Job Description
We are building the infrastructure for tomorrow. Quantum Horizon Labs is seeking a visionary Senior AI/ML Engineer to spearhead our proprietary 2026 roadmap. In this role, you will bridge the gap between theoretical AI research and scalable production systems, working on next-generation Large Language Models (LLMs) and autonomous agent architectures.
If you are passionate about pushing the boundaries of artificial general intelligence and want to leave a lasting impact on the tech landscape, we want to meet you.
Responsibilities
- Architect & Lead: Design and implement state-of-the-art machine learning pipelines and deep learning frameworks for our core 2026 product suite.
- Model Optimization: Refine and optimize large-scale models for latency, throughput, and memory efficiency to ensure seamless user experiences.
- Research & Innovation: Stay at the forefront of AI research, adapting cutting-edge methodologies (e.g., reinforcement learning, transformers) into practical engineering solutions.
- Team Mentorship: Guide a team of junior data scientists and engineers, fostering a culture of technical excellence and continuous learning.
- Collaboration: Partner closely with product managers and engineering teams to define AI requirements and translate them into technical specifications.
- Deployment: Oversee the end-to-end MLOps lifecycle, including CI/CD, model serving, and monitoring in production environments.
Qualifications
- Experience: 5+ years of professional experience in machine learning, deep learning, or artificial intelligence engineering.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Strong understanding of distributed computing and cloud infrastructure (AWS/GCP/Azure).
- Education: MS or PhD in Computer Science, Mathematics, or a related technical field is preferred.
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems and deliver robust, scalable solutions under tight deadlines.
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders.
- Tools: Experience with MLOps tools (Kubernetes, Docker, MLflow) and data visualization libraries.