Job Description
Are you ready to architect the future? 2026 Systems is looking for a visionary Lead Machine Learning Engineer to spearhead the development of next-generation predictive models. We are not just building software; we are engineering the backbone of tomorrow's intelligence.
In this role, you will work at the intersection of data science and scalable infrastructure, leading a team of elite engineers to solve complex problems in real-time processing and generative AI. If you thrive in a fast-paced, high-impact environment and want to define the standards of 2026, we want to meet you.
Why Join Us?
- Impact: Your code will directly influence the trajectory of global logistics and automated decision-making systems.
- Culture: A diverse, inclusive environment that values radical candor and continuous learning.
- Equity: Competitive stock options in a unicorn-stage company.
Responsibilities
- Architect and implement scalable machine learning pipelines using Python, TensorFlow, and PyTorch.
- Lead a cross-functional team of data scientists and software engineers to deliver high-quality AI products.
- Design novel algorithms to improve data accuracy and model performance in high-traffic environments.
- Mentor junior engineers, conducting code reviews and technical architecture discussions.
- Collaborate with product managers to translate business requirements into technical solutions.
- Stay abreast of the latest research in Deep Learning and Natural Language Processing to integrate cutting-edge techniques.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field; PhD preferred.
- 5+ years of professional experience in machine learning engineering or data science.
- Strong proficiency in Python and SQL.
- Experience with distributed computing frameworks (Apache Spark, Kubernetes, AWS SageMaker).
- Deep understanding of statistical modeling and A/B testing methodologies.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.