Job Description
We are building the future of intelligence. At 2026 Labs, we are not just predicting what comes next; we are architecting it. We are looking for a visionary Senior AI/ML Engineer to join our elite team and help define the technological landscape of 2026 and beyond.
In this role, you will spearhead the development of next-generation generative models and autonomous systems. You will work in a high-performance environment where innovation is the currency, and your code will power the core infrastructure of the 2026 ecosystem.
Why join 2026 Labs?
- Work on cutting-edge AI research with real-world impact.
- Competitive compensation and equity packages.
- Flexible remote-first culture with state-of-the-art equipment.
- Opportunity to shape the roadmap for the 2026 technological horizon.
Responsibilities
- Architect AI Systems: Design and implement scalable, robust machine learning pipelines and deep learning architectures for the 2026 platform.
- Model Optimization: Continuously refine and optimize models for latency, throughput, and accuracy using advanced techniques like quantization and distillation.
- Research & Development: Stay at the forefront of the AI field, exploring new methodologies and integrating them into our production systems.
- MLOps Implementation: Build and maintain CI/CD pipelines for model training, validation, and deployment using Kubernetes and cloud-native technologies.
- Cross-Functional Leadership: Collaborate with product managers, engineers, and designers to translate complex technical requirements into elegant user solutions.
- Code Review & Mentorship: Lead technical initiatives and mentor junior engineers, fostering a culture of excellence and continuous learning.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field with a focus on AI/ML.
- Experience: 5+ years of professional experience in AI/ML engineering, preferably in a high-scale production environment.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, and Hugging Face Transformers.
- Infrastructure: Strong experience with MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS, GCP, or Azure).
- Problem Solving: Deep understanding of neural networks, NLP, and computer vision (or relevant specialization).
- Communication: Excellent verbal and written communication skills, capable of articulating complex technical concepts to non-technical stakeholders.