Job Description
We are seeking a visionary Senior AI & Machine Learning Engineer to lead our next-generation intelligence initiatives. As we prepare for the technological landscape of 2026 and beyond, we need a technical expert who can bridge the gap between theoretical research and scalable production systems.
In this pivotal role, you will be instrumental in architecting the core of our AI infrastructure, driving innovation in Natural Language Processing (NLP), Computer Vision, and predictive analytics. You will work alongside a world-class team of data scientists and engineers to build products that define the future of human-computer interaction.
Why Join Us?
• Work on cutting-edge technology shaping the next era of AI.
• Competitive compensation package with equity options.
• Flexible work environment and remote-first culture.
Responsibilities
- Design, train, and deploy state-of-the-art machine learning models and deep learning architectures.
- Lead the end-to-end machine learning lifecycle, from data ingestion and feature engineering to model training, evaluation, and MLOps.
- Collaborate with cross-functional product teams to identify opportunities for AI integration and automation.
- Mentor junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
- Stay abreast of the latest advancements in AI research (e.g., Transformers, GANs, RLHF) and integrate relevant findings into our product stack.
- Optimize models for low-latency, high-throughput production environments.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field (PhD preferred).
- 5+ years of professional experience in machine learning engineering or data science.
- Proficiency in Python and modern machine learning frameworks (PyTorch, TensorFlow, JAX).
- Strong experience with Large Language Models (LLMs) and fine-tuning techniques (e.g., LoRA, PEFT).
- Deep understanding of data structures, algorithms, and software engineering best practices.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).