Job Description
Are you ready to shape the technological landscape of 2026? Nexus Future Labs is seeking a visionary Senior AI/ML Engineer to lead our next-generation research initiatives. We are building the infrastructure that will define the future of intelligent systems, and we need a technical leader who can bridge the gap between theoretical AI breakthroughs and practical, scalable applications.
In this role, you will not just implement existing models; you will architect the foundational algorithms that will power our platform in 2026 and beyond. You will work in a collaborative environment with top-tier researchers and engineers, pushing the boundaries of what's possible with Large Language Models (LLMs) and autonomous agents.
Why join us?
- Work on cutting-edge AI projects with a clear roadmap to 2026.
- Competitive compensation package and equity options.
- Flexible hybrid work environment in the heart of Austin.
- Access to state-of-the-art compute resources and research data.
Responsibilities
- Architect Scalable AI Pipelines: Design and implement robust machine learning infrastructure capable of handling petabyte-scale data processing and real-time inference.
- Lead R&D for Next-Gen Models: Spearhead research into Generative AI, Reinforcement Learning, and multimodal learning models tailored for the 2026 ecosystem.
- Model Optimization: Optimize model performance for latency and accuracy, ensuring our AI solutions run efficiently on edge devices and cloud environments.
- Technical Mentorship: Guide a team of junior data scientists and ML engineers, fostering a culture of innovation and continuous learning.
- Cross-Functional Collaboration: Partner with product managers and software engineers to translate complex AI capabilities into user-centric features.
- Experimental Prototyping: Rapidly prototype and validate novel AI architectures to stay ahead of industry trends.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related technical field (or equivalent practical experience).
- Experience: 5+ years of professional experience in machine learning engineering, with at least 2 years leading technical projects.
- Technical Skills: Deep proficiency in Python, PyTorch, TensorFlow, or JAX.
- Modeling: Strong understanding of Deep Learning architectures, specifically Transformers and GANs.
- Tools: Experience with MLOps tools (Kubernetes, MLflow, Airflow) and cloud platforms (AWS, GCP, or Azure).
- Problem Solving: Demonstrated ability to solve complex, unstructured problems with data-driven solutions.
- Communication: Excellent verbal and written communication skills for technical and non-technical stakeholders.