Machine Learning Engineer

Poznan , Greater Poland Voivodeship, Poland

Type: n/a

Category: Construction & Trades

Requirements: English
Company: Cerebre
Region: Poznan , Greater Poland Voivodeship

Cerebre is a rapidly growing global team on a mission to digitize the physical world. Our software transforms traditional sources of engineering knowledge into accessible, usable data that supports field operations and advanced analytics.

Our team is developing cutting-edge technology to make our physical world safer, more productive, and environmentally sustainable.

We collaborate with the largest companies and most innovative clients and partners who aim to transform the industrial industry.

Our development team is comprised of world-class engineers who design and create novel solutions.

This is an opportunity to join a market-leading team with opportunities to change the industrial world.

If you love building and creating value in the "white space," if you love freedom and flexibility to think outside the box, if you are passionate about working with critical thinkers who challenge the status quo, and if you aspire to work in a fast-paced environment, we would love to get to know you!

We believe flexibility leads to creativity and that our team should live and work where we are our best selves. We are 100% remote with flexible PTO and unlimited mental health days.

About the role

As a Machine Learning Engineer at Cerebre, you will be at the forefront of building intelligent solutions for industrial challenges. We are seeking an individual with a strong background in deep learning and hands-on experience in building and optimizing machine learning models. You will work alongside a multidisciplinary team to solve complex challenges and contribute to our mission of transforming the industrial landscape.

Key Responsibilities

• Design, train, and fine-tune deep learning models using architectures such as CNNs, RNNs, and Transformers
• Develop and optimize Large Language Models (LLMs) using Retrieval-Augmented Generation (RAG) architecture, Transformers, and specialized frameworks
• Implement advanced computer vision techniques, including Instance Segmentation, Panoptic Segmentation, and Semantic Segmentation
• Build and deploy intelligent multi-agent AI systems using frameworks like LangChain, LangGraph, AutoGen, or Semantic Kernel
• Deploy scalable models using Azure Machine Learning, Databricks, or other cloud-based platforms

Required Qualifications

• Programming: Proficiency in Python and hands-on experience with frameworks like PyTorch (including PyTorch Geometric), TensorFlow, Keras, Pandas, Scikit-Learn, and NumPy
• Deep Learning: Solid understanding of model architectures, activation functions, cost functions, regularization techniques, and quantization
• Model Training Optimization: Ability to diagnose and resolve issues such as overfitting and underfitting
• Computer Vision: Experience with advanced segmentation techniques
• Natural Language Processing: Hands-on experience with Large Language Models (LLMs), including Transformers, model fine-tuning, and Retrieval-Augmented Generation (RAG) architectures
• Multi-Agent Systems: Familiarity with frameworks like LangChain, LangGraph, or AutoGen
• Vector Databases Knowledge Graphs: Experience using tools like Chroma, FAISS, or similar is a plus
• Deployment: Experience in deploying models as endpoints and working with cloud platforms such as Azure Machine Learning or Databricks
• Research Publications: Previous contributions to ML/DL research and publications are highly valued
• Bonus: A basic understanding of the .NET framework and C# is considered a plus

More about Cerebre

We are cross-functional collaborators.

We blend manufacturing process knowledge with software and big data engineering expertise to create value in physical settings

We are experienced.

We are armed with industry-leading experts in numerical simulation, combustion, power, computational fluid dynamics, and chemical process modeling

We are serious builders.

We develop our platforms using leading practices in IT/OT architecture, OT security, AI architecture, ML Ops, and Platform engineering

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