Job Description
Are you ready to define the technology stack of tomorrow?
Apex Horizon Technologies is looking for a visionary Senior AI Research Engineer to join our elite R&D division. We are not just building software for today; we are engineering the core intelligence that will drive the digital landscape in 2026 and beyond. You will work on cutting-edge Generative AI models, autonomous agents, and scalable neural architectures designed to solve complex, high-stakes problems.
In this role, you will bridge the gap between theoretical machine learning breakthroughs and production-grade engineering. You will lead a team of top-tier talent in a fast-paced environment, pushing the boundaries of what is possible with Large Language Models (LLMs) and reinforcement learning.
Why join Apex Horizon?
- Work on mission-critical AI infrastructure that will shape the future.
- Competitive compensation package with equity options.
- Flexible remote-first culture with hubs in San Francisco and New York.
Responsibilities
- Architect Next-Gen AI Systems: Design and implement proprietary neural network architectures optimized for the specific demands of 2026 workloads, focusing on efficiency and scalability.
- Model Training & Fine-Tuning: Lead the end-to-end training pipeline for Large Language Models (LLMs) and multimodal systems, utilizing proprietary datasets to enhance accuracy and reduce hallucinations.
- Research Leadership: Conduct high-level research in Natural Language Processing (NLP), Computer Vision, and Multi-Agent systems to stay ahead of industry trends.
- MLOps Implementation: Build robust, automated CI/CD pipelines for machine learning models to ensure rapid deployment and continuous integration.
- Cross-Functional Collaboration: Partner with product managers and software engineers to translate complex research findings into practical, user-friendly applications.
- Ethical AI Compliance: Ensure all models adhere to strict ethical guidelines and safety protocols regarding bias and data privacy.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related quantitative field from a top-tier university.
- Experience: Minimum 5+ years of experience in machine learning research or software engineering with a heavy focus on AI.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow. Deep understanding of transformer architectures, attention mechanisms, and distributed training.
- Tooling: Experience with MLOps platforms (e.g., Kubeflow, MLflow) and cloud infrastructure (AWS, GCP, or Azure).
- Problem Solving: Demonstrated ability to tackle ambiguous, unsolved problems and derive novel solutions.
- Communication: Exceptional ability to communicate complex technical concepts to both technical and non-technical stakeholders.