Job Description
At 2026, we are not just predicting the future; we are architecting it. We are a premier research lab and tech firm dedicated to pushing the boundaries of generative AI and cognitive computing. We are looking for a visionary Senior AI Engineer to lead our model training infrastructure and drive the next wave of intelligent automation.
If you are passionate about Large Language Models (LLMs), neural architecture search, and building systems that think, we want to meet you. Join a team of world-class researchers and engineers working on projects that will define the technological landscape of the decade.
Why Join 2026?
- Work on cutting-edge Generative AI technologies.
- Competitive compensation package including equity.
- Flexible remote-first culture with state-of-the-art equipment.
Your Impact:
- Architect and optimize large-scale deep learning models.
- Reduce inference costs and improve model latency.
- Lead the design of data pipelines for training and fine-tuning.
Responsibilities
- Model Development: Design, train, and fine-tune state-of-the-art transformer-based models (e.g., GPT, Llama variants) for specific enterprise applications.
- Infrastructure Optimization: Implement MLOps best practices to ensure scalable, reproducible, and efficient model deployment on cloud platforms (AWS/GCP).
- Data Strategy: Curate high-quality datasets and implement advanced data preprocessing and augmentation techniques.
- Research & Innovation: Stay at the forefront of AI research, experiment with novel architectures, and publish findings in top-tier conferences.
- Collaboration: Work closely with product managers and software engineers to integrate AI capabilities into seamless user experiences.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 5+ years of professional experience in Machine Learning or Deep Learning.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Domain Knowledge: Deep understanding of NLP, Computer Vision, or Reinforcement Learning.
- Tools: Experience with Kubernetes, Docker, and cloud ML services.
- Communication: Ability to translate complex technical concepts for diverse stakeholders.