Job Description
Are you ready to architect the next generation of intelligent systems?
Nexus Future Labs is at the forefront of the AI revolution, and we are seeking a visionary Senior AI Product Engineer to join our elite R&D division. In this role, you will bridge the gap between cutting-edge machine learning research and scalable, production-ready applications. You will be instrumental in developing the foundational models that will define the technological landscape of 2026 and beyond.
Why Join Us?
- Work on proprietary Generative AI and Large Language Model (LLM) projects.
- Competitive compensation package with equity options.
- Access to state-of-the-art computing infrastructure and datasets.
- Collaborate with world-class engineers and data scientists.
If you are passionate about the future of AI and possess the technical prowess to build it, we want to hear from you.
Responsibilities
- Model Development: Design, train, and fine-tune large-scale neural networks and LLMs using Python and PyTorch.
- System Architecture: Design robust machine learning pipelines that ensure high availability, low latency, and scalability.
- Product Integration: Collaborate with product managers and frontend teams to integrate AI capabilities into user-facing products seamlessly.
- Performance Optimization: Continuously monitor, evaluate, and optimize model inference speed and accuracy.
- R&D Leadership: Stay ahead of the curve in emerging AI trends (e.g., Multimodal AI, Edge AI) and implement innovative solutions.
- Code Quality: Write clean, maintainable code and conduct thorough code reviews to maintain high engineering standards.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, or a related field (or equivalent practical experience).
- Experience: 5+ years of experience in software engineering with a strong focus on machine learning or artificial intelligence.
- Technical Skills: Proficiency in Python, TensorFlow, PyTorch, and SQL. Experience with cloud platforms (AWS/GCP) is required.
- Deep Learning: Strong understanding of deep learning architectures, NLP, and computer vision.
- Problem Solving: Demonstrated ability to tackle complex, ambiguous problems and deliver innovative solutions.
- Communication: Excellent verbal and written communication skills, with the ability to translate technical concepts to non-technical stakeholders.