Continual (1-10 Employees, 2 Yr Employee Growth Rate)
Continual is a Series A startup building the missing AI layer for the modern data stack. Our mission is to unlock the transformational power of machine learning and AI for every organization. We do this by delivering an operational AI platform designed natively for cloud data warehouses that empowers modern data and analytics teams to deliver production-grade machine learning solutions without operational burden. Our customers use Continual to help better understand their customers, operate more efficiently, and power innovative new products and services. You can learn more about Continual at https://continual.ai.
We offer competitive benefits, a collaborative work environment, flexible working arrangements, and rapid learning and growth opportunities. We’re a small team that cares deeply about our colleagues, customers, and mission. We embrace diversity and are committed to building a team that represents a variety of backgrounds, perspectives, and skills.
What this role offers:
This is a dream role for an AI/ML Engineer that wants to work at the intersection of AI/ML research and machine learning operations. As an AI/ML Engineer at Continual you will be responsible for understanding, experimenting with, and implementing new AI approaches, helping build the operational infrastructure that trains thousands of models per day, and conceiving of higher-level abstractions for AI that empower data teams to work smarter and faster.
Our mission is to empower modern data teams to leverage the transformational power of AI and you will be a key member of the team defining what this really means. This role offers the opportunity to think creatively about the ideal workflow for operational AI, not simply the particulars of specific models or infrastructure.
- Strong math, statistics, and machine learning knowledge
- 3+ years of experience building ML solutions or tools
- Fluency in Python
- Deep understanding of machine learning (e.g. PyTorch, TensorFlow, Keras, XGBoost, SciKit, Pandas) and MLOps ecosystems (e.g. Kubeflow/MLFlow)
- A strong intuition for elegant higher-level abstractions in AI/ML
- Familiarity with SQL and modern cloud data platforms (e.g. Snowflake, Databricks, Redshift, BigQuery)
- Familiarity distributed systems like Spark, Ray, or Dask.
- Excellent written and verbal communication skills
- Curiosity, drive, and flexibility to push beyond the status quo
- Graduate coursework in machine learning or equivalent experience.
- Evidence of creative problem solving (e.g. published research, writing, or tools)
- Experience with Go, Java, Scala, C++ or another statically typed language.
- Familiarity with Docker and Kubernetes.