Job Description
Join 2026 Technologies as a Senior Machine Learning Engineer and help architect the infrastructure that will define the next decade of artificial intelligence. We are at the forefront of the 2026 Initiative, a project focused on developing sustainable, scalable, and ethical AI models for enterprise deployment. If you are passionate about pushing the boundaries of deep learning and want to work in a high-performance environment, we want to hear from you.
Why Join Us?
- Impactful Work: Directly contribute to the core algorithms powering the 2026 platform.
- Top-Tier Team: Collaborate with world-class researchers and engineers from top-tier universities and tech giants.
- Modern Stack: Work with the latest in PyTorch, TensorFlow, and distributed computing systems.
We are looking for a visionary engineer who thrives in a fast-paced, innovative culture.
Responsibilities
- Design, develop, and optimize complex machine learning models and deep neural networks tailored for large-scale production environments.
- Lead the end-to-end lifecycle of ML projects, from data ingestion and preprocessing to model training, evaluation, and deployment.
- Collaborate with cross-functional teams (data science, engineering, product) to translate business requirements into technical solutions.
- Implement best practices for MLOps, including CI/CD pipelines, model monitoring, and automated retraining strategies.
- Conduct rigorous experimentation and research to stay ahead of the curve in emerging AI technologies.
- Ensure model transparency, fairness, and compliance with ethical AI standards.
Qualifications
- PhD or Master’s degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
- Minimum of 5+ years of professional experience in machine learning, deep learning, or a related domain.
- Strong proficiency in Python, including deep knowledge of libraries such as PyTorch, TensorFlow, or JAX.
- Experience with big data technologies (e.g., Spark, Hadoop, Kafka) and distributed computing frameworks (e.g., Ray, Kubernetes).
- Proven track record of deploying scalable ML models to production (AWS, GCP, or Azure).
- Excellent problem-solving skills and the ability to work independently in a dynamic environment.