Job Description
We are seeking a visionary Senior Generative AI Engineer to join Nexus Future Labs. As a pioneer in the AI space, we are building the next generation of Large Language Models (LLMs) and multimodal systems that will redefine human-computer interaction. You will be at the forefront of the AI revolution, working on cutting-edge models that power enterprise solutions.
Why Join Us?
- Work with state-of-the-art technology including Transformers, Diffusion models, and Reinforcement Learning from Human Feedback (RLHF).
- Competitive compensation package with equity options.
- Flexible remote-first policy with a focus on results.
If you are passionate about pushing the boundaries of artificial intelligence and possess deep technical expertise, we want to hear from you.
Responsibilities
- Model Development: Design, train, and fine-tune large-scale generative models (e.g., GPT, Llama variants) using custom datasets.
- Optimization: Implement advanced optimization techniques to improve model inference speed and reduce latency in production environments.
- Architecture: Design scalable distributed training pipelines and infrastructure for handling massive datasets.
- Evaluation: Establish rigorous evaluation frameworks to measure model performance, safety, and alignment with human values.
- Collaboration: Partner with data scientists, product managers, and researchers to translate business requirements into technical AI solutions.
- RAG Systems: Develop and deploy Retrieval-Augmented Generation (RAG) architectures to enhance model accuracy and reduce hallucinations.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Machine Learning, Mathematics, or a related field.
- Experience: 5+ years of experience in deep learning, natural language processing (NLP), or computer vision.
- Programming: Expert-level proficiency in Python and C++.
- Frameworks: Strong experience with PyTorch, TensorFlow, or JAX.
- LLM Knowledge: Deep understanding of transformer architectures, pre-training, and fine-tuning methodologies.
- Tools: Familiarity with MLOps tools (MLflow, Kubeflow), version control (Git), and cloud platforms (AWS, GCP, or Azure).
- Soft Skills: Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.