W
AI Solutions Engineer (Remote — Worldwide)
PyTorchLLMOpsPythonKubernetesDistributed TrainingMachine Learning InfrastructureJAXWeights & Biases SDK
About the role
Weights & Biases is the developer-first MLOps platform used by the world's most advanced AI research labs and enterprises. As an AI Solutions Engineer, you will serve as the technical bridge between our product team and frontier AI organizations, helping them architect robust experimentation and evaluation pipelines. You will drive the adoption of W&B components like Launch, Sweeps, and Models to solve complex challenges in LLM fine-tuning, RLHF, and large-scale distributed training.
Responsibilities
- Act as the primary technical advisor for Top-tier AI labs, guiding them on best practices for experiment tracking and model lineage.
- Design and implement custom integrations and workflows to streamline the transition from research to production-grade AI.
- Create high-quality technical content, including boilerplate repositories, technical blogs, and documentation for new W&B features.
- Collaborate with the Product and Engineering teams to translate customer feedback into new platform capabilities for Generative AI.
- Troubleshoot complex infrastructure bottlenecks related to W&B local deployments and data ingestion at scale.
- Lead deep-dive technical workshops and proof-of-concept (PoC) engagements for prospective enterprise partners.
- Build internal tools and scripts to automate the migration of legacy ML systems to the W&B ecosystem.
Requirements
- 4+ years of experience in Machine Learning engineering, Data Science, or a deeply technical customer-facing engineering role.
- Strong proficiency in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex) and evaluation methodologies.
- Proven track record of managing large-scale infrastructure for model training, including multi-node GPU clusters.
- Exceptional communication skills with the ability to explain complex technical MLOps concepts to both researchers and C-suite stakeholders.
- Ability to work autonomously across global time zones in a fully remote environment.
- Experience with cloud providers (AWS, GCP, Azure) and container orchestration using Kubernetes.
Benefits
Equity, benefits, remote.
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