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About the role
We are seeking a highly motivated Senior Machine Learning Engineer to join our AI/ML team. This individual will play a central role in designing, building, and deploying the machine learning systems that power our platform, with a focus on large language models (LLMs), retrieval-augmented generation (RAG), agentic orchestration, and production-grade model deployment. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Master's degree (or above) in Computer Science, Machine Learning, AI, or a related field, equivalent practical experience considered
- 5+ years of professional experience in applied machine learning
- Strong foundation in machine learning, deep learning, and natural language processing (NLP)
- Proven experience deploying LLM-powered applications in production environments, including experience with frameworks such as PyTorch, vLLM, or LangGraph
- Hands-on experience with RAG, agentic orchestration, or multi-agent systems is strongly preferred
- Proficiency in Python and tools for ML development, large-scale data pipelines, and standard evaluation protocols
More about this role
We are seeking a highly motivated Senior Machine Learning Engineer to join our AI/ML team. This individual will play a central role in designing, building, and deploying the machine learning systems that power our platform, with a focus on large language models (LLMs), retrieval-augmented generation (RAG), agentic orchestration, and production-grade model deployment.
You will work across the full ML lifecycle, from framing problems and experimenting with models, to fine-tuning and deploying them in production and monitoring their performance. Beyond shipping models, you will influence architecture decisions, set ML best practices, and help define how AI shapes our product and scientific workflows. This is an opportunity to work closely with world-class scientists, engineers, and researchers in a fast-moving, mission-driven environment.
- Design, build, and deploy ML systems that power the product, translating scientific and product requirements into concrete ML problems (e.g., retrieval, summarization, classification, agentic reasoning)
- Architect and implement scalable RAG and LLM-based systems that integrate multi-modal data sources, including structured datasets, documents,...
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