Job Description
The Future is Now. Apex Horizon Technologies is pioneering the 2026 Initiative, a groundbreaking research and development program dedicated to advancing autonomous reasoning and generative AI systems. We are looking for a visionary Senior AI/ML Engineer to join our elite research division in San Francisco.
In this role, you will not just build models; you will architect the cognitive infrastructure for the next decade. You will work at the intersection of neuroscience, deep learning, and distributed systems to create systems that exceed human efficiency in complex environments.
Why Join the 2026 Initiative?
- Work on the bleeding edge of Artificial General Intelligence (AGI) research.
- Competitive equity package and top-tier compensation.
- Flexible remote-first culture with a hub in the heart of the Bay Area.
We are looking for individuals who are obsessed with performance, scalability, and ethical AI implementation.
Responsibilities
- Architecture Design: Design and implement scalable deep learning frameworks and neural network architectures optimized for the 2026 roadmap.
- Model Optimization: Apply techniques such as Quantization, Pruning, and Knowledge Distillation to deploy high-performance models on resource-constrained hardware.
- Research & Development: Conduct cutting-edge research to improve model accuracy, reducing hallucination rates in generative tasks.
- Infrastructure: Collaborate with the DevOps team to build MLOps pipelines ensuring seamless CI/CD for machine learning models.
- Prototyping: Rapidly prototype and validate novel AI concepts, translating theoretical research into production-ready code.
- Team Leadership: Mentor junior engineers and data scientists, fostering a culture of technical excellence and innovation.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field (or equivalent practical experience).
- Core Tech: Strong proficiency in Python, PyTorch, TensorFlow, and CUDA.
- Experience: 5+ years of experience in machine learning engineering, preferably in NLP, Computer Vision, or Reinforcement Learning.
- System Design: Deep understanding of distributed systems, microservices, and cloud-native architecture (AWS/GCP).
- Tools: Experience with Kubernetes, Docker, and MLflow or similar MLOps tools.
- Language: Fluency in English with the ability to communicate complex technical concepts clearly.