Machine Learning Research Engineer

Celonis Celonis · Data AI · New York, NY · Engineering

Machine Learning Research Engineer at Celonis, a leader in Process Intelligence. The role focuses on developing and productionizing ML algorithms and foundation models for enterprise applications, integrating Generative AI (LLMs, RAG) into the core platform, and building ML infrastructure. Requires a Master's/Ph.D., 3+ years of ML/AI experience, proficiency in Python, cloud platforms, and data handling.

What you'd actually do

  1. Design, test, and productionize machine learning algorithms for enterprise applications.
  2. Research, develop and iterate on foundation model architectures for specialized domains.
  3. Build and maintain ML infrastructure, pipelines, and backend services to support model deployment (MLOps).
  4. Integrate Generative AI capabilities (such as LLMs and RAG architectures) into the core Celonis platform to drive automation and process insights.
  5. Process and analyze complex operational datasets and knowledge graphs to train predictive models.

Skills

Required

  • Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, or a highly related field.
  • 3+ years of experience in academia or industry, specializing in machine learning, deep learning, NLP, or artificial intelligence.
  • Practical experience with Generative AI technologies, including Large Language Models (LLMs), fine-tuning, Prompt Engineering, and Retrieval-Augmented Generation (RAG).
  • Comfort with established Machine Learning methods and paradigms, e.g. bias-variance tradeoff, overfitting, across a variety of settings (supervised, unsupervised, semi-supervised, structured, RL).
  • Production-level experience in Python (and frameworks like PyTorch, TensorFlow, or JAX).
  • Exposure to at least one other programming language (e.g., Java, Go, or C++).
  • Experience deploying models using Docker, Kubernetes, and cloud compute services (AWS EC2/Lambda/SageMaker, Azure, or GCP).
  • Proficiency in writing complex SQL queries, working with graph databases, and processing large datasets.
  • Strong analytical and problem-solving abilities.
  • Excellent English communication skills.

Nice to have

  • German language skills

What the JD emphasized

  • productionize machine learning algorithms for enterprise applications
  • foundation model architectures
  • Generative AI capabilities
  • LLMs
  • RAG architectures
  • complex operational datasets
  • knowledge graphs
  • predictive models
  • MLOps
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • bias-variance tradeoff
  • overfitting
  • supervised
  • unsupervised
  • semi-supervised
  • structured
  • RL
  • Python
  • PyTorch
  • TensorFlow
  • JAX
  • Docker
  • Kubernetes
  • AWS EC2/Lambda/SageMaker
  • Azure
  • GCP
  • SQL
  • graph databases

Other signals

  • integrating generative AI into core platform
  • productionizing ML algorithms for enterprise applications
  • researching and developing foundation model architectures