Job Description
Are you ready to architect the intelligence layer of tomorrow? Apex Future Systems is looking for a visionary Senior Generative AI Engineer to lead our cutting-edge initiatives for the 2026 product roadmap. We are building the next generation of autonomous agents and multimodal reasoning engines, and we need a technical leader who can turn theoretical possibilities into production-ready reality.
In this pivotal role, you will not just implement existing models; you will push the boundaries of what is possible, contributing to the foundational architecture that will define the AI landscape of 2026. Join us in shaping the future of human-machine interaction.
Why Join Us?
- Future-First Technology: Work on bleeding-edge LLMs, RAG architectures, and ethical AI frameworks designed for the 2026 era.
- Elite Engineering Culture: Collaborate with world-class researchers and engineers in the heart of Silicon Valley.
- Equity & Impact: Competitive compensation package with significant equity upside in a high-growth startup.
Core Responsibilities
You will own the technical direction for our generative AI stack:
Responsibilities
- Design and implement scalable Generative AI pipelines focusing on Large Language Models (LLMs) and Diffusion models.
- Lead the development of Retrieval-Augmented Generation (RAG) systems to enhance model accuracy and reduce hallucinations.
- Optimize model inference latency and throughput for real-time, high-volume applications.
- Establish best practices for AI safety, bias mitigation, and responsible deployment.
- Collaborate cross-functionally with product managers and designers to define the 2026 AI vision.
- Conduct cutting-edge research to evaluate and integrate emerging architectures (e.g., Mamba, Switch Transformers).
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related technical field (4+ years of experience for Bachelor’s).
- Deep expertise in PyTorch, TensorFlow, or JAX with a strong portfolio of published models or patents.
- Proven experience deploying large-scale models in production environments (AWS, GCP, or Azure).
- Strong understanding of NLP concepts including Transformers, Attention mechanisms, and Tokenization strategies.
- Experience with vector databases (Pinecone, Weaviate, Milvus) and embedding strategies.
- Excellent communication skills and a passion for the ethical implications of advanced AI.