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
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 clea...
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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