Application Guide

How to Apply for Senior Software Engineer, ML Platform

at Afresh Technologies

🏢 About Afresh Technologies

Afresh Technologies is a mission-driven AI company tackling food waste in the fresh food supply chain, partnering with major grocers like Albertsons and Meijer. With 70% growth in 2025 and over 200M lbs of food waste saved, it offers a rare blend of high-impact social good and cutting-edge ML engineering at scale.

About This Role

As a Senior Software Engineer on the ML Platform team, you'll build and maintain the foundational infrastructure that powers all ML and applied science solutions at Afresh—including data APIs, featurization, forecasting systems, optimization engines, and training pipelines. Your work directly enables the company's core product to scale across thousands of grocery departments, reducing food waste and making fresh food accessible.

💡 A Day in the Life

Your day might start with a standup discussing ongoing work on the feature store or forecasting pipeline, then diving into coding a new data API endpoint or optimizing a training job. After lunch, you'll review a design doc for a new platform capability, and later pair with a data scientist to debug a model serving issue, ensuring predictions are delivered reliably across thousands of stores.

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Interview Prep

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🎯 Who Afresh Technologies Is Looking For

  • Proven experience building and scaling ML platforms or infrastructure, with strong software engineering skills in Python and distributed systems.
  • Deep understanding of ML lifecycle tools (e.g., feature stores, model serving, CI/CD for ML) and experience with cloud platforms (AWS/GCP) and containerization (Docker/Kubernetes).
  • Ability to design performant data APIs and optimize for high-throughput, low-latency predictions across a growing product suite.
  • Passion for social impact and experience collaborating with cross-functional teams (data scientists, product managers) to deliver reliable, scalable ML systems.

📝 Tips for Applying to Afresh Technologies

1

Highlight specific projects where you built or improved ML platform components (e.g., feature engineering pipelines, model deployment frameworks) and quantify impact (e.g., latency reduction, throughput increase).

2

Showcase your experience with distributed systems and handling scale—mention any work with real-time data pipelines, parallel optimization, or high-volume forecasting.

3

Tailor your resume to include keywords like 'ML platform', 'feature store', 'model serving', 'Kubernetes', and 'Python' to align with the job description.

4

In your cover letter, explicitly connect your previous work to Afresh's mission of reducing food waste—show you understand the domain and care about the impact.

5

Prepare to discuss trade-offs in ML platform design (e.g., standardization vs. flexibility, batch vs. real-time) and how you've navigated them.

✉️ What to Emphasize in Your Cover Letter

['Emphasize your experience building scalable ML infrastructure and how it directly supports data science teams in production.', "Demonstrate understanding of Afresh's unique position in fresh food and your alignment with their mission to eliminate food waste.", "Mention specific technical challenges you've solved (e.g., optimizing feature computation, reducing model deployment time) that are relevant to ML platform work.", 'Show enthusiasm for working in a fast-growing company where your contributions have tangible social and environmental impact.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read Afresh's blog posts or case studies about their ML platform and how they scale predictions across departments.
  • → Understand the fresh food supply chain challenges—perishability, demand variability, and multi-echelon inventory—to connect your work to business impact.
  • → Look at their engineering blog or GitHub to see their tech stack (likely Python, AWS, Kubernetes, and maybe Ray or Airflow).
  • → Check recent news about their growth (70% growth in 2025, partnerships with major grocers) to understand the scale and urgency.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Design a feature store for a multi-department grocery forecasting system—how would you handle freshness and perishability features?
2 How would you architect a scalable prediction service that can handle both real-time and batch requests across thousands of stores?
3 Describe a time you improved the reliability or performance of an ML pipeline; what metrics did you use and what was the outcome?
4 How do you balance standardization (shared platform components) with team autonomy for specialized ML needs?
5 Explain how you would debug a gradual degradation in model inference latency in a distributed system.
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⚠️ Common Mistakes to Avoid

  • Focusing only on ML model building without demonstrating platform/infrastructure skills—this role is about enabling ML, not modeling.
  • Submitting a generic application that doesn't mention food waste or Afresh's mission—passion for the problem is key.
  • Overlooking the importance of reliability and scalability; avoid downplaying production challenges or operational experience.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Afresh Technologies!

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