Application Guide
How to Apply for ML Platform Engineer
at Afresh Technologies
🏢 About Afresh Technologies
Afresh Technologies is unique because it combines cutting-edge AI with a meaningful mission: reducing food waste in the fresh food supply chain, saving millions of pounds of food annually. Working here means contributing to both technological innovation and tangible environmental impact, with a remote-first culture that values collaboration across ML and applied science teams.
About This Role
As an ML Platform Engineer at Afresh, you'll build and maintain the core infrastructure and tooling that powers all machine learning solutions, enabling teams to develop, deploy, and scale robust models efficiently. This role is impactful because you'll directly accelerate innovation across ML and applied science teams, helping reduce food waste faster through scalable, reliable platforms.
💡 A Day in the Life
A typical day involves collaborating remotely with ML and applied science teams to design and implement shared platform components, such as APIs for model deployment or tools for data pipeline management. You might troubleshoot infrastructure issues, optimize performance for scalability, and participate in code reviews to ensure high-quality, reliable systems that accelerate model development across the company.
🚀 Application Tools
🎯 Who Afresh Technologies Is Looking For
- Has 3+ years of professional software development experience with a track record of shipping high-quality services, ideally in Python or similar languages for ML infrastructure.
- Possesses deep expertise in library design, API design, and data structures, with experience creating shared components for ML workflows (e.g., model training pipelines or deployment tools).
- Has collaborated closely with machine learning engineers or data scientists on large-scale projects, understanding their pain points in model development and deployment.
- Is passionate about building scalable, reliable systems that support real-world AI applications, with an interest in sustainability or food supply chain challenges.
📝 Tips for Applying to Afresh Technologies
Highlight specific examples in your resume where you built or maintained infrastructure for ML models, emphasizing scalability and collaboration with ML teams.
Tailor your application to mention Afresh's mission—explain why reducing food waste resonates with you and how your skills align with their AI-driven approach.
Showcase projects involving API or library design for ML tooling, as this role focuses on shared components; include metrics like performance improvements or reliability gains.
If you have experience with cloud platforms (e.g., AWS, GCP) or containerization (e.g., Docker, Kubernetes), detail how you used them to support ML deployments.
Demonstrate your ability to work in a remote, collaborative environment by mentioning tools or processes you've used for cross-team coordination in software projects.
✉️ What to Emphasize in Your Cover Letter
['Explain your experience building foundational ML infrastructure, such as tooling for model training or deployment, and how it enabled teams to innovate faster.', "Connect your skills to Afresh's mission—describe why you're motivated to work on AI solutions that reduce food waste and how your background supports this goal.", 'Provide a brief example of a time you collaborated with ML engineers or data scientists on a large-scale project, highlighting your role in improving platform performance or reliability.', "Mention your expertise in library/API design and how it can help elevate Afresh's ML platform to the next level of scalability."]
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Explore Afresh's website and blog posts to understand their AI-driven platform, specific projects in food supply chain optimization, and their impact metrics on waste reduction.
- → Look into their tech stack or engineering culture through LinkedIn or Glassdoor reviews, focusing on tools they might use for ML infrastructure (e.g., cloud services, CI/CD).
- → Research the fresh food supply chain industry to grasp key challenges where ML can make a difference, such as inventory management or demand forecasting.
- → Check if Afresh has published any technical papers or case studies on their ML solutions to better understand their approach and tailor your application accordingly.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Submitting a generic application without mentioning Afresh's mission or how your skills relate to ML infrastructure for sustainability.
- Failing to provide concrete examples of collaboration with ML teams or experience in library/API design, as these are core requirements.
- Overemphasizing theoretical ML knowledge without demonstrating hands-on software development experience in building scalable services.
📅 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:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!
Ready to Apply?
Good luck with your application to Afresh Technologies!