Job Description
Are you ready to architect the future of Artificial Intelligence?
Apex Neural Systems is seeking a visionary Senior AI Research Engineer to lead the development of next-generation Agentic AI architectures. As we prepare for the 2026 AI landscape, we need a technical leader who understands not just current models, but the trajectory of autonomous reasoning and scalable intelligence.
In this role, you will be at the forefront of integrating Large Language Models (LLMs) with autonomous agent frameworks, pushing the boundaries of what is possible in enterprise automation and decision-making systems. You will define the technical roadmap for our research initiatives, ensuring our solutions are robust, efficient, and ahead of the curve.
Why join us?
• Work on cutting-edge AI systems that will define the industry in 2026.
• Competitive compensation and equity package.
• Collaborative culture focused on innovation and impact.
• Opportunity to shape the future of AI infrastructure.
Responsibilities
- Architect 2026-ready AI Systems: Design scalable inference pipelines and training architectures for large language models and autonomous agents capable of complex reasoning.
- Conduct cutting-edge research to improve model planning capabilities, memory retention, and multi-step task execution.
- Optimize model performance and reduce latency in real-time applications to ensure seamless user experiences.
- Collaborate closely with product and engineering teams to translate advanced research into deployable, production-grade features.
- Mentor a team of talented machine learning engineers and researchers, fostering a culture of technical excellence.
- Stay ahead of industry trends, specifically focusing on the evolution of AI agents and multimodal systems.
Qualifications
- Ph.D. or M.S. in Computer Science, Mathematics, or a related field with a focus on AI/ML.
- 5+ years of professional experience in machine learning research or applied AI, with a portfolio of published work or significant open-source contributions.
- Deep proficiency in Python, PyTorch, and TensorFlow.
- Strong understanding of transformer architectures, attention mechanisms, and optimization techniques.
- Experience with distributed training systems (e.g., Ray, Kubernetes, Spark) and high-performance computing environments.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.