Afresh - Software Engineer, ML Platform
Requirements
• BS in Computer Science or a relevant technical field. • 3+ years of professional software development experience with a proven track record of shipping high-quality applications and services. • Experience working collaboratively with machine learning engineers, data scientists, or applied scientists on large-scale software projects involving machine learning models. • Deep expertise in library design, API design, data structures, and algorithms. • Strong familiarity with Python. • Python • Tech Stack: Our backend is pure Python (NumPy, Pandas, Torch, PySpark, Cython, orchestrated in Airflow). We use Databricks as our data warehouse. While we'd like you to have very good familiarity with Python, many of our problems are stack-agnostic. • This position is not eligible for company sponsorship. • Salary Band in Canada: $114,00 - 154,000 • Salary Band in U.S.: $130,000 - $176,000 • Founded in 2017, Afresh is using AI to tackle the #1 solution to curb climate change: reducing food waste. By building AI specifically for the intricacies of grocery—from the fresh perimeter to the center store—we help grocers minimize waste and maximize sales. • Afresh sits at an incredible intersection of positive social impact, rocket ship financial growth, and cutting-edge technology. Our best-in-class AI research has been published in top journals, including ICML, and our investors include Al Gore’s Just Climate, former Whole Foods Market CEO Walter Robb, and Eric Schmidt's Innovation Endeavors. • Grocery is the past, present, and future of our food system – the waste we create today will impact our planet for years to come. Join us as we continue to build a vibrant, diverse, and inclusive team that embodies our company’s values of proactivity, kindness, candor, and humility.
Responsibilities
• In your first 3 months, you might deliver a feature that helps generalize model configuration, enables no-code model deploys for our various ML solutions, or vastly improves integration testing across our ML systems. • In your first 3 months • By the end of your first 6 months, you will have owned the implementation of significant scalability improvements and additions to our ML platform. This might include new feature pipelines that power our recommendation engine, or work to stand up the first instance of real-time inference at Afresh. • By the end of your first 6 months
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