Job Description
Shape the Future with 2026 AI Systems
We are on a mission to redefine the boundaries of artificial intelligence and predictive analytics. As a leader in the 2026 niche, we are building the infrastructure that will power the next generation of autonomous systems. We are seeking a visionary Senior Machine Learning Engineer to join our elite engineering team in San Francisco.
In this role, you won't just maintain models; you will architect them. You will work with a team of world-class researchers and engineers to deploy cutting-edge algorithms that solve complex, real-world problems at scale.
Why Join Us?
- Impact: Directly influence the core technology stack that will define the industry in 2026 and beyond.
- Innovation: Work with state-of-the-art hardware and cloud infrastructure.
- Growth: Unlimited learning budget and clear pathways to technical leadership.
Responsibilities
- Design, develop, and deploy robust machine learning models and data pipelines with a focus on scalability and fault tolerance.
- Collaborate with cross-functional teams of data scientists, researchers, and product managers to translate business requirements into technical solutions.
- Optimize existing algorithms for improved accuracy, speed, and efficiency in production environments.
- Conduct rigorous A/B testing and model monitoring to ensure model performance and data integrity.
- Lead code reviews and mentor junior engineers to foster a culture of technical excellence.
- Stay abreast of the latest advancements in AI research and implement relevant breakthroughs into our stack.
Qualifications
- Master’s degree or PhD in Computer Science, Mathematics, Statistics, or a related technical field.
- 5+ years of professional experience in machine learning engineering or data science roles.
- Strong proficiency in Python, PyTorch, or TensorFlow.
- Deep experience with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra).
- Experience deploying models to cloud environments (AWS, GCP, or Azure) using containerization tools like Docker and Kubernetes.
- Proven track record of working with large-scale datasets and distributed systems.