training stability
- Experience with NVIDIA NeMo Framework, Megatron Core, Megatron Bridge, PyTorch FSDP, DeepSpeed, or comparable distributed training technologies
- System-level understanding of GPU platforms
- Experience profiling and optimizing training workloads
Benefits
Comp & perks- Equity
- Benefits
- Occasional travel for on-site customer engagements and industry events