Job Description
Are you ready to architect the intelligent systems that will define the digital landscape of 2026?
Join Synapse Future Labs as a Senior AI/ML Engineer. We are a venture-backed team of futurists and engineers dedicated to building the next generation of Artificial General Intelligence (AGI). If you are passionate about pushing the boundaries of what is possible with deep learning and want to solve problems that matter, we want to meet you.
In this pivotal role, you will not just use existing tools; you will help build the tools of the future. You will be responsible for designing scalable neural architectures, optimizing inference pipelines, and deploying robust models that handle real-world complexity.
Responsibilities
- Design, train, and deploy state-of-the-art machine learning models with a focus on scalability, accuracy, and production readiness.
- Lead the architecture of proprietary neural network frameworks, ensuring high performance and low latency in high-traffic environments.
- Collaborate closely with our research division to translate theoretical advancements in NLP and computer vision into practical, commercial applications.
- Optimize existing models for inference speed and resource efficiency using advanced quantization, pruning, and model distillation techniques.
- Establish and enforce best practices for MLOps, CI/CD, and code quality across the engineering organization.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field, or equivalent extensive practical experience.
- 5+ years of professional experience in Machine Learning Engineering or Data Science, with a focus on deep learning.
- Strong proficiency in Python, PyTorch, TensorFlow, and modern deep learning libraries.
- Deep understanding of Large Language Models (LLMs), transformers, and Generative AI architectures.
- Proven experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Experience with vector databases and RAG (Retrieval-Augmented Generation) pipelines.