Job Description
Are you ready to architect the future of artificial intelligence? FutureScale 2026 is seeking a visionary Senior AI Research Engineer to join our elite team in the heart of Seattle. We are building the next generation of autonomous systems and predictive models that will define the technological landscape of the coming decade.
We offer a competitive compensation package, equity options, and a culture that prioritizes innovation, impact, and work-life balance. If you are a technical expert looking to push the boundaries of what is possible in Machine Learning and Deep Learning, we want to hear from you.
Responsibilities
- Lead Research Initiatives: Design, develop, and deploy cutting-edge machine learning models and algorithms to solve complex business problems.
- Model Optimization: Continuously improve model accuracy, scalability, and performance through rigorous testing and optimization strategies.
- Prototype Development: Build and validate Proof of Concepts (PoCs) for new AI technologies and integrate them into production environments.
- Collaboration: Work closely with cross-functional teams of data scientists, software engineers, and product managers to translate research into real-world applications.
- Technical Mentorship: Mentor junior engineers and researchers, fostering a culture of technical excellence and continuous learning within the team.
- Documentation: Create comprehensive documentation for models, methodologies, and technical standards to ensure knowledge transfer and reproducibility.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, Statistics, or a related field; equivalent practical experience is a plus.
- Experience: Minimum of 5+ years of professional experience in AI, Machine Learning, or Deep Learning.
- Programming: Expert-level proficiency in Python (PyTorch, TensorFlow, or JAX) and strong coding practices.
- Technical Skills: Deep understanding of NLP, Computer Vision, or Reinforcement Learning.
- Cloud Infrastructure: Experience deploying models on cloud platforms (AWS, GCP, or Azure) using containerization (Docker, Kubernetes).
- Problem Solving: Demonstrated ability to troubleshoot complex technical issues and drive projects from conception to completion.