Job Description
Architecting the Future of Intelligence
Apex 2026 Solutions is at the forefront of next-generation technology. We are looking for a visionary Senior AI Research Engineer to join our elite San Francisco team. In this pivotal role, you will drive the research and development of proprietary Large Language Models (LLMs) and autonomous agent systems designed for the year 2026 and beyond.
We offer a competitive compensation package, equity options, and the opportunity to work with cutting-edge hardware and software stacks. If you are passionate about pushing the boundaries of what is possible in Artificial Intelligence, we want to hear from you.
What You Will Do
Lead Research Initiatives: Spearhead the design and implementation of novel deep learning architectures tailored for high-scale data processing.
Model Optimization: Fine-tune pre-trained models to improve inference speed, accuracy, and energy efficiency for edge deployment.
Cross-Functional Collaboration: Work closely with product managers, data engineers, and security experts to ensure AI solutions are robust, scalable, and ethically sound.
Technical Mentorship: Mentor junior researchers and engineers, fostering a culture of continuous learning and innovation within the team.
Responsibilities
- Research, design, and implement state-of-the-art machine learning algorithms and neural network architectures.
- Optimize existing models for production environments to ensure low latency and high throughput.
- Analyze complex datasets to derive actionable insights and drive product strategy.
- Stay abreast of the latest advancements in the AI field and integrate relevant innovations into the company's roadmap.
- Collaborate with stakeholders to define technical requirements and deliver high-quality software solutions.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, or a related field.
- 5+ years of professional experience in AI/ML research or software engineering with a strong focus on NLP.
- Proficiency in Python, PyTorch, or TensorFlow.
- Strong understanding of distributed computing, cloud platforms (AWS/GCP), and containerization (Docker/Kubernetes).
- Experience with fine-tuning LLMs (e.g., GPT, Llama, BERT) and RAG architectures.