Staff ML Platform Engineer

TheML Platform Engineering teamat 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).

About the Role

As a Staff ML Platform Engineeron 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.

What You'll Do:

• In your first 3 months, you'll partner with ML, Applied Science, and engineering leadership to identify the highest-leverage gaps in our ML platform and shape a multi-quarter technical strategy to close them.
• By the end of your first 6 months, you will have driven a platform-level initiative that meaningfully changes what's possible at Afresh — establishing the architecture for real-time inference across the company, redesigning model configuration and deployment end-to-end, or rebuilding our distributed inference layer for an order-of-magnitude growth in scale — while raising the bar across the ML org through design reviews and mentorship of other senior engineers.

Skills and Experience

• BS in Computer Science or a relevant technical field.
• 7+ 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.
• You possess a genuine curiosity about ML modeling (e.g., demand forecasting, state estimation, ordering policy). You aren't just building "pipes"; you want to understand what is flowing through them.
• You have a deep understanding of how scientists work and build tools that bridge the gap between a research notebook and production-grade software.
• 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 the Python ecosystem (NumPy, Pandas, Torch, PySpark). While our stack is Python-heavy, we value engineers who are stack-agnostic and focus on solving fundamental distributed systems problems.
• Proven ability to architect high-throughput distributed inference systems using Spark, Dask, or Ray.
• Experience in engineering robust data architectures with a focus on schema evolution and performance tuning.
• Experience with end-to-end orchestration (lineage tracking, resource scheduling, self-healing pipelines).

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 U.S. (USD): $169,000 - $273,000