Staff Software Engineer - ML Platform
Afresh Technologies · San Francisco, CA/Remote (US)
Afresh is on a mission to eliminate food waste and make fresh food accessible to all. Our first A.I.-powered solution optimizes ordering, forecasting, and store operations for fresh food departments in brick-and-mortar grocers. With our Fresh Operating System, regional and national grocery retailers have placed $1.6 billion in produce orders across the US and we've helped our partners prevent 34 million pounds of food from going to waste. Working at Afresh represents a one-of-a-kind opportunity to have massive social impact at scale by leveraging uncommonly impactful software – we hope you'll join us!
About the Role
The Prediction, Optimization, and Planning (POP) team builds Afresh's core replenishment technology. We built the Prediction Engine library to house the shared components that power every new solution we build: a performant data API, configurable featurization, robust and speedy forecasting, highly parallel optimization, and scalable backtesting over tens of thousands of time series. As our product suite and customer base grow, so does the scale and complexity of what this library needs to model. It needs to gracefully accommodate predictions and simulations at different 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 (average shelf fullness, backroom loads, truck capacities).
As a ML Platform Engineer - Prediction Engine on the POP team, you will take this core library to its next level of performance, reliability, and scalability. Your work will soon help power replenishment decisions on more than 15% of all produce sold in the United States. What you will do:
- In your first 3 months, you might deliver a project that helps generalize model configuration, enables no-code model deploys, or vastly improves our integration testing.
- By the end of your first 6 months, you will have owned the design and implementation of large improvements and additions to the Prediction Engine. This might be an extension to our backtesting library that allows the team to easily trigger full-scale analyses over millions of store-items on a parallel cluster, or it might be an automatic data quality monitoring module that requests human input to resolve suspicious customer data.
- By the end of your first year, you will have redesigned and extended large parts of the Prediction Engine. Some example contributions include a stress-testing framework that automatically generates difficult time series and backtests our models against them, or a generalized forecaster data model that easily accommodates warehouse, bulk, and prepared items.
Skills and Experience
The following represents attributes our ideal candidate possesses. We encourage all highly qualified candidates to apply, even if they do not fulfill all the listed criteria.
- BS in Computer Science or relevant field
- 4+ 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
- Technical leadership experience and a demonstrated ability to mentor junior engineers
- Deep expertise in library design, API design, data structures, and algorithms.
- Strong familiarity with Python
Tech Stack: Our backend is pure Python (NumPy, Pandas, Torch, PySpark, Cython, orchestrated in Airflow). We use Snowflake as our data warehouse. We’d like you to have very good familiarity with Python, but many of our problems are stack-agnostic.
Afresh is committed to pay equity and providing highly competitive cash compensation, equity, and benefits package. Afresh conducts a pay equity audit twice each year to ensure that jobs of similar scope and impact are paid similar amounts. The final compensation offered for this role will be based on multiple factors such as the role’s scope, complexity, internal equity, the candidate’s experience/expertise, and success through the interview process.
Founded in 2017, Afresh is working on the #1 solution to curb climate change: reducing food waste. By combining human insight and transformative technology, we're helping grocers provide fresher food to customers at more affordable prices.
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 we've raised over $148 million in funding from investors including former co-CEO of Whole Foods Market Walter Robb and Eric Schmidt's Innovation Endeavors.
Fresh 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.
Afresh provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity/expression, marital status, pregnancy or related condition, or any other basis protected by law.
Here at Afresh, many of our employees work remotely provided that they reside in one of the following states: AR, CA, CO, FL, GA, IL, KY, MA, MI, MT, MO, NV, NJ, NY, NC, OR, PA, TX, WA, WI. However, there may be key roles that will require a candidate/employee to be local to our San Francisco, CA office. In which case this requirement will be included in the job posting details under "Skills and experience" for reference.