Later
Later is the world’s most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing’s most visible investments.
Built on a native, AI-powered platform and more than a decade of proprietary data—including billions of social interactions, impressions, and $2.4B+ in verified influencer-driven purchases—Later helps teams understand what will work before they launch.
By combining trusted insight with expert guidance, Later removes guesswork from influencer marketing, enabling brands to choose the right creators, execute fully managed campaigns, and drive meaningful growth across awareness, engagement, and revenue. Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good—they deliver results. Learn more at later.com.
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Later is the world’s most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing’s most visible investments.
Built on a native, AI-powered platform and more than a decade of proprietary data—including billions of social interactions, impressions, and $2.4B+ in verified influencer-driven purchases—Later helps teams understand what will work before they launch.
By combining trusted insight with expert guidance, Later removes guesswork from influencer marketing, enabling brands to choose the right creators, execute fully managed campaigns, and drive meaningful growth across awareness, engagement, and revenue. Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good—they deliver results. Learn more at later.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
Where we work:
We have offices in Boston, MA; Vancouver, BC; Chicago, IL; and Vancouver, WA. For select positions, we are open to hiring fully remote candidates. We post our positions in the location(s) where we are open to having the successful candidate be located.
Diversity, inclusion, and accessibility:
At Later, we are committed to fostering a culture rooted in an inclusion-first mindset at every level of the company, embracing the importance of hiring and building teams for culture add rather than culture fit. We openly build and maintain unbiased hiring, pay, and promotion practices to create a foundation for an equitable workplace, paving the way for systemic change.
We are committed to creating a diverse environment and are proud to be an equal opportunity employer. All applications will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, national origin, disability, or age. Please let us know if you require any accommodations or support during the recruitment process.
Ready to apply?
Apply to Later
Later is the world’s most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing’s most visible investments.
Built on a native, AI-powered platform and more than a decade of proprietary data—including billions of social interactions, impressions, and $2.4B+ in verified influencer-driven purchases—Later helps teams understand what will work before they launch.
By combining trusted insight with expert guidance, Later removes guesswork from influencer marketing, enabling brands to choose the right creators, execute fully managed campaigns, and drive meaningful growth across awareness, engagement, and revenue. Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good—they deliver results. Learn more at later.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
Where we work:
We have offices in Boston, MA; Vancouver, BC; Chicago, IL; and Vancouver, WA. For select positions, we are open to hiring fully remote candidates. We post our positions in the location(s) where we are open to having the successful candidate be located.
Diversity, inclusion, and accessibility:
At Later, we are committed to fostering a culture rooted in an inclusion-first mindset at every level of the company, embracing the importance of hiring and building teams for culture add rather than culture fit. We openly build and maintain unbiased hiring, pay, and promotion practices to create a foundation for an equitable workplace, paving the way for systemic change.
We are committed to creating a diverse environment and are proud to be an equal opportunity employer. All applications will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, national origin, disability, or age. Please let us know if you require any accommodations or support during the recruitment process.
Ready to apply?
Apply to Later
Later is the world’s most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing’s most visible investments.
Built on a native, AI-powered platform and more than a decade of proprietary data—including billions of social interactions, impressions, and $2.4B+ in verified influencer-driven purchases—Later helps teams understand what will work before they launch.
By combining trusted insight with expert guidance, Later removes guesswork from influencer marketing, enabling brands to choose the right creators, execute fully managed campaigns, and drive meaningful growth across awareness, engagement, and revenue. Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good—they deliver results. Learn more at later.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
Where we work:
We have offices in Boston, MA; Vancouver, BC; Chicago, IL; and Vancouver, WA. For select positions, we are open to hiring fully remote candidates. We post our positions in the location(s) where we are open to having the successful candidate be located.
Diversity, inclusion, and accessibility:
At Later, we are committed to fostering a culture rooted in an inclusion-first mindset at every level of the company, embracing the importance of hiring and building teams for culture add rather than culture fit. We openly build and maintain unbiased hiring, pay, and promotion practices to create a foundation for an equitable workplace, paving the way for systemic change.
We are committed to creating a diverse environment and are proud to be an equal opportunity employer. All applications will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, national origin, disability, or age. Please let us know if you require any accommodations or support during the recruitment process.
Ready to apply?
Apply to Later
Later is the world’s most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing’s most visible investments.
Built on a native, AI-powered platform and more than a decade of proprietary data—including billions of social interactions, impressions, and $2.4B+ in verified influencer-driven purchases—Later helps teams understand what will work before they launch.
By combining trusted insight with expert guidance, Later removes guesswork from influencer marketing, enabling brands to choose the right creators, execute fully managed campaigns, and drive meaningful growth across awareness, engagement, and revenue. Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good—they deliver results. Learn more at later.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 CAD
#LI-Hybrid
Where we work:
We have offices in Boston, MA; Vancouver, BC; Chicago, IL; and Vancouver, WA. For select positions, we are open to hiring fully remote candidates. We post our positions in the location(s) where we are open to having the successful candidate be located.
Diversity, inclusion, and accessibility:
At Later, we are committed to fostering a culture rooted in an inclusion-first mindset at every level of the company, embracing the importance of hiring and building teams for culture add rather than culture fit. We openly build and maintain unbiased hiring, pay, and promotion practices to create a foundation for an equitable workplace, paving the way for systemic change.
We are committed to creating a diverse environment and are proud to be an equal opportunity employer. All applications will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, national origin, disability, or age. Please let us know if you require any accommodations or support during the recruitment process.
Ready to apply?
Apply to Later
Later is the world’s most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing’s most visible investments.
Built on a native, AI-powered platform and more than a decade of proprietary data—including billions of social interactions, impressions, and $2.4B+ in verified influencer-driven purchases—Later helps teams understand what will work before they launch.
By combining trusted insight with expert guidance, Later removes guesswork from influencer marketing, enabling brands to choose the right creators, execute fully managed campaigns, and drive meaningful growth across awareness, engagement, and revenue. Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good—they deliver results. Learn more at later.com.
About this position:
Later seeks a dynamic Director of Data Analytics to oversee our Insights & Analytics team. This leadership role involves guiding a diverse, cross-functional group of embedded analysts who support our Marketing, Sales, Client Services, Creator Success, RevOps, and Product departments.You’ll own the analytics roadmap, our BI layer, and how the company uses data to make decisions. This is a player-coach role. You’ll set the strategic vision and grow the team while staying hands-on delivering high-impact analyses alongside your reports. The time split is roughly equal across three dimensions: strategic leadership, individual contribution, and people management.This is a strategic leadership role. You'll set the vision for the Data Analytics team, drive strategic initiatives, and grow the function. While staying informed on delivery and coaching the team, the emphasis will be on long-term strategy and organizational growth.
What you'll be doing:
Own the Analytics Roadmap
- Define quarterly and annual analytics priorities, aligning to company OKRs across Later Social, Later Influence, and Mavely
- Balance strategic builds (predictive models, campaign forecasting, self-serve dashboards) with operational support and ad hoc stakeholder requests
- Drive the shift from reactive reporting to proactive, insight-driven analytics that directly influences business outcomes
Own the BI Layer
- End-to-end ownership of the business intelligence layer, including dashboard strategy, semantic modeling, and data governance
- Lead BI platform consolidation and modernization, expanding self-serve analytics capabilities across the organization
- Build enterprise data products: forecast calculators, benchmarking tools, client-facing dashboards, and operational reports
- Partner with Data Engineering on data warehouse strategy, data quality frameworks, and metric standardization
Partner Cross-Functionally
- Serve as the primary analytics partner to senior stakeholders across the business:
- Marketing: demand generation, paid media, ABM, attribution, and marketing ROI
- Sales: pipeline analytics, revenue forecasting, and CRM reporting
- Client Services: campaign performance, client health, utilization, and enterprise account analytics
- Creator Success: creator scoring, GMV attribution, account management dashboards, and creator-facing reports
- RevOps & Finance: MRR/ARR modeling, churn analysis, pricing analytics, and operational reporting
- Product & Engineering: product analytics strategy, data infrastructure, and featureadoption
- Translate complex data insights into clear, actionable recommendations for executive audiences
- Manage a structured service desk intake process for analytics requests with business priority scoring
Lead the Team
- Manage and grow a cross-functional team of analysts across marketing analytics,
- client/creator analytics, commerce analytics, data science, and BI engineering
- Operate as a player-coach: set direction and remove blockers while personally contributing to high-impact analyses and data products
- Run sprint planning, resource allocation, and capacity planning across concurrent workstreams
- Build career development frameworks and onboarding programs for new analysts
- Own the hiring roadmap to scale the team in line with business growth
What you bring:
- 8+ years of progressive experience in data analytics, business intelligence, or a related quantitative field
- 3+ years managing analytics teams, with a track record of developing high-performing analysts
- Proven ability to own and execute an analytics roadmap across multiple business domains simultaneously
- Deep SQL expertise and proficiency with modern BI tools (Domo, Mode)
- Experience with a cloud data warehouse (BigQuery) and data modeling patterns
- Experience building predictive models, forecasting tools, or data science–adjacent products (Python/R a plus)
- Strong financial acumen—comfortable with MRR/ARR modeling, revenue attribution, and LTV/CAC analysis
- Exceptional stakeholder management across executive, technical, and operational audiences
- Experience in marketplace, creator economy, or influencer marketing is highly valued
- A bias for action and comfort with ambiguity in a fast-paced, evolving environment
Nice to Have
- Experience with influencer marketing platforms, creator commerce, or affiliate/performance marketing analytics
- Familiarity with CRM/GTM tools (Salesforce, HubSpot) and product analytics platforms (Amplitude, Mixpanel)
- Background in data governance, semantic layers, or enterprise data dictionary development
- Experience with AI/ML–powered analytics products (campaign prediction, scoring algorithms, churn models)
- Track record of BI platform migrations or consolidation projects
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:
$200,000 - $225,000 USD
*Co-op team members, independent contractors, and freelancers are not eligible for company benefits.
#LI-Hybrid #LI-Remote
Where we work:
We have offices in Boston, MA; Vancouver, BC; Chicago, IL; and Vancouver, WA. For select positions, we are open to hiring fully remote candidates. We post our positions in the location(s) where we are open to having the successful candidate be located.
Diversity, inclusion, and accessibility:
At Later, we are committed to fostering a culture rooted in an inclusion-first mindset at every level of the company, embracing the importance of hiring and building teams for culture add rather than culture fit. We openly build and maintain unbiased hiring, pay, and promotion practices to create a foundation for an equitable workplace, paving the way for systemic change.
We are committed to creating a diverse environment and are proud to be an equal opportunity employer. All applications will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, national origin, disability, or age. Please let us know if you require any accommodations or support during the recruitment process.
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