Software Engineer, Afresh
ML Platform
$130-176k
Salary Band in Canada: $114,00 - 154,000
More information about location
SaaS for grocery store operations
Open for applications
SaaS for grocery store operations
101-200 employees
Open for applications
$130-176k
Salary Band in Canada: $114,00 - 154,000
More information about location
101-200 employees
Company mission
To eliminate food waste and make fresh food accessible to all.
Role
Who you are
- 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
What the job involves
- The ML Platform Engineering team at Afresh is responsible for building and maintaining the foundational infrastructure and tooling that powers all of our machine learning and applied science solutions
- We provide the shared components and services that enable our teams to develop, deploy, and scale robust ML models
- This includes a performant data API, configurable featurization, reliable forecasting systems, highly parallel optimization engines, and scalable training pipelines, and deep experimentation capabilities
- As our product suite and customer base grow, so does the scale and complexity of what our platform needs to support;
- Gracefully accommodating predictions and simulations across various time scales (hours, days, weeks), complex data hierarchies (pallets on a truck, shelves of mangos in a store, chunks of fruit in a bowl), and endless configuration possibilities (average shelf fullness, backroom loads, truck capacities)
- As an ML Platform Engineer on the ML Platform Engineering team, you will be instrumental in elevating our core ML platform to its next level of performance, reliability, and scalability
- You'll work on the critical infrastructure that directly enables all of Afresh's Machine Learning and Applied Science teams to innovate faster and deliver impact
- Your contributions will empower our product suite, including our flagship Prediction Engine, to power replenishment decisions on more than 15% of all produce sold in the United States
- 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
- 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
- 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
Our take
Outdated inventory systems remain a major issue for grocers, particularly when it comes to fresh food. Many still rely on manual processes or rigid software that struggles to account for constantly changing factors like demand, shelf life and supply fluctuations. The result is over-ordering, wasted food and unnecessary costs, all problems that become even harder to manage at scale.
Afresh is tackling this with an AI-powered platform built specifically for fresh food operations. Its system helps grocers forecast demand, manage inventory and make smarter ordering decisions across categories like produce, meat and bakery. What started as a replenishment tool has expanded into a broader operating system, supporting planning, buying and day-to-day decision-making across store teams.
The company has grown quickly too, now supporting more than 12,000 grocery departments across North American and partnering with major retailers like Albertsons, Meijer and Wakefern. Following a year of strong growth and new product launches, Afresh is expanding its reach across the food supply chain, with ambitions that go beyond the store level. As pressure mounts on grocers to reduce waste and improve margins, Afresh is ready to be a key player in bringing AI-driven decision-making to the entire fresh food ecosystem.
Freddie
Company Specialist at Welcome to the Jungle
Insights
6% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Aug 2022
$115m
SERIES B
Nov 2020
$13m
SERIES A
Total funding: $147.8m
Company benefits
- Comprehensive health plans
- Generous parental leave
- Equity packages
- 401k matching
- Flexible vacation policy
- Professional development program
- Work from home stipend
Company HQ
Yerba Buena, San Francisco, CA
Leadership
Matt Schwartz
(Co-Founder & CEO)Prior to co-founding Afresh in 2016, Matt worked as a Consultant for Bain & Company.
Nathan Fenner
(Co-Founder & CPO)Nathan has a Bachelors, Masters and MBA from Stanford University. Post studying, they became a Lecturer at Stanford in the Mechanical Engineering department before working at Liquid Robotics for 5 years.
Kristina Kemmer
(CTO)Kristina joined Afresh in 2023 as a Senior VP of Engineering, moving into the role of CTO in 2025. Prior to Afresh, she spent 4 years at Zapier as a Director/VP of Engineering and 2 years at InVision as an Engineering, then Senior Engineering Manager.
Adam Litle
(CRO)Prior to joining Afresh in 2025, Adam was the CEO of Sound Agriculture for over 5 years. He also served as the CRO at Granular for nearly 3 years and worked as a VP of Sales at Monetate for 1 year.
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