Job Description
Join 2026, a pioneering force in generative AI and future-forward technology. We are on a mission to redefine the boundaries of human-machine interaction through scalable, secure, and sophisticated AI systems. As a Senior Machine Learning Engineer, you will lead the architecture and development of our next-generation models, working alongside world-class researchers and product visionaries.
We are looking for someone who thrives in ambiguity and possesses the technical depth to build systems that matter. If you are passionate about shaping the future of AI and want to work in an environment that values innovation, impact, and inclusivity, we want to meet you.
Responsibilities
- Model Development: Design, train, and deploy state-of-the-art Large Language Models (LLMs) and deep learning architectures tailored to enterprise-grade applications.
- System Architecture: Build scalable, fault-tolerant ML pipelines and infrastructure capable of handling high-throughput inference in real-time environments.
- Optimization: Continuously optimize model performance, reducing latency and computational costs while maintaining high accuracy and fidelity.
- Collaboration: Partner with cross-functional teamsâincluding product managers, designers, and software engineersâto translate complex business requirements into technical solutions.
- Mentorship: Guide junior engineers and data scientists, fostering a culture of technical excellence and continuous learning within the team.
- Research & Innovation: Stay at the forefront of the AI landscape, evaluating emerging technologies and methodologies to integrate them into our product roadmap.
Qualifications
- Education: Masterâs or PhD in Computer Science, Mathematics, Statistics, or a related field (or equivalent professional experience).
- Experience: Minimum of 5+ years of experience in machine learning, deep learning, or natural language processing.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX; experience with MLOps tools (MLflow, Kubeflow) and cloud platforms (AWS, GCP, or Azure).
- Algorithms: Strong understanding of statistical modeling, optimization algorithms, and distributed systems.
- Problem Solving: Demonstrated ability to tackle complex technical challenges and deliver robust, scalable solutions.
- Communication: Excellent written and verbal communication skills, with the ability to explain technical concepts to non-technical stakeholders.