Job Description
Are you ready to architect the future? Horizon 2026 is at the forefront of next-generation artificial intelligence, building the cognitive infrastructure for a smarter tomorrow. We are seeking a visionary Senior AI/ML Engineer to lead our research and development efforts in predictive modeling and autonomous systems.
In this role, you won't just maintain legacy systems; you will pioneer the algorithms that define the landscape of 2026 and beyond. If you thrive in a high-performance environment and are passionate about pushing the boundaries of what's possible with machine learning, we want to meet you.
Why Join Horizon 2026?
We offer a competitive salary, equity package, and the opportunity to work on projects that will impact the global tech ecosystem.
Responsibilities
- Architect Scalable ML Pipelines: Design, develop, and deploy end-to-end machine learning pipelines that handle petabyte-scale data with zero latency.
- Pioneer New Algorithms: Research and implement novel deep learning architectures to improve our predictive accuracy for autonomous decision-making systems.
- Model Optimization: Continuously monitor and optimize existing models for latency, throughput, and cost efficiency in cloud environments.
- Collaborative Innovation: Work closely with cross-functional teams of data scientists, engineers, and product managers to translate business requirements into technical solutions.
- Mentorship: Guide junior engineers and data scientists, fostering a culture of technical excellence and continuous learning within the engineering squad.
- Risk Mitigation: Identify potential biases in data sets and models to ensure fair, ethical, and robust AI deployment.
Qualifications
- Education: Master’s or PhD in Computer Science, Statistics, Mathematics, or a related field.
- Experience: 5+ years of professional experience in machine learning, deep learning, or AI engineering.
- Technical Proficiency: Strong proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with MLOps tools (Docker, Kubernetes, MLflow) is required.
- Data Engineering: Solid understanding of big data technologies (Spark, Hadoop) and SQL databases.
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders.
- Problem Solving: Demonstrated ability to troubleshoot complex system issues and innovate under tight deadlines.