Job Description
At 2026, we are not just predicting the future; we are building it. We are seeking a visionary Senior Machine Learning Engineer to join our elite engineering team. As we move toward the next decade of AI, our infrastructure must be as revolutionary as the models we run.
In this role, you will be at the forefront of technical innovation, responsible for the architectural integrity of our AI stack. You will work in a fast-paced, high-performance environment where your code directly impacts millions of users globally.
Why join us?
- Competitive salary and equity package.
- Top-tier health, dental, and vision coverage.
- Flexible remote-first work culture.
- Access to cutting-edge hardware and research.
If you are passionate about engineering excellence and want to define the standards for AI infrastructure in the 2026 era, we want to hear from you.
Responsibilities
- Architect Scalable AI Systems: Design and implement robust machine learning infrastructure capable of handling petabyte-scale data flows.
- Optimize Model Performance: Continuously refine inference engines and reduce latency to ensure real-time responsiveness in critical applications.
- Cloud & MLOps Strategy: Oversee the deployment of CI/CD pipelines and automate the training, validation, and deployment lifecycle.
- Collaborate with Research: Partner with data scientists to translate theoretical models into production-ready software solutions.
- Security & Compliance: Enforce best practices for data privacy and model security within the AWS and Google Cloud environments.
- Talent Mentorship: Lead technical initiatives and mentor junior engineers to foster a culture of innovation and excellence.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Mathematics, or a related quantitative field.
- Experience: Minimum of 5+ years of experience in building and deploying large-scale machine learning systems.
- Programming: Expert proficiency in Python, with deep experience in frameworks like PyTorch, TensorFlow, or JAX.
- Cloud Expertise: Strong background in cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Distributed Systems: Solid understanding of distributed computing, message queues, and database systems.
- Problem Solving: Demonstrated ability to troubleshoot complex performance bottlenecks and architectural challenges.