Job Description
Are you ready to shape the future of Artificial Intelligence?
Nexus Future Labs is at the forefront of the next technological revolution. We are seeking a visionary Senior Generative AI Engineer to lead the development of cutting-edge Large Language Models (LLMs) and autonomous agents. If you are passionate about pushing the boundaries of what's possible in 2026 and beyond, we want to hear from you.
In this role, you won't just write code; you will architect the cognitive backbone of our next-generation products. You will work in a high-performance environment with top-tier talent, utilizing the latest in transformer architectures, reinforcement learning, and ethical AI frameworks.
Why Join Nexus Future Labs?
- Impactful Work: Deploy models that will redefine human-computer interaction.
- Top-Tier Compensation: Competitive salary, equity packages, and annual bonuses.
- Future-Ready Tech Stack: Access to the most advanced compute resources and research papers.
- Culture of Innovation: A flat hierarchy where your ideas drive product direction.
Join us in building the intelligence layer of the web.
Responsibilities
- Design, train, and fine-tune state-of-the-art foundation models using proprietary and open-source datasets.
- Implement Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and reduce hallucinations.
- Optimize model inference latency and throughput for real-time applications using techniques like quantization and distillation.
- Collaborate with product managers and researchers to define AI product requirements and success metrics.
- Ensure ethical AI practices by auditing models for bias, safety, and fairness.
- Conduct cutting-edge research to explore emerging architectures such as MoE (Mixture of Experts) and multi-modal models.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in AI/ML engineering, with at least 2 years focused on NLP or LLMs.
- Programming: Expert-level proficiency in Python and C++. Deep understanding of PyTorch or TensorFlow.
- Modeling: Strong grasp of transformer architectures, attention mechanisms, and optimization algorithms.
- Tools: Experience with MLOps tools (DVC, MLflow, Kubeflow) and cloud platforms (AWS, GCP, Azure).
- Communication: Excellent technical writing and presentation skills; ability to translate complex concepts for diverse audiences.