How to apply for Staff Forward Deployed Engineer

About Afresh Technologies

Afresh builds an AI platform for the fresh food supply chain and grocery industry. Its tools help grocers like Albertsons, Meijer, and Wakefern make better decisions about fresh inventory. The company reports that its platform prevented over 200 million pounds of food waste last year and grew revenue 70% in 2025.

About the role

This is a staff forward deployed engineer role that combines field work with platform building. You will embed with enterprise grocery customers to integrate their data and ship AI systems, then turn those lessons into reusable tooling for the rest of the team. The job is split between direct customer delivery and improving the platform that speeds up future deployments.

A typical day

You might spend a morning on a call with a customer's data team, debugging an integration issue in their environment. In the afternoon, you could switch to writing code for a platform tool that makes the next customer deployment faster. The split between field work and platform work is a core part of the role.

Who Afresh Technologies is looking for

• Has experience deploying software or AI systems directly with enterprise customers, ideally in grocery, retail, or supply chain.
• Can integrate with messy, high-volatility customer data and build production systems on top of it.
• Is comfortable switching between field work and platform engineering, and can turn customer-specific solutions into general tools.
• Has worked at a staff or senior level and can operate independently across both customer and internal engineering teams.

Tips for this application

• In your resume, highlight any role where you embedded with a customer to integrate data or ship a production system. Name the customer type and the data challenges.
• Show examples of turning a one-off customer solution into a reusable tool or platform feature. This role explicitly values that loop.
• Mention any experience with high-volatility data environments, like fresh food, demand forecasting, or inventory systems.
• Reference Afresh's customers (Albertsons, Meijer, Wakefern) or the 200 million pounds of food waste prevented to show you understand the scale and domain.
• If you have worked in grocery, retail, or supply chain, make that clear. If not, explain how your data integration and deployment experience transfers to fresh food.

What to cover in your cover letter

Explain a time you were embedded with a customer and had to integrate their data to ship an AI or software system. Describe how you turned that work into something reusable for your team. Connect your experience to Afresh's focus on fresh food and high-volatility environments. State why you want a role that splits time between field deployment and platform building.

Research before applying

• Read Afresh's case studies or press releases about Albertsons, Meijer, and Wakefern to understand what the deployments involve.
• Look into the fresh food supply chain and why it is considered high-volatility compared to shelf-stable grocery.
• Check Afresh's engineering blog or public talks to see what their platform and AI stack look like.
• Find out what 'forward deployed engineer' means at other companies and how Afresh's version of the role differs.

Likely interview topics

Based on the job description, expect questions about:

• How you have integrated customer data into a production system, especially when the data was messy or high-volume.
• A specific example of turning a customer-specific solution into a reusable platform tool.
• How you handle the tension between delivering for a customer now and building for the platform later.
• Your understanding of the fresh food supply chain and why it is harder than other retail categories.
• How you would approach deploying Afresh's AI into a new enterprise grocery customer from day one.

Common mistakes to avoid

• Only talking about platform engineering or only talking about customer-facing work. This role requires both.
• Ignoring the grocery or fresh food domain. The job is specific to that industry and its data problems.
• Using generic language about 'passion for AI' without concrete examples of deploying AI into customer environments.