Application Guide

How to Apply for Staff Software Engineer, ML Platform

at Afresh Technologies

🏢 About Afresh Technologies

Afresh is the leading AI company in fresh food, partnering with major grocers to reduce food waste and make fresh food accessible. With record-breaking growth and a mission-driven culture, it offers a unique opportunity to work on impactful technology that directly contributes to sustainability and social good.

About This Role

As a Staff Software Engineer on the ML Platform team, you will build and maintain the foundational infrastructure powering all ML solutions at Afresh, including data APIs, featurization, forecasting systems, and optimization engines. Your work will directly enable the deployment and scaling of models that help reduce millions of pounds of food waste annually.

💡 A Day in the Life

You'll start the day with a standup to sync with your team on platform improvements and support requests. Much of your time will be spent designing and coding new infrastructure components (e.g., a feature pipeline or model serving API), reviewing peers' code, and collaborating with data scientists to understand their needs. You'll also monitor system performance, triage production issues, and participate in on-call rotations.

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Interview Prep

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🎯 Who Afresh Technologies Is Looking For

  • Has 7+ years of software engineering experience with a focus on building ML platforms or infrastructure, including experience with scalable data pipelines, distributed systems, and MLOps.
  • Proficient in Python and at least one compiled language (e.g., C++, Go, Java), with deep knowledge of cloud platforms (AWS, GCP) and containerization (Docker, Kubernetes).
  • Experienced in designing and maintaining high-availability APIs and services for ML model training, deployment, and monitoring.
  • Passionate about sustainability and eager to work on a platform that directly reduces food waste and improves access to fresh food.

📝 Tips for Applying to Afresh Technologies

1

Highlight specific projects where you built or improved ML infrastructure (e.g., feature stores, model serving, or training pipelines) and quantify impact (e.g., latency reduction, throughput increase).

2

Mention experience with real-time data streaming (e.g., Kafka, Kinesis) and large-scale batch processing (Spark, Airflow) as these are likely used for fresh food demand forecasting.

3

Emphasize cross-team collaboration skills, as the role involves working with data scientists and product teams to define platform requirements.

4

Tailor your resume to include keywords like 'ML platform', 'MLOps', 'feature engineering', 'model deployment', and 'scalable infrastructure'.

5

Showcase your understanding of the fresh food supply chain or similar domains (e.g., retail, logistics) to demonstrate domain awareness.

✉️ What to Emphasize in Your Cover Letter

["Express passion for Afresh's mission to eliminate food waste and make fresh food accessible, and connect your technical skills to that mission.", 'Describe a specific example of building an ML platform component that improved model development velocity or reliability.', 'Explain your experience with scalable infrastructure and how it can help Afresh handle growth across 12,000+ grocery departments.', 'Mention your interest in working remotely and your ability to collaborate effectively across time zones.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read Afresh's blog or news articles about their partnerships with Albertsons, Wakefern, etc., to understand their product impact.
  • → Explore their tech stack (likely Python, AWS, Kubernetes) by looking at public engineering talks or job descriptions.
  • → Understand the fresh food supply chain challenges (e.g., perishability, demand variability) to speak to domain relevance.
  • → Check Afresh's Crunchbase or LinkedIn for recent funding and growth metrics to gauge company trajectory.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Design a feature store for fresh food demand forecasting, considering data freshness, latency, and scale.
2 How would you architect a system to serve predictions for millions of SKU-store combinations daily?
3 Describe a time you improved the reliability of an ML training pipeline; what metrics did you use?
4 Explain your experience with A/B testing or experimentation platforms for ML models.
5 How do you handle trade-offs between model accuracy and inference speed in a production system?
Practice Interview Questions →

⚠️ Common Mistakes to Avoid

  • Submitting a generic cover letter that doesn't mention Afresh's mission or specific technology challenges.
  • Focusing only on ML modeling experience without emphasizing infrastructure and platform engineering skills.
  • Ignoring the remote aspect: failing to demonstrate self-motivation and async communication skills.

📅 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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