Job Description
Are you ready to define the future of Artificial Intelligence?
Nexus Future Labs is at the forefront of the next technological revolution. We are seeking a visionary Senior AI Research Engineer to join our elite team and drive our roadmap through 2026 and beyond. In this role, you will not just use existing models; you will architect the foundation for the next generation of autonomous systems and generative intelligence.
You will work in a high-performance environment with access to cutting-edge compute resources, collaborating with world-class researchers and engineers to solve complex problems in Natural Language Processing (NLP), Computer Vision, and Reinforcement Learning.
Why join us?
- Shape the trajectory of AI technology for the year 2026 and the decades that follow.
- Competitive equity package and performance bonuses.
- Unlimited PTO and a culture of continuous learning.
If you are passionate about pushing the boundaries of what is possible with AI, we want to hear from you.
Responsibilities
- Model Architecture Design: Design, train, and fine-tune large-scale language models and multimodal systems tailored for enterprise applications.
- Research & Development: Conduct original research to improve model efficiency, accuracy, and safety standards.
- Deployment & MLOps: Oversee the deployment of models into production environments using containerization and orchestration tools.
- Performance Optimization: Optimize inference latency and throughput to ensure scalable, real-time AI performance.
- Collaboration: Partner with product managers and engineers to translate research into practical, user-facing features.
- Ethical AI: Implement robust guardrails and fairness checks to ensure AI outputs are responsible and unbiased.
Qualifications
- Education: MS or PhD in Computer Science, Mathematics, or a related field, with a focus on Machine Learning or Artificial Intelligence.
- Experience: 5+ years of professional experience in AI/ML engineering, preferably within a high-growth tech environment.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow. Deep understanding of Transformer architectures and LLMs.
- Tools: Experience with MLOps pipelines (e.g., MLflow, Kubeflow), Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
- Problem Solving: Strong analytical skills with a proven track record of solving complex engineering challenges.
- Communication: Ability to communicate complex technical concepts to both technical and non-technical stakeholders.