Job Description
We are on a mission to pioneer the next generation of artificial intelligence. Nexus Horizon is seeking a visionary Senior Machine Learning Engineer to join our elite 'Project 2026' initiative. This is a unique opportunity to shape the technological landscape as we look toward a future defined by autonomous systems and predictive intelligence.
In this role, you will not just implement existing algorithms; you will architect the core infrastructure for our next-generation AI products. We offer a competitive package, a remote-first culture with a hub in Austin, and the resources to push the boundaries of what is possible in the tech industry.
Why Join Us?
- Be at the forefront of the AI revolution leading up to 2026.
- Work with a world-class team of engineers and researchers.
- Competitive salary and equity package.
- Flexible working arrangements and continuous learning opportunities.
Responsibilities
- Architect Scalable Models: Design, develop, and deploy robust machine learning and deep learning models for the Project 2026 ecosystem.
- Lead Innovation: Spearhead the research and development of novel algorithms to solve complex, unstructured business problems.
- Infrastructure Management: Oversee the MLOps pipeline, ensuring model training, deployment, and monitoring are seamless and scalable.
- Team Mentorship: Guide junior engineers and data scientists, conducting code reviews and technical workshops to foster a culture of excellence.
- Performance Optimization: Continuously optimize model latency and accuracy to meet enterprise-grade standards.
- Stakeholder Collaboration: Translate complex technical concepts into actionable insights for product managers and business stakeholders.
Qualifications
- Education: Master’s degree in Computer Science, Mathematics, Statistics, or a related technical field (PhD preferred).
- Experience: Minimum of 5+ years of professional experience in machine learning engineering or data science.
- Tech Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with SQL and NoSQL databases.
- MLOps: Strong background in deploying models on cloud platforms (AWS, GCP, or Azure) using Docker, Kubernetes, and MLflow.
- Communication: Excellent verbal and written communication skills with the ability to present technical strategies to diverse audiences.
- Problem Solving: Proven track record of tackling ambiguous problems and delivering data-driven solutions.