Job Description
Join the Architects of Tomorrow.
At Nexus 2026, we are building the technological infrastructure for the next decade. We are seeking a highly skilled and visionary Senior Machine Learning Engineer to join our elite R&D team in San Francisco. In this role, you will not just implement existing algorithms; you will pioneer novel approaches to solve complex, unsolved problems in predictive analytics and generative AI.
Why Nexus 2026?
We are a forward-thinking collective focused on accelerating the trajectory towards a smarter, autonomous future. Our mission is to deliver solutions that were thought impossible just five years ago, and we are looking for talent that thrives on ambiguity and innovation.
Responsibilities
- Lead the end-to-end development of machine learning pipelines, from data ingestion and preprocessing to model training and deployment.
- Architect scalable, high-performance AI systems capable of processing petabytes of data in real-time.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate business requirements into technical solutions.
- Conduct rigorous A/B testing and performance monitoring to optimize model accuracy and reduce latency.
- Stay at the forefront of industry trends, evaluating and integrating cutting-edge research into our production environment.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, Statistics, or a related field.
- Minimum of 5+ years of professional experience in machine learning and deep learning.
- Proficiency in programming languages such as Python, PyTorch, or TensorFlow.
- Strong understanding of distributed computing systems (e.g., Spark, Kubernetes) and cloud platforms (AWS or GCP).
- Proven track record of deploying production-ready models and managing large-scale data infrastructure.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.