You are a creative problem solver with strong ML and engineering skills who thrives in a fast-paced environment working across teams and organizations. You enjoy learning new technologies and have a deep interest in ML/GenAI models and systems. You take ownership of your work communicate scope clearly and are motivated by the impact your models and systems will have on real-world users. Most importantly you care about building responsibly and sharing your learnings with the broader ML community. The main responsibilities for this position include:
Bachelor of Science in Computer Science Machine Learning or a related quantitative field or equivalent experience
2 years of hands-on experience in machine learning engineering
1 years focused on generative AI or LLM technologies or Agentic workflows
Solid experience in Python
Experienced building ML frameworks (PyTorch JAX) for training fine-tuning and deploying generative models at scale
Experience building enterprise-grade ML pipelines (data prep distributed training optimization monitoring) in cloud environments (AWS GCP Azure) or on-prem infrastructure
Deep understanding of transformer architectures prompt engineering retrieval-augmented generation (RAG) and LLM evaluation methodologies
Experience optimizing models for latency cost and scalability (quantization distillation hardware-aware ML)
Contributions to major open-source ML frameworks or research communities
MS or PhD in Computer Science Machine Learning or a related quantitative field
Solid grasp of NLP techniques multimodal AI (text image code) and agent workflows.
Experience with LLM Agentic workflows and framework (Langchain LangGraph LlamaIndex CrewAI etc.)
Knowledge in compiler/runtime optimizations for machine learning workloads
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