Job Description
We are building the technology stack for the next decade. As a 2026 AI & Machine Learning Engineer, you will be at the forefront of developing next-generation artificial intelligence systems designed to solve complex global challenges.
In this high-impact role, you will not just use existing models; you will architect the foundational algorithms that define the future of human-machine interaction. We are looking for a technical visionary with a passion for scalability, ethics, and innovation.
Why Join Us?
- Work on projects that will define the AI landscape of 2026 and beyond.
- Competitive compensation package including equity options.
- Flexible remote-first culture with premium benefits.
- Access to cutting-edge hardware and proprietary datasets.
Key Objectives:
Translate abstract business requirements into robust, scalable AI models. Drive the deployment of Generative AI tools that enhance productivity and creativity across our enterprise ecosystem.
Responsibilities
- Architect and train state-of-the-art deep learning models for Large Language Models (LLMs) and Computer Vision applications.
- Optimize model inference pipelines to ensure sub-millisecond latency for real-time applications.
- Collaborate with product teams to integrate AI capabilities into user-facing products seamlessly.
- Conduct rigorous research to explore novel neural architectures and optimization techniques.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence.
- Ensure all AI systems adhere to strict ethical guidelines and bias mitigation standards.
- Manage the full ML lifecycle, from data ingestion and feature engineering to model monitoring and retraining.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related quantitative field (or equivalent professional experience).
- 3+ years of hands-on experience in Python, PyTorch, or TensorFlow.
- Proven track record of deploying ML models to production using cloud platforms (AWS, GCP, or Azure).
- Deep understanding of MLOps principles, CI/CD for ML, and containerization technologies (Docker, Kubernetes).
- Experience with distributed computing frameworks (Apache Spark, Ray) and high-performance computing.
- Strong problem-solving skills and the ability to work in a fast-paced, agile environment.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.