About the Role:

• Build and ship high quality applications to optimize in store operations and decision making.
• Collaborate with your engineering teammates to support thousands of grocery stores critical fresh food supply chains across the US.
• Working closely with product managers, designers, and our users, deliver great product experiences across the iPad, the web front-end, API, and data layers.
• Participate in and provide meaningful feedback about major design choices.
• Build out our analytics and monitoring/observability to understand our system behavior and make informed decisions.
• Ensure the quality of our software via automated tests
• Design and build UIs, APIs, and solutions that are reusable and extensible.
• Work alongside machine learning specialists to drive new feature development.

Skills and Experience

The following represents attributes our ideal candidate possesses. We encourage all highly qualified candidates to apply, even if they do not fulfill all the listed criteria.

• BS in Computer Science or equivalent experience
• 2+ years experience with full-stack web development, including some web front-end and API development
• Experience building & maintaining production level APIs and applications
• Collaborative attitude while also being highly self-directed
• Strong problem solving skills and excitement to tackle hard problems
• Enjoy working in a fast-paced, startup environment
• Required skills: TypeScript (or Javascript), React, and Python
• Nice-to-have skills: PostgreSQL, GraphQL, Databricks, DBT, Terraform/OpenTofu, Azure, pair programming, test-driven development

This position is not eligible for company sponsorship.

Salary Range in U.S.: $156,000 - $231,00

The ML Platform Engineering team at 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 Engineer on 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 Canada (CAD): $148,000 - $242,000

The ML Platform Engineering team at 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 Engineer on 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

The ML Platform Engineering team at 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 Senior ML Platform Engineer on 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 will do:

• In your first 3 months, you might deliver a project that helps generalize model configuration, enables no-code model deploys for our various ML solutions, or vastly improves integration testing across our ML systems.
• By the end of your first 6 months, you will have owned the design and implementation of significant scalability improvements and additions to our ML platform. This might include new feature pipelines that power our recommendation engine, or work to stand up the first instance of real-time inference at Afresh.

Skills and Experience

• BS in Computer Science or a relevant technical field.
• 5+ 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 Range for Canada in CAD: $137,000 - $205,000

The ML Platform Engineering team at 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 Senior ML Platform Engineer on 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 will do:

• In your first 3 months, you might deliver a project that helps generalize model configuration, enables no-code model deploys for our various ML solutions, or vastly improves integration testing across our ML systems.
• By the end of your first 6 months, you will have owned the design and implementation of significant scalability improvements and additions to our ML platform. This might include new feature pipelines that power our recommendation engine, or work to stand up the first instance of real-time inference at Afresh.

Skills and Experience

• BS in Computer Science or a relevant technical field.
• 5+ 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 Range in U.S. $156,000 - $231,000

About the Role:

• Build and ship high quality applications to optimize in store operations and decision making.
• Collaborate with your engineering teammates to support thousands of grocery stores critical fresh food supply chains across the US.
• Working closely with product managers, designers, and our users, deliver great product experiences across the iPad, the web front-end, API, and data layers.
• Participate in and provide meaningful feedback about major design choices.
• Build out our analytics and monitoring/observability to understand our system behavior and make informed decisions.
• Ensure the quality of our software via automated tests
• Design and build UIs, APIs, and solutions that are reusable and extensible.
• Work alongside machine learning specialists to drive new feature development.

Skills and Experience

The following represents attributes our ideal candidate possesses. We encourage all highly qualified candidates to apply, even if they do not fulfill all the listed criteria.

• BS in Computer Science or equivalent experience
• 2+ years experience with full-stack web development, including some web front-end and API development
• Experience building & maintaining production level APIs and applications
• Collaborative attitude while also being highly self-directed
• Strong problem solving skills and excitement to tackle hard problems
• Enjoy working in a fast-paced, startup environment
• Required skills: TypeScript (or Javascript), React, and Python
• Nice-to-have skills: PostgreSQL, GraphQL, Databricks, DBT, Terraform/OpenTofu, Azure, pair programming, test-driven development

This position is not eligible for company sponsorship. 

Salary Range in Canada.: $136,000 - $205,000 (CAD)

About the Role

As a Senior Data Engineer, you’ll play a key role in scaling and improving how we integrate and process customer data. You will design and implement ETLs that reliably process large volumes of customer-provided data and build tools/improve the platform to make customer integrations faster, more accurate, and more scalable. You’ll also contribute to the development of new features that support our expanding product lines. Your work will have a direct and visible impact on our ability to onboard customers more easily and quickly and power our machine learning grocery solution.

What You’ll Do

• Build tools and frameworks that streamline customer integrations, enabling faster onboarding and better handling of customer data.
• Create robust ETLs in PySpark and DBT to process billions of records from customer datasets, ensuring data is accurate, reliable, and ready for downstream use.
• Investigate and implement new technologies into the data platform, focusing on practical solutions that address current pain points and anticipate future needs.
• Collaborate with product, engineering, and go-to-market teams to design and deliver data solutions for new products and features.
• Identify and implement optimizations to improve ETL runtime and data processing scalability, reducing the time and effort required for integrations.
• Solve real-world data quality challenges by working directly with messy, incomplete, or inconsistent customer data to extract the signal we need.
• Support team members by mentoring engineers, leading technical discussions, and providing clear, actionable feedback.

What Makes You a Great Fit

We encourage all highly-qualified candidates to apply, even if they don’t meet every listed qualification.

• Significant experience designing and maintaining ETLs that process large-scale datasets.
• Proficiency with Python, PySpark, SQL, and experience working on platforms/tools like Databricks, Snowflake, or DBT.
• Strong problem-solving skills and the ability to work with ambiguous or incomplete requirements to deliver concrete, impactful solutions.
• A focus on practical outcomes—you're skilled at balancing technical rigor with the need to get things done.
• Experience working directly with complex, unclean datasets and finding innovative ways to process and analyze them.
• A knack for identifying areas where tooling or automation can simplify workflows and reduce manual effort.
• Excellent communication skills—you’re able to explain your ideas clearly to both technical and non-technical audiences.
• Proven leadership in technical projects, with a willingness to mentor and help others grow.

Why Work Here

• Join a mission-driven company reducing millions of pounds of food waste in grocery stores per year.
• Work on challenging, real-world problems that have a direct impact on our customers.
• Be part of a collaborative, supportive team where your ideas are valued and acted on.
• Use cutting-edge tools and platforms to solve meaningful data challenges.

We’re looking for someone who thrives on tackling complex data problems and takes pride in building systems that work seamlessly at scale. If that sounds like you, we’d love to hear from you!

This position is not eligible for immigration sponsorship

Salary Range in Canada (CAD): $137,000 - $205,000

About the Role

As a Senior Data Engineer, you’ll play a key role in scaling and improving how we integrate and process customer data. You will design and implement ETLs that reliably process large volumes of customer-provided data and build tools/improve the platform to make customer integrations faster, more accurate, and more scalable. You’ll also contribute to the development of new features that support our expanding product lines. Your work will have a direct and visible impact on our ability to onboard customers more easily and quickly and power our machine learning grocery solution.

What You’ll Do

• Build tools and frameworks that streamline customer integrations, enabling faster onboarding and better handling of customer data.
• Create robust ETLs in PySpark and DBT to process billions of records from customer datasets, ensuring data is accurate, reliable, and ready for downstream use.
• Investigate and implement new technologies into the data platform, focusing on practical solutions that address current pain points and anticipate future needs.
• Collaborate with product, engineering, and go-to-market teams to design and deliver data solutions for new products and features.
• Identify and implement optimizations to improve ETL runtime and data processing scalability, reducing the time and effort required for integrations.
• Solve real-world data quality challenges by working directly with messy, incomplete, or inconsistent customer data to extract the signal we need.
• Support team members by mentoring engineers, leading technical discussions, and providing clear, actionable feedback.

What Makes You a Great Fit

We encourage all highly-qualified candidates to apply, even if they don’t meet every listed qualification.

• Significant experience designing and maintaining ETLs that process large-scale datasets.
• Proficiency with Python, PySpark, SQL, and experience working on platforms/tools like Databricks, Snowflake, or DBT.
• Strong problem-solving skills and the ability to work with ambiguous or incomplete requirements to deliver concrete, impactful solutions.
• A focus on practical outcomes—you're skilled at balancing technical rigor with the need to get things done.
• Experience working directly with complex, unclean datasets and finding innovative ways to process and analyze them.
• A knack for identifying areas where tooling or automation can simplify workflows and reduce manual effort.
• Excellent communication skills—you’re able to explain your ideas clearly to both technical and non-technical audiences.
• Proven leadership in technical projects, with a willingness to mentor and help others grow.

Why Work Here

• Join a mission-driven company reducing millions of pounds of food waste in grocery stores per year.
• Work on challenging, real-world problems that have a direct impact on our customers.
• Be part of a collaborative, supportive team where your ideas are valued and acted on.
• Use cutting-edge tools and platforms to solve meaningful data challenges.

We’re looking for someone who thrives on tackling complex data problems and takes pride in building systems that work seamlessly at scale. If that sounds like you, we’d love to hear from you!

This position is not eligible for immigration sponsorship

Salary Range in U.S.: $156,000 - $231,000

About the Role

As the Data Engineering Manager on the Core Data Engineering team, you'll lead the team responsible for integrating customers onto Afresh's platform and continuously improving the processes and code that make those integrations faster and more reliable. Your team will also build core dataflows that power new Afresh products as they launch.

This means owning the full lifecycle of customer data integrations: from raw data ingestion through transformation and delivery to downstream product and ML teams, across both new and existing grocery customers. Afresh's customer base is growing fast! We want to integrate customers across all of our products quickly and reliably. You'll drive integration execution, integration velocity, improve data quality and pipeline reliability, and build tooling and processes that make this process seamless.

This is a hands-on management role. You'll be deeply involved in technical decisions while also coaching your team, managing execution across multiple customer projects, and collaborating closely with Solutions Engineering, Product, and other engineering teams.

What You'll Do

• Lead and grow a team of data engineers, providing mentorship, technical guidance, and career development support.
• Own execution of customer integrations across multiple product lines (Store Ordering, RecsAPI, DC, Production Planning, and more), ensuring integrations are delivered on time and at a high quality bar.
• Build core dataflows that power new Afresh products, working closely with Product and ML teams to understand requirements and deliver reliable data foundations.
• Drive integration velocity: improve the speed and predictability of getting new customers from raw data landing to production-ready, leveraging AI tooling, standardized architecture, and strong contractor enablement.
• Continuously improve integration processes and shared code: identify patterns across customer integrations and invest in abstractions, templates, and automation that reduce per-customer effort over time.
• Collaborate deeply with Solutions Engineers, Product Managers, and customer-facing teams to scope integration work, unblock dependencies, communicate progress, and ensure integrations meet our customers’ needs.
• Improve data quality and pipeline reliability by investing in better alerting, self-healing patterns, and resilience to messy or incomplete customer data.
• Champion AI-forward engineering practices: evaluate and adopt AI tools and agentic workflows that accelerate development, automate repetitive tasks, and push the team to the bleeding edge of modern data engineering.
• Contribute technically: reviewing code and architecture decisions, pairing with engineers on complex problems, and staying close to the work.

What Makes You a Great Fit

We encourage all highly-qualified candidates to apply, even if they don't meet every listed qualification.

• 3+ years of engineering management experience
• Strong technical background in data engineering: experience with Python, PySpark, SQL, dbt, Airflow, and modern data platforms (Databricks, Snowflake, or similar).
• Track record of shipping high-quality data integrations or ETL systems at scale, and a deep understanding of what makes data pipelines reliable.
• Experience managing execution across multiple concurrent projects with different customers or stakeholders, balancing competing priorities with clear communication.
• Demonstrated ability to improve team velocity through better tooling, process improvements, or automation.
• Genuine enthusiasm for AI-augmented engineering workflows. You've experimented with AI coding tools, agentic workflows, or similar, and have a vision for how they can transform data engineering and uplevel the team.
• Comfort working with messy, real-world data from enterprise customers, and the pragmatism to ship solutions that work without over-engineering.
• Strong collaboration skills: you're effective working across engineering, product, and customer-facing teams in a fast-paced environment.

Nice to Have

• Experience in grocery, retail, or supply chain data domains.
• Prior experience at a high-growth startup navigating rapid customer expansion.

Our Tech Stack

• Python, PySpark, dbt
• Databricks (Delta Lake, Unity Catalog)
• Astronomer (Airflow) for orchestration
• Claude, GitHub, Shortcut, Notion for development workflows

Why Work Here

• We are a mission-driven company that reduces millions of pounds of food waste (and growing!) in grocery stores every year.
• Work on challenging, real-world data problems that have a direct and visible impact on customers.
• Be part of an engineering culture that's genuinely AI-forward. We want to be on the bleeding edge.
• Lead a team at a pivotal inflection point: rapid customer growth means your work directly shapes how Afresh scales.
• Collaborative, supportive environment & awesome people :)

This position is not eligible for company sponsorship.

Salary Range in Canada (CAD): $157,433 - $232,996

About the Role

As the Data Engineering Manager on the Core Data Engineering team, you'll lead the team responsible for integrating customers onto Afresh's platform and continuously improving the processes and code that make those integrations faster and more reliable. Your team will also build core dataflows that power new Afresh products as they launch.

This means owning the full lifecycle of customer data integrations: from raw data ingestion through transformation and delivery to downstream product and ML teams, across both new and existing grocery customers. Afresh's customer base is growing fast! We want to integrate customers across all of our products quickly and reliably. You'll drive integration execution, integration velocity, improve data quality and pipeline reliability, and build tooling and processes that make this process seamless.

This is a hands-on management role. You'll be deeply involved in technical decisions while also coaching your team, managing execution across multiple customer projects, and collaborating closely with Solutions Engineering, Product, and other engineering teams.

What You'll Do

• Lead and grow a team of data engineers, providing mentorship, technical guidance, and career development support.
• Own execution of customer integrations across multiple product lines (Store Ordering, RecsAPI, DC, Production Planning, and more), ensuring integrations are delivered on time and at a high quality bar.
• Build core dataflows that power new Afresh products, working closely with Product and ML teams to understand requirements and deliver reliable data foundations.
• Drive integration velocity: improve the speed and predictability of getting new customers from raw data landing to production-ready, leveraging AI tooling, standardized architecture, and strong contractor enablement.
• Continuously improve integration processes and shared code: identify patterns across customer integrations and invest in abstractions, templates, and automation that reduce per-customer effort over time.
• Collaborate deeply with Solutions Engineers, Product Managers, and customer-facing teams to scope integration work, unblock dependencies, communicate progress, and ensure integrations meet our customers’ needs.
• Improve data quality and pipeline reliability by investing in better alerting, self-healing patterns, and resilience to messy or incomplete customer data.
• Champion AI-forward engineering practices: evaluate and adopt AI tools and agentic workflows that accelerate development, automate repetitive tasks, and push the team to the bleeding edge of modern data engineering.
• Contribute technically: reviewing code and architecture decisions, pairing with engineers on complex problems, and staying close to the work.

What Makes You a Great Fit

We encourage all highly-qualified candidates to apply, even if they don't meet every listed qualification.

• 3+ years of engineering management experience
• Strong technical background in data engineering: experience with Python, PySpark, SQL, dbt, Airflow, and modern data platforms (Databricks, Snowflake, or similar).
• Track record of shipping high-quality data integrations or ETL systems at scale, and a deep understanding of what makes data pipelines reliable.
• Experience managing execution across multiple concurrent projects with different customers or stakeholders, balancing competing priorities with clear communication.
• Demonstrated ability to improve team velocity through better tooling, process improvements, or automation.
• Genuine enthusiasm for AI-augmented engineering workflows. You've experimented with AI coding tools, agentic workflows, or similar approaches, and you have a vision for how they can transform data engineering and uplevel the team.
• Comfort working with messy, real-world data from enterprise customers, and the pragmatism to ship solutions that work without over-engineering.
• Strong collaboration skills: you're effective working across engineering, product, and customer-facing teams in a fast-paced environment.

Nice to Have

• Experience in grocery, retail, or supply chain data domains.
• Prior experience at a high-growth startup navigating rapid customer expansion.

Our Tech Stack

• Python, PySpark, dbt
• Databricks (Delta Lake, Unity Catalog)
• Astronomer (Airflow) for orchestration
• Claude, GitHub, Shortcut, Notion for development workflows

Why Work Here

• We are a mission-driven company that reduces millions of pounds of food waste (and growing!) in grocery stores every year.
• Work on challenging, real-world data problems that have a direct and visible impact on customers.
• Be part of an engineering culture that's genuinely AI-forward. We want to be on the bleeding edge.
• Lead a team at a pivotal inflection point: rapid customer growth means your work directly shapes how Afresh scales.
• Collaborative, supportive environment & awesome people :)

This position is not eligible for company sponsorship.

Salary Range in U.S.: $ 179,469 - $262,811

The ML Platform Engineering team at 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 an ML Platform Engineer on 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 might deliver a feature that helps generalize model configuration, enables no-code model deploys for our various ML solutions, or vastly improves integration testing across our ML systems.
• By the end of your first 6 months, you will have owned the implementation of significant scalability improvements and additions to our ML platform. This might include new feature pipelines that power our recommendation engine, or work to stand up the first instance of real-time inference at Afresh.

Skills and Experience

• BS in Computer Science or a relevant technical field.
• 3+ 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.
• Deep expertise in library design, API design, data structures, and algorithms.
• Strong familiarity with Python.

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 Canada: $114,00 - 154,000

Salary Band in U.S.: $130,000 - $176,000