Job Description
Shape the Future of Intelligence.
OmniCorp Future Systems is at the forefront of the 2026 technological revolution. We are seeking a visionary Senior AI Research Scientist to lead our breakthrough initiatives in Generative AI, Large Language Models (LLMs), and Autonomous Systems. If you are passionate about pushing the boundaries of what is possible in machine learning and want to work in a high-performance environment that values innovation, we want to hear from you.
As a key member of our Research Division, you will be responsible for developing next-generation algorithms that power our core products. We offer a competitive salary, equity packages, and the opportunity to work with world-class talent in the heart of the Pacific Northwest.
Why Join Us?
- Work on cutting-edge AI technologies for 2026 and beyond.
- Flexible remote-first culture with quarterly all-hands in Seattle.
- Unlimited PTO and comprehensive health benefits.
- Professional development budget and access to premium tech stacks.
Responsibilities
- Architect Development: Design, train, and deploy state-of-the-art machine learning models and neural network architectures.
- Research Leadership: Lead research initiatives to explore new frontiers in Natural Language Processing (NLP) and Computer Vision.
- Model Optimization: Improve model accuracy, scalability, and inference speed for production environments.
- Cross-Functional Collaboration: Partner with product managers and software engineers to translate research into scalable, user-friendly products.
- Mentorship: Guide and mentor junior data scientists and research engineers within the team.
- Technical Strategy: Stay abreast of the latest academic papers and industry trends to inform our technical roadmap.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 5+ years of proven experience in AI/ML research or applied machine learning.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Theory: Deep understanding of statistical learning, probability, and optimization theory.
- Tools: Experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (MLflow, Kubeflow).
- Communication: Excellent written and verbal communication skills with the ability to present complex technical concepts to diverse audiences.