Job Description
The Future is Here. Nexus Future Labs is pioneering the next generation of Artificial General Intelligence. We are seeking a visionary Senior AI/ML Engineer to architect the neural networks of tomorrow. If you are passionate about pushing the boundaries of what's possible in machine learning and want to define the landscape for 2026 and beyond, we want to meet you.
In this role, you will not just write code; you will build the brain of our ecosystem. You will work with cutting-edge LLMs, quantum-inspired algorithms, and ethical AI frameworks to deliver solutions that redefine human-machine interaction.
Why Join Us?
- Work on projects that directly impact the future of humanity.
- Competitive compensation package with equity options.
- Flexible remote-first culture with state-of-the-art equipment.
- Unlimited PTO and continuous learning budgets.
Responsibilities
- Design, implement, and deploy scalable machine learning models and AI architectures using Python, TensorFlow, and PyTorch.
- Lead the end-to-end lifecycle of data engineering, model training, fine-tuning, and evaluation processes.
- Collaborate with cross-functional teams of researchers, engineers, and product managers to translate complex business requirements into technical solutions.
- Optimize model inference speeds and reduce latency for real-time applications.
- Stay ahead of the curve by researching and integrating the latest breakthroughs in Generative AI and NLP.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field with a focus on Artificial Intelligence.
- Minimum of 5+ years of professional experience in Machine Learning, Deep Learning, or AI research.
- Extensive experience with large language models (LLMs), transformers, and neural network architectures.
- Proficiency in programming languages such as Python, C++, and SQL.
- Strong understanding of cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Experience with MLOps tools (MLflow, Kubeflow) and data versioning (DVC).
- Demonstrated ability to work in a fast-paced, agile environment.