Senior Machine Learning Engineer

Pecan is an automated AI-based predictive analytics platform. It simplifies and accelerates the process of building and deploying machine learning models in various business use-cases, such as life-time value, Churn, demand forecast and more. Pecan connects to the raw data and completely automates the data preparation, engineering and prepossessing phases, as well as the model training and evaluation lifecycle. It was acknowledged as one of Israel's 50 most promising startups two years in a row

Company Highlights:

• Series C company with over $117M raised to date. Tier-1 investors: Google Ventures (GV), Insight Partners, GGV, Dell Ventures, Mindset and S Capital.
• 50 employees
• Customers across CPG, retail, healthcare, mobile apps, fintech, insurance, and consumer services. Marquee customers include Johnson & Johnson,, and SciPlay..

What You Will Do:

• Design, build, and own the scalable backend systems and APIs that power our entire AutoML platform turning raw customer data into production-ready predictive models that sit at the heart of Pecan’s product.
• Architect asynchronous, event-driven pipelines using message queues to orchestrate long-running, distributed machine learning training and inference jobs across multiple servers and cloud environments.
• Take end-to-end ownership of your microservices: you will architect, code, deploy, monitor, and continuously improve them in production.
• Tackle fascinating technical challenges at the intersection of extreme scale and the complex, uncharted territory of AutoML and real-world inference.

What You Need (Required):

• 6+ years of experience building high-performance, scalable server-side systems in production environments.
• Strong understanding of distributed systems, event-driven architectures, message queues, and eventual consistency patterns.
• Proven experience designing and running microservices that process and manage large-scale data pipelines reliably and efficiently.
• Hands-on experience with large databases, caching layers, and major cloud platforms.
• A fast-learning, “can-do” mindset. You thrive on solving complex problems, take full ownership, and are a true team player with zero ego.

Bonus Points (Nice to Have):

• Direct experience designing, training, or deploying machine learning models.
• Familiarity with MLOps practices model artifact management, preventing data leakage, model explainability, drift detection, and production model monitoring.