Pecan is an automated, AI-based predictive analytics platform that simplifies and accelerates the process of building and deploying machine learning models across various business use cases. Learn more about our dedicated offering here: demandforecast.ai.

Pecan connects to the raw data and completely automates the data preparation, engineering, and preprocessing phases, as well as the model training and evaluation lifecycle. It was acknowledged as one of Israel’s 50 most promising startups for two consecutive years.

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
• HQ in Tel Aviv with a growing sales and marketing organization in the US
• Customers across CPG, retail, healthcare, mobile apps, fintech, insurance, and consumer services. Marquee customers include Johnson & Johnson, Mars, and SciPlay.

About the Role

We are looking for a seasoned Machine Learning Engineer who isn't afraid to get their hands dirty with data infrastructure. In this hybrid role, you will bridge the gap between data engineering and model development. You won't just design and train models; you will take ownership of the pipelines that feed them and the infrastructure that serves them.

This is an impactful role for a builder who wants autonomy over the entire data-to-model lifecycle, from raw ingestion to production inference.

Who You Are

A problem solver at heart, you have a passion for excellence and a curiosity to learn, but you possess the pragmatism to deliver and ship code. You aren't threatened by complex, dynamic, or demanding environments. You believe deeply that “there is no I in team” and naturally look out for your peers. Crucially, you know how to take ownership—you don't wait for data to be handed to you; you go build the pipeline to get it.

What You’ll Do

• End-to-End ML Ownership: Design, train, and deploy machine learning models using modern libraries (XGBoost, LightGBM, Scikit-learn).
• Data Engineering & Pipelines: Design, build, and maintain scalable ETL pipelines and workflows (using Databricks, Spark, or Airflow) to ensure high-quality data availability for your models.
• Production Engineering: Own the MLOps lifecycle. Implement monitoring, alerting, and retraining loops to ensure model performance and data quality in production.
• System Design: Architect distributed systems that serve predictions at scale.
• Collaboration: Work closely with DS and Customer-Success teams to identify opportunities where data and AI can drive real business value.

What We're Looking For

• Experience: 5+ years of hands-on experience as a Machine Learning Engineer or a Data Engineer with a strong focus on ML.
• Strong Coding: Proficiency in Python is a must.
• Big Data & ETL: Strong understanding of distributed compute frameworks (Spark, Dask, Ray) and experience building data pipelines (Databricks, Airflow, or similar).
• ML Expertise: Hands-on experience with open-source ML libraries (NumPy, Pandas, Scikit-learn, XGBoost, etc.).
• Engineering Fundamentals: Strong grasp of object-oriented programming, and design patterns.
• Database Knowledge: Proficiency in SQL and data modeling.
• Infrastructure: Comfort working with Cloud environments (AWS/GCP/Azure).

Bonus Points

• BSc/MSc in Computer Science or a related quantitative field.