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Zaimler is on a mission to democratize data discovery and curation for the era of generative AI. The Applied ML Research Engineer will join the ML team to turn raw enterprise data into structured knowledge graphs and embeddings, while prototyping new algorithms and improving model performance.

Responsibilities:

• Prototype and refine models for extracting structured knowledge from text

• Apply knowledge distillation techniques to compress and optimize LLMs for downstream tasks

• Explore the use of reinforcement learning and feedback loops for improving model behavior

• Build evaluation pipelines for entity linking, retrieval, and semantic consistency

• Develop new algorithms and data structures to improve extraction, tagging and resolution.

• Read, implement, and build upon recent research in LLM alignment, distillation, and symbolic grounding

• Collaborate closely with infra and data engineers to scale your research into production-ready components

Qualifications:

Required:

• 2–4 years experience (research lab, internship, academic project, or early industry role) working in ML or NLP

• Strong fundamentals turning research papers, algorithms and mathematics into working code

• Exposure to knowledge distillation, RLHF, or curriculum learning techniques

• Strong Python skills and familiarity with ML frameworks like PyTorch or TensorFlow

• Experience with language models and transformers (e.g., BERT, LLaMA, or similar)

• Solid understanding of ML fundamentals: training pipelines, loss functions, evaluation metrics

• A collaborative mindset and willingness to work across research and engineering teams

Preferred:

• Familiarity with reinforcement learning, including policy optimization or reward modeling

• Familiarity with using and scaling algorithms to manipulate data, including graph algorithms, text-manipulation, embeddings.

• Experience with semantic representations such as knowledge graphs or entity embeddings

• Comfort working with tools like HuggingFace Transformers, Ray, or vLLM

• Understanding of small-model techniques (pruning, quantization, adapter layers)

• Interest in the LLM ecosystem and techniques for model alignment or prompt tuning

• Prior contributions to open-source projects or academic publications in ML/NLP

Company:

Founded in , the company is headquartered in , with a team of 2-10 employees. The company is currently Early Stage.

Seniority level

• Seniority level

  Entry level

Employment type

• Employment type

  Full-time

Job function

• Industries

  Software Development

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Inferred from the description for this job

Medical insurance

Vision insurance

401(k)

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