Machine Learning Researcher

Gray Swan AI

6d•$183,000 - $278,000•Hybrid

About The Position

As a Machine Learning Researcher at Gray Swan AI, you will work at the boundary between research and production — developing new approaches to adversarial testing, model evaluation, and robust inference that directly inform how secure AI systems are built and deployed. AI security is not a solved problem. This role is fundamentally about research: designing experiments, prototyping novel methods, analyzing results empirically, and translating findings into real systems that withstand adversarial pressure. You will collaborate closely with engineering and platform teams to see your ideas through from prototype to production impact.

Requirements

Bachelor's degree in Computer Science, Machine Learning, Engineering, or a related technical field.
Experience building and deploying ML models and systems.
Demonstrated expertise in designing, training, and deploying deep learning models, particularly with PyTorch.
Strong Python programming skills; C++ preferred.
Experience developing scalable ML pipelines and integrating with cloud infrastructure (AWS, GCP, or Azure).
ML research experience: building research prototype systems, designing experiments, empirical analysis of results, and communicating results via publications.
Neural network architectures, including transformers, sequence models, and other state-of-the-art approaches.
Strong algorithmic problem-solving skills and knowledge of ML theory and optimization.
Data preprocessing, transformation, and handling large-scale, multi-modal datasets.

Nice To Haves

A Master's or PhD in a relevant technical field is strongly preferred, especially with a focus on machine learning and AI safety.
Experience with LLMs (training, fine-tuning, or analyzing), synthetic data generation, and AI safety or security work.
AI safety practices: model validation, robustness testing, and continuous monitoring for safety and security incidents.
Familiarity with AI safety and security assessments and adversarial testing.

Responsibilities

Design and develop novel ML approaches to adversarial testing, model evaluation, and robust inference.
Build and deploy ML models that meet real-world performance and scalability requirements.
Design experiments, analyze results empirically, and communicate findings through publications and internal research reports.
Develop and advance methodologies for controlling, monitoring, and analyzing ML models in production environments.
Translate research ideas into scalable AI systems deployed in real-world, adversarial settings.
Work closely with cross-functional teams to ensure research outcomes inform production systems.