About Us

We’re looking for a Staff Engineer -  User Experience (Backend) to join our Patient Platform team and shape the systems that power patient-facing experiences across our health technology platform. This team owns the APIs, orchestration services, and communication infrastructure that enable secure, reliable interactions between patients and our platform (Email, SMS, notifications).

We are at a critical stage of growth, scaling our platform, modernizing legacy services, and strengthening our event-driven architecture to support increasing patient volume and communication complexity. This role will play a central part in defining how our backend systems evolve as we scale.

As a Staff Engineer -  User Experience (Backend), you will be the technical authority for the Patient Platform domain, translating architectural strategy into scalable, production-ready distributed systems in partnership with cross-functional teams. You will drive the evolution of our API-first architecture (including BFF and orchestration patterns), define and enforce API contracts, and modernize tightly coupled services into scalable, event-driven components. 

This is an opportunity for a senior technical leader who thrives in ambiguity, moves pragmatically, and enjoys balancing hands-on engineering with architectural influence.

You will strengthen reliability, observability, and fault tolerance across distributed systems, scale and harden our patient communication platform (Email, SMS, notifications), and design secure systems handling sensitive patient and identity data in regulated environments. Additionally, you will champion best practices in automated testing, CI/CD, and secure development while elevating engineering standards through mentorship, design reviews, and technical leadership.

We have a strong preference for candidates based in Vancouver, BC who are able to work in a hybrid capacity. However, we are open to remote candidates across Canada.

Help reshape the world through proactive healthcare while working with cutting-edge technology and high performing teams with deep expertise - join us to make a difference in people’s lives! 

What You’ll Do

• Lead design and modernization of our patient-facing backend services
• Drive API design best practices, including BFF and orchestration patterns
• Define and enforce API contracts and cross-service communication standards
• Design and implement event-driven workflows using AWS EventBridge and async messaging
• Improve reliability and fault tolerance of communication pipelines (email, SMS, notifications)
• Ensure secure authentication and authorization is consistently implemented across APIs
• Untangle tech debt and make high-leverage decisions that improve engineering velocity
• Design secure, scalable systems that handle sensitive patient and identity data
• Mentor engineers across the team and elevate system design standards
• Collaborate cross-functionally with Product, Architecture, Security, QA, Clinical, and Frontend teams
• Communicate complex technical concepts clearly to cross-functional stakeholders and leadership, influencing architectural direction and product decisions
• Advocate for improvements in developer experience, testing practices, and system observability

What You’ll Bring

• 10+ years of backend engineering experience, including platform or high-scale distributed systems
• Strong experience in Python (FastAPI, Flask) or similar modern backend frameworks
• Deep expertise designing event-driven architectures in AWS (EventBridge, SQS/SNS, Lambda, ECS)
• Experience building and operating async task processing systems (e.g., Celery or similar)
• Proven ability to define and govern REST and/or GraphQL APIs at scale
• Strong understanding of service boundaries, microservices, and async service-to-service communication
• Experience designing highly observable systems (metrics, tracing, alerting — e.g., Datadog or similar)
• Experience implementing secure authentication and authorization (OAuth2, JWT, service-to-service security)
• Experience working with Postgres, DynamoDB, and distributed caching systems (e.g., Redis)
• Familiarity with Infrastructure as Code (Terraform/CDK) and mature CI/CD practices
  Experience designing systems in regulated environments handling sensitive data (HIPAA, PHI/PII)
• Excellent technical communication skills and demonstrated cross-team architectural influence
• Ability to operate effectively in ambiguity and drive clarity across teams

Nice to Have

• Experience building or scaling communication platforms (email delivery systems, SMS gateways, notification orchestration)
• Familiarity with BFF patterns supporting React or modern frontend applications
• Hands-on experience with contract testing frameworks (e.g., Pact)
• Experience navigating the challenges of a startup transitioning into a scale-up
• Experience with healthcare or regulated environments (HIPAA, PHI/PII)
• Familiarity with security and privacy standards (e.g., HIPAA, ISO 27001, ISO 13485)

Our Values 

First: we are Pioneers

• Transforming healthcare requires divergent thinking, bias for action, disciplined experimentation, and consistent grit and determination to maintain momentum. This journey is as challenging as it is rewarding.

Second: we are Platform-Builders

• We’re always building foundations that allow us to achieve tomorrow more than we did today. We never lose sight of what’s ahead – in a mindset of ownership and duty to our mission.

Above all: we are Patients

• We could all be the next person who walks through our very doors, seeking clarity or peace of mind. We are proud of our impact on our patients’ lives, and we won’t stop till everyone can benefit from our work.

What We Offer

• An avenue to make a positive impact on people's lives and their health
• We believe in preventative healthcare for everyone, including our team - Prenuvo provides free, whole-body scans to each team member 
• Growth opportunities are at the heart of our people journey, we’re doing big things with bright minds - there is no single path to success, it can be shaped along the way
• Building strong relationships is at the core of everything we do - our team gets together each week to connect, share, and socialize 
• Recognizing time away to restore is vital to our wellbeing - we have a flexible vacation policy and we will encourage you to use it
• We offer a comprehensive benefits package including health, dental, vision, including Mental Health coverage, to support you and your family
• Prenuvo offers eligible Canadian employees access to both RRSP and TFSA retirement savings plans, with immediate, optional enrollment and the flexibility to invest and save for the future on your own terms. While Prenuvo provides access to these plans, the company does not currently offer matching contributions.
• The base salary for this role ranges from $180,000- $210,000 in local currency, depending upon experience and geographic location

We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

As part of the hiring process, successful candidates will undergo a background check in compliance to applicable federal, provincial, and state rules.

Please be advised that official communication from our recruitment team will only come from our authorized domain [prenuvo.com]. If you are contacted by a recruiter, please ensure their email address ends with @prenuvo.com. We do not use third-party recruitment services or any other email domains for hiring purposes. If you receive communication that you believe to be fraudulent, please report it immediately to jobs@prenuvo.com.

About Us

We’re looking for a Staff Engineer -  User Experience (Backend) to join our Patient Platform team and shape the systems that power patient-facing experiences across our health technology platform. This team owns the APIs, orchestration services, and communication infrastructure that enable secure, reliable interactions between patients and our platform (Email, SMS, notifications).

We are at a critical stage of growth, scaling our platform, modernizing legacy services, and strengthening our event-driven architecture to support increasing patient volume and communication complexity. This role will play a central part in defining how our backend systems evolve as we scale.

As a Staff Engineer -  User Experience (Backend), you will be the technical authority for the Patient Platform domain, translating architectural strategy into scalable, production-ready distributed systems in partnership with cross-functional teams. You will drive the evolution of our API-first architecture (including BFF and orchestration patterns), define and enforce API contracts, and modernize tightly coupled services into scalable, event-driven components. 

This is an opportunity for a senior technical leader who thrives in ambiguity, moves pragmatically, and enjoys balancing hands-on engineering with architectural influence.

You will strengthen reliability, observability, and fault tolerance across distributed systems, scale and harden our patient communication platform (Email, SMS, notifications), and design secure systems handling sensitive patient and identity data in regulated environments. Additionally, you will champion best practices in automated testing, CI/CD, and secure development while elevating engineering standards through mentorship, design reviews, and technical leadership.

This is a fully remote role, open to candidates based in Ontario, with flexibility to collaborate across Pacific and Eastern time zones.

Help reshape the world through proactive healthcare while working with cutting-edge technology and high performing teams with deep expertise - join us to make a difference in people’s lives! 

What You’ll Do

• Lead design and modernization of our patient-facing backend services
• Drive API design best practices, including BFF and orchestration patterns
• Define and enforce API contracts and cross-service communication standards
• Design and implement event-driven workflows using AWS EventBridge and async messaging
• Improve reliability and fault tolerance of communication pipelines (email, SMS, notifications)
• Ensure secure authentication and authorization is consistently implemented across APIs
• Untangle tech debt and make high-leverage decisions that improve engineering velocity
• Design secure, scalable systems that handle sensitive patient and identity data
• Mentor engineers across the team and elevate system design standards
• Collaborate cross-functionally with Product, Architecture, Security, QA, Clinical, and Frontend teams
• Communicate complex technical concepts clearly to cross-functional stakeholders and leadership, influencing architectural direction and product decisions
• Advocate for improvements in developer experience, testing practices, and system observability

What You’ll Bring

• 10+ years of backend engineering experience, including platform or high-scale distributed systems
• Strong experience in Python (FastAPI, Flask) or similar modern backend frameworks
• Deep expertise designing event-driven architectures in AWS (EventBridge, SQS/SNS, Lambda, ECS)
• Experience building and operating async task processing systems (e.g., Celery or similar)
• Proven ability to define and govern REST and/or GraphQL APIs at scale
• Strong understanding of service boundaries, microservices, and async service-to-service communication
• Experience designing highly observable systems (metrics, tracing, alerting — e.g., Datadog or similar)
• Experience implementing secure authentication and authorization (OAuth2, JWT, service-to-service security)
• Experience working with Postgres, DynamoDB, and distributed caching systems (e.g., Redis)
• Familiarity with Infrastructure as Code (Terraform/CDK) and mature CI/CD practices
  Experience designing systems in regulated environments handling sensitive data (HIPAA, PHI/PII)
• Excellent technical communication skills and demonstrated cross-team architectural influence
• Ability to operate effectively in ambiguity and drive clarity across teams

Nice to Have

• Experience building or scaling communication platforms (email delivery systems, SMS gateways, notification orchestration)
• Familiarity with BFF patterns supporting React or modern frontend applications
• Hands-on experience with contract testing frameworks (e.g., Pact)
• Experience navigating the challenges of a startup transitioning into a scale-up
• Experience with healthcare or regulated environments (HIPAA, PHI/PII)
• Familiarity with security and privacy standards (e.g., HIPAA, ISO 27001, ISO 13485)

Our Values 

First: we are Pioneers

• Transforming healthcare requires divergent thinking, bias for action, disciplined experimentation, and consistent grit and determination to maintain momentum. This journey is as challenging as it is rewarding.

Second: we are Platform-Builders

• We’re always building foundations that allow us to achieve tomorrow more than we did today. We never lose sight of what’s ahead – in a mindset of ownership and duty to our mission.

Above all: we are Patients

• We could all be the next person who walks through our very doors, seeking clarity or peace of mind. We are proud of our impact on our patients’ lives, and we won’t stop till everyone can benefit from our work.

What We Offer

• An avenue to make a positive impact on people's lives and their health
• We believe in preventative healthcare for everyone, including our team - Prenuvo provides free, whole-body scans to each team member 
• Growth opportunities are at the heart of our people journey, we’re doing big things with bright minds - there is no single path to success, it can be shaped along the way
• Building strong relationships is at the core of everything we do - our team gets together each week to connect, share, and socialize 
• Recognizing time away to restore is vital to our wellbeing - we have a flexible vacation policy and we will encourage you to use it
• We offer a comprehensive benefits package including health, dental, vision, including Mental Health coverage, to support you and your family
• Prenuvo offers eligible Canadian employees access to both RRSP and TFSA retirement savings plans, with immediate, optional enrollment and the flexibility to invest and save for the future on your own terms. While Prenuvo provides access to these plans, the company does not currently offer matching contributions.
• The base salary for this role ranges from $180,000- $210,000 in local currency, depending upon experience and geographic location

We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

As part of the hiring process, successful candidates will undergo a background check in compliance to applicable federal, provincial, and state rules.

Please be advised that official communication from our recruitment team will only come from our authorized domain [prenuvo.com]. If you are contacted by a recruiter, please ensure their email address ends with @prenuvo.com. We do not use third-party recruitment services or any other email domains for hiring purposes. If you receive communication that you believe to be fraudulent, please report it immediately to jobs@prenuvo.com.

About this position:

We’re looking for a Machine Learning Infrastructure Engineer to join our growing Data & Platform team and build the foundation that powers our AI and machine learning capabilities across Later’s product portfolio. As our first dedicated ML Infrastructure Engineer, you will own the systems that support model experimentation, training, deployment, and monitoring at scale.

This role is critical to accelerating our data science initiatives and enabling future AI innovation. You’ll design and operate reliable, secure, and scalable ML infrastructure that empowers data scientists and engineers to ship high-impact models with confidence. If you’re excited about building robust ML systems in a fast-moving environment—and want to define the standard for ML Ops at Later—this is your opportunity.

What you'll be doing:

Strategy

• Define and own the long-term ML infrastructure roadmap, ensuring it supports both current experimentation needs and future AI initiatives.
• Establish best practices for model lifecycle management, deployment standards, monitoring, and governance. 
• Identify infrastructure gaps and proactively design scalable solutions to enable high-velocity ML development.
• Contribute to cross-functional technical planning, ensuring ML systems align with product and platform strategy.

Technical/ Execution

• Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask).
• Automate end-to-end ML lifecycle workflows including training pipelines, model validation, registry management, deployment, and rollback strategies.
• Implement robust monitoring systems for model performance, latency, drift detection, and infrastructure health using tools such as CloudWatch, Prometheus, and Grafana.
• Operate across AWS and GCP environments to manage training and inference workloads, including GPU-based infrastructure and BigQuery datasets.
• Develop and maintain infrastructure-as-code (Terraform, CloudFormation) to ensure scalable, repeatable, and secure cloud environments.
• Implement and optimize CI/CD workflows (e.g., GitHub Actions, GitLab CI, Bitbucket Pipelines) for ML and infrastructure automation.

Team / Collaboration

• Partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows.
• Translate data science experimentation needs into production-ready infrastructure solutions.
• Serve as the technical bridge between ML experimentation and productized deployment.
• Share knowledge and best practices to elevate ML maturity across teams.

Research/Best Practices

• Stay current on emerging ML Ops practices, tools, and frameworks to continuously improve system reliability and efficiency.
• Evaluate and implement model-serving frameworks (e.g., TorchServe, Seldon, TensorRT) where appropriate.
• Contribute to governance, reproducibility, and auditability standards for ML systems.
• Experiment with new tooling and workflows to improve reproducibility, performance, and developer velocity.

What success looks like:

• ML models move from experimentation to production quickly and reliably, with minimal manual intervention.
• CI/CD pipelines enable safe, repeatable deployments with clear rollback strategies.
• Model performance, drift, and infrastructure health are proactively monitored and observable.
• Infrastructure supports scalable GPU training and real-time inference without bottlenecks.
• Data scientists report improved velocity, reproducibility, and confidence in deploying models.
• ML systems are secure, compliant, and aligned with evolving product and AI strategy.

What you bring:

• 4+ years of experience in ML Ops, ML infrastructure, backend engineering, or related roles supporting production ML systems.
• Experience working in cloud-native environments (AWS and/or GCP) with hands-on deployment of ML workloads.
• Proven track record designing and implementing CI/CD pipelines for ML systems.
• Strong experience with Amazon SageMaker, Docker, Flask-based APIs, and infrastructure automation tools.
• Hands-on experience with ML lifecycle tooling such as MLflow, SageMaker Studio, or Weights & Biases.
• Experience managing container orchestration platforms (Kubernetes, EKS, or GKE).
• Strong programming experience in Python (additional experience in Go, Java, or Scala is a plus).
• Experience working with infrastructure-as-code tools such as Terraform or CloudFormation.
• Familiarity with observability tools such as CloudWatch, Prometheus, Grafana, Datadog, or centralized logging platforms.
• Experience managing GPU-based workloads and scaling training/inference systems.
• Familiarity with data infrastructure tools such as BigQuery and cloud-native data pipelines.
• Bonus: Experience supporting LLMs or generative AI pipelines, distributed training systems, feature stores (e.g., Feast), real-time inference systems, or ML governance frameworks.
• A mindset focused on automation, reliability, performance, and continuous improvement in fast-scaling environments.

How you work: 

• Driven by Impact: You deliver results that matter—prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
• Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
• Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast—challenging the status quo with a mindset of improvement.
• Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive—removing roadblocks, sharing insights, and keeping morale high.
• Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
• Emotionally Intelligent: You build trust through empathy and collaboration, foster inclusion, and inspire others with grit, optimism, and integrity.

Our approach to compensation:

We take a market-based & data-driven approach to compensation. We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope. We evaluate compensation bi-annually, including performance and market-related factors.

Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting. Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only.

To comply with local legislation and ensure transparency, we share salary ranges on all job postings. Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted. 

Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.

Salary Range: 

$ 145,000 -165,000 

#LI-remote

About this position:

We’re looking for a Machine Learning Infrastructure Engineer to join our growing Data & Platform team and build the foundation that powers our AI and machine learning capabilities across Later’s product portfolio. As our first dedicated ML Infrastructure Engineer, you will own the systems that support model experimentation, training, deployment, and monitoring at scale.

This role is critical to accelerating our data science initiatives and enabling future AI innovation. You’ll design and operate reliable, secure, and scalable ML infrastructure that empowers data scientists and engineers to ship high-impact models with confidence. If you’re excited about building robust ML systems in a fast-moving environment—and want to define the standard for ML Ops at Later—this is your opportunity.

What you'll be doing:

Strategy

• Define and own the long-term ML infrastructure roadmap, ensuring it supports both current experimentation needs and future AI initiatives.
• Establish best practices for model lifecycle management, deployment standards, monitoring, and governance. 
• Identify infrastructure gaps and proactively design scalable solutions to enable high-velocity ML development.
• Contribute to cross-functional technical planning, ensuring ML systems align with product and platform strategy.

Technical/ Execution

• Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask).
• Automate end-to-end ML lifecycle workflows including training pipelines, model validation, registry management, deployment, and rollback strategies.
• Implement robust monitoring systems for model performance, latency, drift detection, and infrastructure health using tools such as CloudWatch, Prometheus, and Grafana.
• Operate across AWS and GCP environments to manage training and inference workloads, including GPU-based infrastructure and BigQuery datasets.
• Develop and maintain infrastructure-as-code (Terraform, CloudFormation) to ensure scalable, repeatable, and secure cloud environments.
• Implement and optimize CI/CD workflows (e.g., GitHub Actions, GitLab CI, Bitbucket Pipelines) for ML and infrastructure automation.

Team / Collaboration

• Partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows.
• Translate data science experimentation needs into production-ready infrastructure solutions.
• Serve as the technical bridge between ML experimentation and productized deployment.
• Share knowledge and best practices to elevate ML maturity across teams.

Research/Best Practices

• Stay current on emerging ML Ops practices, tools, and frameworks to continuously improve system reliability and efficiency.
• Evaluate and implement model-serving frameworks (e.g., TorchServe, Seldon, TensorRT) where appropriate.
• Contribute to governance, reproducibility, and auditability standards for ML systems.
• Experiment with new tooling and workflows to improve reproducibility, performance, and developer velocity.

What success looks like:

• ML models move from experimentation to production quickly and reliably, with minimal manual intervention.
• CI/CD pipelines enable safe, repeatable deployments with clear rollback strategies.
• Model performance, drift, and infrastructure health are proactively monitored and observable.
• Infrastructure supports scalable GPU training and real-time inference without bottlenecks.
• Data scientists report improved velocity, reproducibility, and confidence in deploying models.
• ML systems are secure, compliant, and aligned with evolving product and AI strategy.

What you bring:

• 4+ years of experience in ML Ops, ML infrastructure, backend engineering, or related roles supporting production ML systems.
• Experience working in cloud-native environments (AWS and/or GCP) with hands-on deployment of ML workloads.
• Proven track record designing and implementing CI/CD pipelines for ML systems.
• Strong experience with Amazon SageMaker, Docker, Flask-based APIs, and infrastructure automation tools.
• Hands-on experience with ML lifecycle tooling such as MLflow, SageMaker Studio, or Weights & Biases.
• Experience managing container orchestration platforms (Kubernetes, EKS, or GKE).
• Strong programming experience in Python (additional experience in Go, Java, or Scala is a plus).
• Experience working with infrastructure-as-code tools such as Terraform or CloudFormation.
• Familiarity with observability tools such as CloudWatch, Prometheus, Grafana, Datadog, or centralized logging platforms.
• Experience managing GPU-based workloads and scaling training/inference systems.
• Familiarity with data infrastructure tools such as BigQuery and cloud-native data pipelines.
• Bonus: Experience supporting LLMs or generative AI pipelines, distributed training systems, feature stores (e.g., Feast), real-time inference systems, or ML governance frameworks.
• A mindset focused on automation, reliability, performance, and continuous improvement in fast-scaling environments.

How you work: 

• Driven by Impact: You deliver results that matter—prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
• Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
• Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast—challenging the status quo with a mindset of improvement.
• Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive—removing roadblocks, sharing insights, and keeping morale high.
• Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
• Emotionally Intelligent: You build trust through empathy and collaboration, foster inclusion, and inspire others with grit, optimism, and integrity.

Our approach to compensation:

We take a market-based & data-driven approach to compensation. We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope. We evaluate compensation bi-annually, including performance and market-related factors.

Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting. Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only.

To comply with local legislation and ensure transparency, we share salary ranges on all job postings. Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted. 

Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.

Salary Range: 

$ 145,000 -165,000 

#LI-remote

About this position:

We’re looking for a Machine Learning Infrastructure Engineer to join our growing Data & Platform team and build the foundation that powers our AI and machine learning capabilities across Later’s product portfolio. As our first dedicated ML Infrastructure Engineer, you will own the systems that support model experimentation, training, deployment, and monitoring at scale.

This role is critical to accelerating our data science initiatives and enabling future AI innovation. You’ll design and operate reliable, secure, and scalable ML infrastructure that empowers data scientists and engineers to ship high-impact models with confidence. If you’re excited about building robust ML systems in a fast-moving environment—and want to define the standard for ML Ops at Later—this is your opportunity.

What you'll be doing:

Strategy

• Define and own the long-term ML infrastructure roadmap, ensuring it supports both current experimentation needs and future AI initiatives.
• Establish best practices for model lifecycle management, deployment standards, monitoring, and governance. 
• Identify infrastructure gaps and proactively design scalable solutions to enable high-velocity ML development.
• Contribute to cross-functional technical planning, ensuring ML systems align with product and platform strategy.

Technical/ Execution

• Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask).
• Automate end-to-end ML lifecycle workflows including training pipelines, model validation, registry management, deployment, and rollback strategies.
• Implement robust monitoring systems for model performance, latency, drift detection, and infrastructure health using tools such as CloudWatch, Prometheus, and Grafana.
• Operate across AWS and GCP environments to manage training and inference workloads, including GPU-based infrastructure and BigQuery datasets.
• Develop and maintain infrastructure-as-code (Terraform, CloudFormation) to ensure scalable, repeatable, and secure cloud environments.
• Implement and optimize CI/CD workflows (e.g., GitHub Actions, GitLab CI, Bitbucket Pipelines) for ML and infrastructure automation.

Team / Collaboration

• Partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows.
• Translate data science experimentation needs into production-ready infrastructure solutions.
• Serve as the technical bridge between ML experimentation and productized deployment.
• Share knowledge and best practices to elevate ML maturity across teams.

Research/Best Practices

• Stay current on emerging ML Ops practices, tools, and frameworks to continuously improve system reliability and efficiency.
• Evaluate and implement model-serving frameworks (e.g., TorchServe, Seldon, TensorRT) where appropriate.
• Contribute to governance, reproducibility, and auditability standards for ML systems.
• Experiment with new tooling and workflows to improve reproducibility, performance, and developer velocity.

What success looks like:

• ML models move from experimentation to production quickly and reliably, with minimal manual intervention.
• CI/CD pipelines enable safe, repeatable deployments with clear rollback strategies.
• Model performance, drift, and infrastructure health are proactively monitored and observable.
• Infrastructure supports scalable GPU training and real-time inference without bottlenecks.
• Data scientists report improved velocity, reproducibility, and confidence in deploying models.
• ML systems are secure, compliant, and aligned with evolving product and AI strategy.

What you bring:

• 4+ years of experience in ML Ops, ML infrastructure, backend engineering, or related roles supporting production ML systems.
• Experience working in cloud-native environments (AWS and/or GCP) with hands-on deployment of ML workloads.
• Proven track record designing and implementing CI/CD pipelines for ML systems.
• Strong experience with Amazon SageMaker, Docker, Flask-based APIs, and infrastructure automation tools.
• Hands-on experience with ML lifecycle tooling such as MLflow, SageMaker Studio, or Weights & Biases.
• Experience managing container orchestration platforms (Kubernetes, EKS, or GKE).
• Strong programming experience in Python (additional experience in Go, Java, or Scala is a plus).
• Experience working with infrastructure-as-code tools such as Terraform or CloudFormation.
• Familiarity with observability tools such as CloudWatch, Prometheus, Grafana, Datadog, or centralized logging platforms.
• Experience managing GPU-based workloads and scaling training/inference systems.
• Familiarity with data infrastructure tools such as BigQuery and cloud-native data pipelines.
• Bonus: Experience supporting LLMs or generative AI pipelines, distributed training systems, feature stores (e.g., Feast), real-time inference systems, or ML governance frameworks.
• A mindset focused on automation, reliability, performance, and continuous improvement in fast-scaling environments.

How you work: 

• Driven by Impact: You deliver results that matter—prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
• Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
• Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast—challenging the status quo with a mindset of improvement.
• Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive—removing roadblocks, sharing insights, and keeping morale high.
• Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
• Emotionally Intelligent: You build trust through empathy and collaboration, foster inclusion, and inspire others with grit, optimism, and integrity.

Our approach to compensation:

We take a market-based & data-driven approach to compensation. We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope. We evaluate compensation bi-annually, including performance and market-related factors.

Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting. Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only.

To comply with local legislation and ensure transparency, we share salary ranges on all job postings. Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted. 

Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.

Salary Range: 

$ 145,000 -165,000 

#LI-remote

Lantern is seeking a Senior Software Engineer to join our healthcare technology team and help shape the future of our platform. In this role, you will drive technical innovation, design and build robust Python-based microservices, and maintain existing C# .NET applications as we transition toward a modern, scalable architecture. You’ll work across the full stack—building secure APIs, developing intuitive frontends, and delivering cloud-native solutions that improve the lives of patients and healthcare providers.

The ideal candidate is a hands-on engineer and technical leader who promotes a culture of knowledge sharing, collaboration, and continuous improvement. You’re comfortable working across domains, partnering with Product, and other stakeholders, and navigating ambiguity to make pragmatic architectural decisions. We value engineers who take ownership, communicate clearly, and are eager to mentor others while raising the technical bar.

At Lantern, we thrive on solving complex challenges and reimagining how healthcare is delivered. We’re looking for someone who brings creativity, curiosity, and a passion for building scalable, maintainable systems—along with a strong sense of teamwork, delivery focus, and a genuine enthusiasm for improving healthcare through technology.

Location: Hybrid - at least 3 days/wk in Vancouver, BC

Responsibilities:

• Design and implement robust backend solutions using Python (FastAPI, Django, or Flask) and contribute to .NET (C#) systems during the ongoing platform transition.
• Develop, document, and maintain secure RESTful APIs for integration with internal applications, third-party services, and mobile platforms.
• Build and maintain responsive frontends using React or Angular, tailored for care advocates and patient-facing applications.
• Leverage cloud platforms (Azure, AWS) to develop scalable, high-availability solutions using cloud-native services such as functions, messaging queues, storage, and managed databases.
• Maintain CI/CD pipelines in co-ownership with infrastructure team to support continuous delivery and infrastructure automation with tools like Azure DevOps or GitHub Actions.
• Collaborate closely with Product, and QA teams to define technical requirements and ensure regulatory alignment.
• Participate in system architecture design, peer code reviews, and sprint planning to drive high-quality engineering outcomes.
• Mentor junior developers and contribute to internal technical standards, documentation, and best practices.

Requirements:

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience
• A minimum of 5 years of experience in software development, with strong proficiency in Python and working knowledge of .NET (C#) for maintaining legacy systems.
• A minimum of 5 years of experience building secure, scalable REST APIs and backend services.
• Solid understanding of relational databases such as Microsoft SQL, PostgreSQL, or MySQL.
• Experience writing unit and integration tests using frameworks such as PyTest, xUnit, or Jest to ensure code quality and maintainability.
• Familiarity with OAuth 2.0 authentication and authorization flows.
• Strong experience with cloud platforms like Azure or AWS, including serverless and containerized services.
• Excellent problem-solving and analytical skills, with a proactive approach to modern design and development practices.
• Familiarity with healthcare data standards (FHIR, HL7, X12) and HIPAA-compliant environments is a plus
• Experience working within Agile/Scrum methodologies.

Benefits:

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Short- & Long-Term Disability
• Life Insurance
• Flexible PTO
• RRSP with company match

Lantern is seeking a Senior Software Engineer to join our healthcare technology team and help shape the future of our platform. In this role, you will drive technical innovation, design and build robust Python-based microservices, and maintain existing C# .NET applications as we transition toward a modern, scalable architecture. You’ll work across the full stack—building secure APIs, developing intuitive frontends, and delivering cloud-native solutions that improve the lives of patients and healthcare providers.

The ideal candidate is a hands-on engineer and technical leader who promotes a culture of knowledge sharing, collaboration, and continuous improvement. You’re comfortable working across domains, partnering with Product, and other stakeholders, and navigating ambiguity to make pragmatic architectural decisions. We value engineers who take ownership, communicate clearly, and are eager to mentor others while raising the technical bar.

At Lantern, we thrive on solving complex challenges and reimagining how healthcare is delivered. We’re looking for someone who brings creativity, curiosity, and a passion for building scalable, maintainable systems—along with a strong sense of teamwork, delivery focus, and a genuine enthusiasm for improving healthcare through technology.

Location: Hybrid - at least 3 days/wk in one of our offices:  Dallas, TX (preferred) / Chicago/Evanston / New York / Washington, DC

Responsibilities:

• Design and implement robust backend solutions using Python (FastAPI, Django, or Flask) and contribute to .NET (C#) systems during the ongoing platform transition.
• Develop, document, and maintain secure RESTful APIs for integration with internal applications, third-party services, and mobile platforms.
• Build and maintain responsive frontends using React or Angular, tailored for care advocates and patient-facing applications.
• Leverage cloud platforms (Azure, AWS) to develop scalable, high-availability solutions using cloud-native services such as functions, messaging queues, storage, and managed databases.
• Maintain CI/CD pipelines in co-ownership with infrastructure team to support continuous delivery and infrastructure automation with tools like Azure DevOps or GitHub Actions.
• Collaborate closely with Product, and QA teams to define technical requirements and ensure regulatory alignment.
• Participate in system architecture design, peer code reviews, and sprint planning to drive high-quality engineering outcomes.
• Mentor junior developers and contribute to internal technical standards, documentation, and best practices.

Requirements:

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience
• A minimum of 5 years of experience in software development, with strong proficiency in Python and working knowledge of .NET (C#) for maintaining legacy systems.
• A minimum of 5 years of experience building secure, scalable REST APIs and backend services.
• Solid understanding of relational databases such as Microsoft SQL, PostgreSQL, or MySQL.
• Experience writing unit and integration tests using frameworks such as PyTest, xUnit, or Jest to ensure code quality and maintainability.
• Familiarity with OAuth 2.0 authentication and authorization flows.
• Strong experience with cloud platforms like Azure or AWS, including serverless and containerized services.
• Excellent problem-solving and analytical skills, with a proactive approach to modern design and development practices.
• Familiarity with healthcare data standards (FHIR, HL7, X12) and HIPAA-compliant environments is a plus
• Experience working within Agile/Scrum methodologies.

Benefits

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Short & Long Term Disability
• Life Insurance
• 401k with company match
• Paid Time Off
• Paid Parental Leave

ROLE OVERVIEW:

As a Full-Stack Developer, you will architect and build custom web applications across multiple client engagements, owning both the backend API layer and the frontend. A defining aspect of this role is working with AI as a logic layer within applications: integrating LLM APIs for validation, decision-making, classification, and content generation. You make strong technical decisions around authentication, security, state management, and performance, and you bring cloud engineering awareness for edge computing, CDN delivery, and serverless environments. This is not a traditional manual coding role. You orchestrate AI development tools, primarily Claude Code, through strategic prompting and rigorous review, taking full ownership of what ships.

RESPONSIBILITIES

Your key responsibilities will include:

• Architect and build custom web applications using Node.js (Nest.js, Express, Hono), Python (FastAPI, Flask), and Next.js/React.
• Design and build RESTful and/or GraphQL APIs that serve multiple frontends and integrate with third-party services.
• Integrate AI and LLM APIs as a core logic layer, building intelligent features such as validation, classification, content generation, and decision support.
• Implement authentication, authorisation (JWT, OAuth, RBAC), and security across applications.
• Design and manage database layers (PostgreSQL, MongoDB, cloud-managed equivalents), including schema design, migrations, and query optimisation.
• Architect with cloud-native patterns: edge functions, CDN configuration, serverless deployments, and caching strategies.
• Write and maintain automated tests across the full stack: unit (Vitest/Jest), API/integration, component (React Testing Library), and E2E (Playwright/Cypress).
• Orchestrate AI development tools (Claude Code) through strategic prompting, ensuring high-quality, production-ready output.

EXPERIENCE

The ideal candidate will have:

• Strong backend proficiency across Node.js (Nest.js, Express, Hono) and Python (FastAPI, Flask), with TypeScript and Python as working languages.
• Strong Next.js and React experience.
• Experience integrating AI/LLM APIs as an application logic layer: validation, classification, content generation, and decision-making within business workflows.
• Database design and management: PostgreSQL, MongoDB, cloud-managed services (Cloud SQL, RDS, ApsaraDB).
• Authentication and authorisation patterns (JWT, OAuth, RBAC) and API security.
• Cloud engineering: edge functions, CDN configuration, serverless architecture, container orchestration.
• Full-stack test automation: unit, API/integration, component, and E2E testing.
• Performance optimisation: caching, query optimisation, code splitting, monitoring.
• REST and/or GraphQL API design.
• Demonstrated ability to orchestrate AI development tools through strategic prompting and quality oversight.

TECHNOLOGIES

Deep understanding of the following technologies is essential:

• Node.js, TypeScript, Nest.js, Express, Hono, Python, FastAPI, Flask.
• Next.js, React, Tailwind CSS.
• PostgreSQL, MongoDB, cloud-managed database services.
• Authentication: JWT, OAuth, RBAC.
• Cloud and edge: Alibaba Cloud, GCP, AWS, Cloudflare Workers, Vercel Edge, Lambda.
• AI/LLM API integration and orchestration.
• Testing: Vitest/Jest, React Testing Library, Playwright/Cypress, API testing.
• Docker, CI/CD, Git, Claude Code.

PREFERRED QUALIFICATIONS

• Experience in a consulting or agency environment across multiple concurrent client engagements.
• Apollo GraphQL Server for federated or schema-first APIs.
• Event-driven patterns (webhooks, message queues, real-time notifications).
• Multi-model AI strategies (routing between LLMs based on task requirements).
• Graph databases (Neo4j) or vector databases (pgvector, Pinecone).
• Infrastructure as Code (Terraform, Pulumi).
• Export/print workflows (PDF generation, report exports).

WHO YOU ARE

You are a full-stack developer who takes ownership of the complete application, from API design and database schema through to the frontend. You treat security, performance, and data integrity as first-class concerns. You are comfortable working with AI as a logic layer, and you understand cloud-native architecture well enough to make informed decisions about edge computing, caching, and deployment. You thrive in a consulting environment, adapting to new domains and communicating clearly with technical teams and client stakeholders.

WHAT APPNOVATION HAS TO OFFER 

• Challenging and rewarding work with real impact
• Direct Access to Cutting-Edge AI Platforms
• Diverse and Inclusive Culture 
• Growth opportunities for personal and professional development
• A collaborative and innovative work environment where your ideas are valued
• Exposure to exciting projects and high-profile clients
• Supportive work environment with access to company leaders
• Hybrid working model

ROLE OVERVIEW:

As a Full-Stack Developer, you will architect and build custom web applications across multiple client engagements, owning both the backend API layer and the frontend. A defining aspect of this role is working with AI as a logic layer within applications: integrating LLM APIs for validation, decision-making, classification, and content generation. You make strong technical decisions around authentication, security, state management, and performance, and you bring cloud engineering awareness for edge computing, CDN delivery, and serverless environments. This is not a traditional manual coding role. You orchestrate AI development tools, primarily Claude Code, through strategic prompting and rigorous review, taking full ownership of what ships.

RESPONSIBILITIES

Your key responsibilities will include:

• Architect and build custom web applications using Node.js (Nest.js, Express, Hono), Python (FastAPI, Flask), and Next.js/React.
• Design and build RESTful and/or GraphQL APIs that serve multiple frontends and integrate with third-party services.
• Integrate AI and LLM APIs as a core logic layer, building intelligent features such as validation, classification, content generation, and decision support.
• Implement authentication, authorisation (JWT, OAuth, RBAC), and security across applications.
• Design and manage database layers (PostgreSQL, MongoDB, cloud-managed equivalents), including schema design, migrations, and query optimisation.
• Architect with cloud-native patterns: edge functions, CDN configuration, serverless deployments, and caching strategies.
• Write and maintain automated tests across the full stack: unit (Vitest/Jest), API/integration, component (React Testing Library), and E2E (Playwright/Cypress).
• Orchestrate AI development tools (Claude Code) through strategic prompting, ensuring high-quality, production-ready output.

EXPERIENCE

The ideal candidate will have:

• Strong backend proficiency across Node.js (Nest.js, Express, Hono) and Python (FastAPI, Flask), with TypeScript and Python as working languages.
• Strong Next.js and React experience.
• Experience integrating AI/LLM APIs as an application logic layer: validation, classification, content generation, and decision-making within business workflows.
• Database design and management: PostgreSQL, MongoDB, cloud-managed services (Cloud SQL, RDS, ApsaraDB).
• Authentication and authorisation patterns (JWT, OAuth, RBAC) and API security.
• Cloud engineering: edge functions, CDN configuration, serverless architecture, container orchestration.
• Full-stack test automation: unit, API/integration, component, and E2E testing.
• Performance optimisation: caching, query optimisation, code splitting, monitoring.
• REST and/or GraphQL API design.
• Demonstrated ability to orchestrate AI development tools through strategic prompting and quality oversight.

TECHNOLOGIES

Deep understanding of the following technologies is essential:

• Node.js, TypeScript, Nest.js, Express, Hono, Python, FastAPI, Flask.
• Next.js, React, Tailwind CSS.
• PostgreSQL, MongoDB, cloud-managed database services.
• Authentication: JWT, OAuth, RBAC.
• Cloud and edge: Alibaba Cloud, GCP, AWS, Cloudflare Workers, Vercel Edge, Lambda.
• AI/LLM API integration and orchestration.
• Testing: Vitest/Jest, React Testing Library, Playwright/Cypress, API testing.
• Docker, CI/CD, Git, Claude Code.

PREFERRED QUALIFICATIONS

• Experience in a consulting or agency environment across multiple concurrent client engagements.
• Apollo GraphQL Server for federated or schema-first APIs.
• Event-driven patterns (webhooks, message queues, real-time notifications).
• Multi-model AI strategies (routing between LLMs based on task requirements).
• Graph databases (Neo4j) or vector databases (pgvector, Pinecone).
• Infrastructure as Code (Terraform, Pulumi).
• Export/print workflows (PDF generation, report exports).

WHO YOU ARE

You are a full-stack developer who takes ownership of the complete application, from API design and database schema through to the frontend. You treat security, performance, and data integrity as first-class concerns. You are comfortable working with AI as a logic layer, and you understand cloud-native architecture well enough to make informed decisions about edge computing, caching, and deployment. You thrive in a consulting environment, adapting to new domains and communicating clearly with technical teams and client stakeholders.

WHAT APPNOVATION HAS TO OFFER 

• Challenging and rewarding work with real impact
• Direct Access to Cutting-Edge AI Platforms
• Diverse and Inclusive Culture 
• Growth opportunities for personal and professional development
• A collaborative and innovative work environment where your ideas are valued
• Exposure to exciting projects and high-profile clients
• Supportive work environment with access to company leaders
• Hybrid working model

About this position:

We’re looking for a Machine Learning Infrastructure Engineer to join our growing Data & Platform team and build the foundation that powers our AI and machine learning capabilities across Later’s product portfolio. As our first dedicated ML Infrastructure Engineer, you will own the systems that support model experimentation, training, deployment, and monitoring at scale.

This role is critical to accelerating our data science initiatives and enabling future AI innovation. You’ll design and operate reliable, secure, and scalable ML infrastructure that empowers data scientists and engineers to ship high-impact models with confidence. If you’re excited about building robust ML systems in a fast-moving environment—and want to define the standard for ML Ops at Later—this is your opportunity.

What you'll be doing:

Strategy

• Define and own the long-term ML infrastructure roadmap, ensuring it supports both current experimentation needs and future AI initiatives.
• Establish best practices for model lifecycle management, deployment standards, monitoring, and governance. 
• Identify infrastructure gaps and proactively design scalable solutions to enable high-velocity ML development.
• Contribute to cross-functional technical planning, ensuring ML systems align with product and platform strategy.

Technical/ Execution

• Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask).
• Automate end-to-end ML lifecycle workflows including training pipelines, model validation, registry management, deployment, and rollback strategies.
• Implement robust monitoring systems for model performance, latency, drift detection, and infrastructure health using tools such as CloudWatch, Prometheus, and Grafana.
• Operate across AWS and GCP environments to manage training and inference workloads, including GPU-based infrastructure and BigQuery datasets.
• Develop and maintain infrastructure-as-code (Terraform, CloudFormation) to ensure scalable, repeatable, and secure cloud environments.
• Implement and optimize CI/CD workflows (e.g., GitHub Actions, GitLab CI, Bitbucket Pipelines) for ML and infrastructure automation.

Team / Collaboration

• Partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows.
• Translate data science experimentation needs into production-ready infrastructure solutions.
• Serve as the technical bridge between ML experimentation and productized deployment.
• Share knowledge and best practices to elevate ML maturity across teams.

Research/Best Practices

• Stay current on emerging ML Ops practices, tools, and frameworks to continuously improve system reliability and efficiency.
• Evaluate and implement model-serving frameworks (e.g., TorchServe, Seldon, TensorRT) where appropriate.
• Contribute to governance, reproducibility, and auditability standards for ML systems.
• Experiment with new tooling and workflows to improve reproducibility, performance, and developer velocity.

What success looks like:

• ML models move from experimentation to production quickly and reliably, with minimal manual intervention.
• CI/CD pipelines enable safe, repeatable deployments with clear rollback strategies.
• Model performance, drift, and infrastructure health are proactively monitored and observable.
• Infrastructure supports scalable GPU training and real-time inference without bottlenecks.
• Data scientists report improved velocity, reproducibility, and confidence in deploying models.
• ML systems are secure, compliant, and aligned with evolving product and AI strategy.

What you bring:

• 4+ years of experience in ML Ops, ML infrastructure, backend engineering, or related roles supporting production ML systems.
• Experience working in cloud-native environments (AWS and/or GCP) with hands-on deployment of ML workloads.
• Proven track record designing and implementing CI/CD pipelines for ML systems.
• Strong experience with Amazon SageMaker, Docker, Flask-based APIs, and infrastructure automation tools.
• Hands-on experience with ML lifecycle tooling such as MLflow, SageMaker Studio, or Weights & Biases.
• Experience managing container orchestration platforms (Kubernetes, EKS, or GKE).
• Strong programming experience in Python (additional experience in Go, Java, or Scala is a plus).
• Experience working with infrastructure-as-code tools such as Terraform or CloudFormation.
• Familiarity with observability tools such as CloudWatch, Prometheus, Grafana, Datadog, or centralized logging platforms.
• Experience managing GPU-based workloads and scaling training/inference systems.
• Familiarity with data infrastructure tools such as BigQuery and cloud-native data pipelines.
• Bonus: Experience supporting LLMs or generative AI pipelines, distributed training systems, feature stores (e.g., Feast), real-time inference systems, or ML governance frameworks.
• A mindset focused on automation, reliability, performance, and continuous improvement in fast-scaling environments.

How you work: 

• Driven by Impact: You deliver results that matter—prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
• Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
• Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast—challenging the status quo with a mindset of improvement.
• Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive—removing roadblocks, sharing insights, and keeping morale high.
• Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
• Emotionally Intelligent: You build trust through empathy and collaboration, foster inclusion, and inspire others with grit, optimism, and integrity.

Our approach to compensation:

We take a market-based & data-driven approach to compensation. We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope. We evaluate compensation bi-annually, including performance and market-related factors.

Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting. Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only.

To comply with local legislation and ensure transparency, we share salary ranges on all job postings. Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted. 

Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.

Salary Range: 

$ 145,000 -165,000 CAD

#LI-Hybrid 

 Software Engineering Intern- AI/ML Backend

Company: Digs

Location: Hybrid (HQ: Vancouver, WA)

Job Type: Internship (Spring 2026)

Duration: 12 weeks 

About Digs

Digs is building the future of collaboration between builders and homeowners. Our platform uses artificial intelligence to organize, surface, and share the information that matters most during a home build—making the entire experience more transparent, connected, and intuitive.

We’re a team of experienced entrepreneurs, operators, and builders who care deeply about craft. We move fast, think long-term, and hold a high bar for what we build. If you’re curious, motivated, and excited about creating meaningful technology from the ground up, you’ll fit right in.

At Digs, you won't be working on throwaway sandbox projects. We expect you to hit the ground running and ship real production code from day one. You will act as a core member of our R&D team, working alongside our Head of R&D and the broader engineering team to build the services and infrastructure that turn ML model outputs into user-facing value — focusing on backend integration, evaluation, and production readiness rather than model research.

Want to know what drives us? Explore our Digs Principles.  

What You'll Do

• Build and maintain FastAPI services in Python that serve ML models and integrate into our broader microservices architecture.

• Build evaluation frameworks and benchmarks to systematically measure quality across our ML services.

• Work with geometric and spatial data using libraries like Shapely, NumPy, and OpenCV to process, validate, and post-process model outputs into production-ready formats.

• Improve and extend our vision and language pipelines for construction document understanding, including tasks such as instance segmentation, classification, and geometric shape analysis.

• Integrate ML outputs into production workflows orchestrated by our Restate durable execution engine.

• Adopt and help shape our agentic coding practices — our team embraces tools like Cursor, Copilot, and Claude Code to accelerate development.

You Might Also 

• Work with vector embeddings and semantic search infrastructure using pgvector.

• Contribute to our TypeScript backend and React frontend as features require end-to-end implementation.

• Run data analysis and experimentation to identify model failure modes and drive improvements with measurable results.

• Contribute to our human-in-the-loop annotation workflows, helping design pre-annotation pipelines and process crowdsourced labeling results.

Who You Are

• You recently completed a degree in Computer Science, Machine Learning, Data Science, or a related field (or have equivalent practical experience) and are looking for a runway to transition into a full-time role.

• You have a strong foundation in Python and are comfortable writing production-quality code, not just notebook prototypes.

• You have coursework or project experience in machine learning and understand the fundamentals of training, evaluation, and iteration.

• You are comfortable working with data — cleaning it, analyzing it, visualizing it, and drawing conclusions from it.

• You are a highly collaborative, self-motivated communicator who thrives in ambiguity, takes ownership of your work without waiting for instructions, and isn't afraid to say "I don't know yet" while moving fast.

• You are comfortable participating in virtual meetings and in-person collaboration at our Vancouver, WA HQ

Bonus Qualities You Might Have

• Experience building APIs with FastAPI, Flask, or similar Python web frameworks and deploying models behind them.

• Familiarity with HuggingFace Transformers and the model hub ecosystem.

• Experience training models from scratch or fine-tuning using supervised fine-tuning or parameter-efficient fine-tuning techniques.

• Contributions to open-source projects or published research.

• Startup experience: proven ability to move fast on a small engineering team.

• Experience or interest in the home building, home remodeling, construction, or real estate industries.

The Details:

This internship position is set to begin in April 2026 and will last 12 weeks, with a commitment of approximately 30 hours per week. The compensation ranges from $20 to $25 per hour, depending on experience. The internship will be hybrid, with in-person and remote components, conducted at our Vancouver, WA headquarters and via platforms such as Google Meet and Zoom. We will provide all necessary equipment, including a laptop, to ensure you have everything needed to succeed in this role. 

How to Apply:

If this role excites you and you feel aligned with our Principles, please submit your application via LinkedIn or the Digs Careers Page. 

Note: This internship position may lead to future employment opportunities, subject to performance and business needs.

Digs is committed to fostering an inclusive and diverse workplace where everyone feels valued and respected. We encourage candidates from all backgrounds to apply. 

Applicants must be authorized to work in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.