Job Description
We are seeking a visionary Senior AI/ML Engineer to join Nexus Future Labs and help architect the intelligent systems of tomorrow. As we look toward the technological landscape of 2026, we are building a team that pushes the boundaries of Generative AI, Large Language Models (LLMs), and autonomous agents.
In this role, you will not just write code; you will define the architecture for next-generation machine learning systems that power our core products. You will work in a fast-paced, innovative environment where your contributions will directly impact millions of users. If you are passionate about solving complex problems and are ready to lead the AI revolution, we want to hear from you.
Why Join Us?
- Work with state-of-the-art AI frameworks and cloud infrastructure.
- Competitive equity package and top-tier health benefits.
- Flexible remote-first policy with a vibrant SF office culture.
Responsibilities
- Design, develop, and deploy scalable machine learning models and LLM pipelines with a focus on latency and accuracy.
- Collaborate with cross-functional teams of data scientists, product managers, and engineers to translate business requirements into technical AI solutions.
- Optimize existing models for production environments, ensuring high availability and low inference costs.
- Research and implement novel techniques in NLP, Computer Vision, or Reinforcement Learning to stay ahead of industry trends.
- Mentor junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
- Ensure ethical AI practices and data governance compliance in all model development lifecycle stages.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, Statistics, or a related field.
- 5+ years of professional experience in machine learning engineering, preferably in a production environment.
- Expert proficiency in Python and deep frameworks such as PyTorch or TensorFlow.
- Strong experience with MLOps tools (Kubeflow, MLflow, Airflow) and cloud platforms (AWS, GCP, or Azure).
- Proven track record of shipping complex AI products to market.
- Experience with vector databases and RAG (Retrieval-Augmented Generation) architectures.