Machine Learning Research Engineer

Machine Learning Research Engineer

Location: Hybrid – Chicago, IL or Remote
Type: Full-Time
Seniority Level: Entry to Mid-Level
Industry: AI Research / Public Health / Cybersecurity

About the Role

We’re seeking a Machine Learning Research Engineer to join our applied research team focused on solving real-world problems in public health, misinformation detection, and security using cutting-edge AI. You'll work on cross-disciplinary projects involving computer vision, NLP, and geospatial intelligence, deploying scalable solutions that have measurable social impact.

Responsibilities

• Design, train, and evaluate advanced ML models in areas such as spatio-temporal action recognition, misinformation detection, or embedded computer vision systems

• Collaborate with researchers across disciplines (e.g., public health, cognitive science) to build ethically aligned, interpretable AI systems

• Build pipelines for multimodal data ingestion, model training, and evaluation using tools like PyTorch, Hugging Face Transformers, and OpenCV

• Develop and fine-tune object detection, classification, and language models using real-world datasets including satellite imagery and social media data

• Deploy models on cloud infrastructure (AWS Lambda, Docker, etc.) or edge devices (Raspberry Pi, Bluetooth sensors)

• Publish findings and contribute to peer-reviewed research and open-source tools

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field

• Strong experience with machine learning frameworks (PyTorch, TensorFlow)

• Proficiency in Python and familiarity with NLP or computer vision libraries (e.g., Hugging Face, OpenCV)

• Experience working with real-world data (e.g., social media APIs, satellite imagery, video streams)

• Familiarity with research workflows: from data collection to publication

• Bonus: Experience with edge computing, GIS, or high-performance computing environments

What We Offer

• Mission-driven work tackling real global challenges

• Access to compute resources (Lambda Labs, Polaris Supercomputers)

• Flexible hours and remote options

• Opportunities to publish and present your work