Machine Learning Research Engineer

Celonis•New York, NY

1d•$161,000 - $189,000•Hybrid

About The Position

As a Machine Learning Research Engineer, you will work at the intersection of AI research and software engineering. You will develop systems that analyze and optimize enterprise operations. This role requires collaboration across product and engineering teams to translate machine learning models into scalable product features.

Requirements

A 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) are a necessary prerequisite.
Production-level experience in Python (and frameworks like PyTorch, TensorFlow, or JAX), alongside 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, with excellent English communication skills (German is a plus).

Responsibilities

Design, test, and productionize machine learning algorithms for enterprise applications.
Research, develop and iterate on foundation model architectures for specialized domains.
Build and maintain ML infrastructure, pipelines, and backend services to support model deployment (MLOps).
Integrate Generative AI capabilities (such as LLMs and RAG architectures) into the core Celonis platform to drive automation and process insights.
Process and analyze complex operational datasets and knowledge graphs to train predictive models.
Implement engineering best practices, ensuring code is efficient and testable, and utilizing logging, monitoring, and model-drift alerting.
Collaborate with product managers, data engineers, and frontend developers to deploy end-to-end AI solutions.