Job Description
Join 2026, a pioneer in next-generation artificial intelligence, as our new Principal AI Engineer. We are building the infrastructure that will define the future of human-computer interaction, and we are looking for a visionary leader to drive our core research initiatives.
At 2026, we don't just predict the future; we engineer it. You will work alongside top-tier researchers and engineers to develop scalable machine learning systems that power our flagship products. If you are passionate about solving complex problems with elegant code and have a knack for turning data into actionable intelligence, we want to hear from you.
Why join us?
- Work on cutting-edge AI models with significant impact.
- Competitive compensation package with equity options.
- Flexible remote-first culture with top-tier benefits.
Responsibilities
- Lead Architecture: Design and implement scalable, high-performance AI architectures for large-scale data processing and model training.
- R&D Leadership: Drive research initiatives in Natural Language Processing (NLP) and Computer Vision to push the boundaries of current technology.
- Model Optimization: Fine-tune existing models and deploy them to production environments using modern MLOps pipelines (Kubernetes, Docker, Terraform).
- Cross-Functional Collaboration: Partner with product managers and data scientists to define technical roadmaps and ensure business goals are met through technical excellence.
- Code Review & Mentorship: Establish high engineering standards, conduct rigorous code reviews, and mentor junior engineers to foster a culture of continuous learning.
Qualifications
- Education: Master’s or PhD degree in Computer Science, Mathematics, Statistics, or a related technical field.
- Experience: 7+ years of professional experience in software engineering, with at least 4 years focused on Machine Learning and Deep Learning.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow; experience with SQL and NoSQL databases; familiarity with cloud platforms (AWS, GCP, or Azure).
- MLOps: Strong understanding of MLOps practices, CI/CD pipelines, and model deployment strategies.
- Problem Solving: Exceptional ability to deconstruct complex problems and derive efficient, scalable solutions.