Job Description
Are you ready to architect the future of predictive analytics? 2026 Systems is a pioneering AI research lab dedicated to solving complex problems for the next decade and beyond. We are looking for a visionary Senior Machine Learning Engineer to join our elite team in San Francisco. You will be at the forefront of developing cutting-edge algorithms that redefine industry standards.
In this role, you will bridge the gap between theoretical research and production-grade deployment, ensuring our models are scalable, robust, and ready for the demands of 2026 and beyond.
Why Join 2026 Systems?
- Work on high-impact projects that shape the trajectory of AI.
- Competitive compensation and equity packages.
- Flexible remote-first culture with state-of-the-art equipment.
- Opportunity to mentor junior engineers and lead architectural decisions.
Responsibilities
- Model Development: Design, train, and deploy advanced machine learning models, including Deep Learning and Reinforcement Learning architectures.
- System Architecture: Lead the architecture of large-scale data pipelines and machine learning infrastructure on cloud platforms (AWS/GCP).
- Optimization: Continuously monitor and optimize model performance for latency, throughput, and accuracy in real-time environments.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and engineers to translate business requirements into technical solutions.
- Research & Innovation: Stay abreast of the latest academic research and industry trends to implement novel techniques in our production stack.
- Mentorship: Guide and mentor junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
Qualifications
- Education: Master’s or PhD degree in Computer Science, Mathematics, Statistics, or a related technical field.
- Experience: 5+ years of professional experience in software engineering and machine learning.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, or Scikit-learn.
- Infrastructure: Experience with containerization (Docker/Kubernetes) and cloud services (AWS, GCP, or Azure).
- Problem Solving: Proven track record of tackling complex, unstructured problems and delivering scalable solutions.
- Communication: Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.