Job Description
We are seeking a visionary Senior Generative AI Engineer to lead our 2026 technology roadmap. As we stand on the precipice of a new era in artificial intelligence, you will be responsible for architecting scalable, multimodal systems that redefine user interaction and enterprise automation.
At Nexus Future Labs, we are building the infrastructure for tomorrow. You will work directly with our research team to fine-tune foundation models, optimize inference pipelines, and deploy intelligent agents that solve complex real-world problems. This is an opportunity to shape the future of AI.
Why You'll Love It Here
- Impactful Work: Build models that power the next generation of digital experiences.
- Top-Tier Compensation: Competitive salary, equity, and comprehensive benefits.
- Modern Stack: Access to the latest H100 clusters, TPUs, and open-source AI tools.
- Flexible Culture: Remote-first with a collaborative global engineering team.
Responsibilities
- Architect and deploy production-grade Large Language Models (LLMs) and diffusion models.
- Optimize model inference speed and reduce latency through advanced quantization and pruning techniques.
- Implement Retrieval-Augmented Generation (RAG) pipelines to enhance knowledge accuracy and reduce hallucinations.
- Collaborate with data scientists to fine-tune models on proprietary datasets using LoRA and PEFT methods.
- Establish robust monitoring and evaluation frameworks to ensure model safety, alignment, and drift management.
- Drive the technical vision for the 2026 AI stack, evaluating emerging technologies and frameworks.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related technical field (4+ years of experience required).
- Deep expertise in Python, PyTorch, and TensorFlow with a proven track record of deploying ML models.
- Strong understanding of Transformer architectures, attention mechanisms, and generative algorithms.
- Experience with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Familiarity with MLOps tools (MLflow, Kubeflow, Weights & Biases) and model serving platforms.