Job Description
Are you ready to define the technological landscape of 2026?
Apex Future Systems is pioneering the next generation of Artificial Intelligence and Predictive Analytics. We are looking for a visionary Senior AI/ML Engineer to lead our research division. You won't just be building models; you will architect the neural networks that will power autonomous systems, advanced robotics, and sentient data interfaces in the coming decade.
In this role, you will bridge the gap between theoretical breakthroughs and scalable production environments. If you are obsessed with the future of tech and possess a deep understanding of generative models, we want to meet you.
Why join us?
- Work on cutting-edge projects with a mission to solve humanity's complex challenges.
- Competitive compensation package with equity options.
- Access to state-of-the-art computing infrastructure.
- Flexible remote-first culture with a San Francisco hub.
Responsibilities
- Architect and Train Large Language Models (LLMs): Design and optimize proprietary foundation models tailored for enterprise applications.
- Pipeline Optimization: Build high-throughput, low-latency data pipelines to handle real-time inference for autonomous agents.
- Research & Development: Explore novel architectures in reinforcement learning and generative adversarial networks (GANs) to push the boundaries of AI capability.
- Model Deployment: Deploy scalable AI solutions on cloud infrastructure (AWS/Azure/GCP) ensuring reliability and security.
- Ethical AI Implementation: Lead initiatives to ensure AI systems are fair, transparent, and compliant with emerging global regulations.
- Mentorship: Guide junior engineers and data scientists in advanced machine learning techniques.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, or a related technical field (or equivalent industry experience).
- Technical Stack: Expert proficiency in Python, PyTorch, TensorFlow, and Hugging Face Transformers.
- Experience: 5+ years of experience in machine learning engineering, specifically with NLP, Computer Vision, or Time-Series forecasting.
- Deployment: Proven track record of deploying production-grade ML models with strong performance monitoring skills.
- Cloud Expertise: Deep understanding of containerization (Docker/Kubernetes) and MLOps practices.
- Soft Skills: Exceptional problem-solving abilities and the ability to communicate complex technical concepts to non-technical stakeholders.