Your role
As an analytics engineer, you’ll be an integral part of our data analysis and QA team, providing essential data support for our cutting-edge Agentic AI initiatives, specifically with ASR and NLP teams. A key focus will be on leveraging data analysis to identify and drive optimization of the conversational flows and underlying data infrastructure for agentic voice and chat solutions. You will actively seek opportunities to improve the team’s productivity and workflow through automation and process optimization, including implementing automated QA for our agentic systems.

This position reports to the manager of the Data Analysis and QA team and has the opportunity to be based in our Vancouver, Canada Office.

What you’ll do 

• You will work closely with other Agentic AI teams to test and evaluate our Agentic voice and chat solutions.
• You will design and implement data logging and monitoring strategies to capture key analytical insights.
• You will utilize data analysis to identify areas for optimization in the voice bot's conversational flows and data pipeline.
• You will build and maintain data pipelines on Vertex AI pipelines. 
• You will work closely with the AI Platform team to build tooling for data science projects. 
• You will implement automation and processes to improve our workflow. 
• You will create and maintain dashboards (Tableau) and data pipelines (Dataform) that help drive product and business decisions.
• You will contribute to our continuous efforts to enforce data privacy and compliance. 
• You will collaborate with cross-functional teams, including AI, engineering, and product teams.

Skills you’ll bring 

• Bachelor's or Master’s degree in Computer Science, Software Engineering, or related fields.
• 1 - 3 years of working experience with software engineering or data engineering projects. 
• 1 - 3 years of experience with Python, working with GCP, including storage, BigQuery, Compute, Kubernetes, or similar.
• 1 - 3 years of experience with SQL, able to optimize complex SQL queries and build data pipelines. 
• Experience with BI tools such as Tableau. 
• Experience analyzing the performance of conversational AI systems (e.g., voice bots, chatbots) and collaborating with cross-functional teams (AI, Product) on data-driven improvements.
• Experience working with popular LLM frameworks.
• Strong problem-solving and analytical abilities, with the capacity to handle complex technical and analytical problems.

Your role
As a Sr. Software Engineer on the Tel Core team, you are the steward of our media engine’s evolution. You will bridge the gap between high-level product goals and technical execution, ensuring our telephony stack remains resilient as we scale. You’ll mentor peers and shape the long-term roadmap while fostering a culture of engineering excellence. Your success is defined by the seamless, high-quality audio experience of millions of users.

This position reports to an Engineering Manager in the Telephony Core team and has the opportunity to be based in either Vancouver or Kitchener, Canada locations 

We don’t mind if you come from a different engineering background or haven’t worked in real-time media before. Just be excited to learn, to contribute to the team, and to build a fast, reliable, elegant product that delights our customers.

What you’ll do

• Maintain and modernize the real-time services that run on the Telephony Engines. Architect backend libraries and APIs to integrate with the broader Telephony Platform.
• Build and optimize low-latency media pipelines for voice and video—tuning codecs, RTP flows, and congestion-control behavior to deliver consistently clear conversations over unreliable networks.
• Investigate and resolve complex production issues across PSTN, WebRTC, and carrier boundaries using Telephony Engine logs, PCAPs, observability tools, and data-driven experimentation.
• Run load, failover, and regression tests on Telephony Engines and backend services to validate scalability, resilience, and capacity limits
• Partner with Tel Cloud, Tel Edge, Calling, and AI teams to design end-to-end call flows and integrations
• Participate in on-call and release rotations, owning Terraform- and GKE-backed rollouts, monitoring health, and driving follow-ups that continuously make the platform safer and easier to operate.
• Shape the Tel Core roadmap by turning broad epics into clear designs and mentoring other engineers.

Skills you’ll bring 

• Bachelor's degree in Computer Science or equivalent practical experience.
• 5+ years of software engineering experience, including owning substantial production backend systems in a SaaS or real‑time communications environment.
• Demonstrated proficiency in coding with AI agents to accelerate development and improve code quality.
• Deep expertise in C/C++ and Python.
• Experience with (or strong interest in learning) SIP, WebRTC, RTP/RTCP, DTLS/SRTP, and media codec negotiation in a production setting.
• Experience troubleshooting and improving real‑time media quality (e.g., jitter, packet loss, choppy audio, codec issues) is a strong plus.
• Comfortable working on KTLO (bugs, refactors, upgrades) as well as strategic roadmap items, and using data to prioritize impact.
• A deep understanding of reliability engineering, including the ability to build observability into services, eliminate performance bottlenecks, and manage the lifecycle of safe software releases.
• A strong sense of urgency, personal agency, and customer empathy.
• Passion for data driven decisions, building resilient systems, and solving classes of problems.

Our tech stack

• C, C++, Python, Go, Lua.
• Kubernetes, Redis, ElasticSearch, BigQuery.
• Google Cloud Platform: Cloud Run, GKE, Datastore, Pub/Sub.

Overview of Role

66degrees’ Managed Cloud Optimization (MCO) team works with some of the largest cloud users in the world to help them transform their businesses with technology. Our Site Reliability Engineers (SREs) combine Google Cloud Platform expertise with a passion for devops methodologies to help our clients maintain, optimize, and scale their cloud implementations.

On a daily basis, our SREs work with varied and exciting customers on topics ranging from solving critical outages to designing and deploying new cloud workloads to building self-healing automation. Our SREs work with cutting-edge Google Cloud technologies like Google Kubernetes Engine (GKE), Anthos, BigQuery and data pipelines, as well as leading 3rd party tools like Prometheus, Datadog, and many others. Our SREs also work with languages like Python and Terraform to create automation, deploy infrastructure, and contribute to open-sourcing.

If you’re looking to continually build and apply your Google Cloud expertise to new and varied environments while acting as a key contributor to building the best Google consulting partner in the industry – let’s talk.

Note: Pacific and Mountain Time Zones preferred; This role has a weekend on-call rotation

Responsibilities

• Ensuring near-zero downtime with monitoring and alerting, self-healing automation, and continuous improvement
• Create highly automated, available and scalable systems by applying software and infrastructure principles
• Employ and advise clients on DevOps and SRE principles and practices, covering deployment pipelines, HA, service reliability, technical debt, and operational toil for live services running at scale
• Provide a proactive approach to our clients’ workloads, anticipating failures, automating tasks, ensuring availability, and providing a great customer experience
• Work closely with clients, your team, and Google engineers to investigate and resolve infrastructure issues
• Manage a Jira queue of inbound requests for numerous clients while effectively balancing and prioritizing projects
• Contribute to ad-hoc initiatives such as writing documentation, open-sourcing, and improving operation, making a huge impact at a rapid-growth Google Premier Partner

Qualifications

• Minimum 4+ years of cloud and infrastructure experience, including demonstrated expertise with Linux, Windows, k8s, databases, and networking services
• 2+ years of full-time Google Cloud experience preferred
• Proficiency with Python required. Other programming language experience is a plus
• Strong provisioning and configuration skills using Terraform
• Experience in troubleshooting that spans systems, network, and code
• Microsoft Server and SQL Server experience is a plus but not required
• Experience with 24x7x365 monitoring, incident response, and on-call support preferred
• Experience determining & negotiating Error budgets, SLIs, SLOs, and SLAs with product owners
• Demonstrate the ability to work independently and as a member of a greater team, including cross-team activities
• Experience working in Agile Scrum, Kanban methodologies in SDLC
• Proven experience balancing service reliability, metrics, sustainability, technical debt, and operational toil for live services running at scale
• Strong communication skills, as this is a heavily customer-facing role
• A Bachelor’s degree in Computer Science, Computer Engineering, or related or equivalent work experience required.

Your Role
As a Sr. Software Engineer in Tel Cloud, you will own the components that power our global communications systems. You will work on features like call routing, SMS/MMS messaging, spam detection, fraud detection, fleet deployments, porting, and number management, ensuring our platform remains scalable, reliable, and trustworthy.

This position reports to an Engineering Manager in the Telephony Platform team.

We don’t mind if you come from a different engineering background or haven’t worked in communications before. Just be excited to learn, contribute to the team, and build a fast, reliable, elegant product that delights our customers.

What you’ll do

• Design data models for heavy concurrency and real-time performance, ensuring sub-second latency for global call routing.
• Test and tune service scaling to ensure we always have capacity available to service our customers’ real-time communications.
• Build monitors and alerts to maintain high reliability and observability.
• Participate in releases and on-call rotations, driving operational excellence for the team's platform components.
• Design, write, and deploy features used by Dialpad engineering teams to communicate with carriers and third-party communication services.
• Strategize and drive the roadmap for cross-team platform initiatives.
• Serve as a champion, coach, and mentor to more junior engineers, promoting a culture of ownership and craftsmanship.

Skills you’ll bring  

• Bachelor's degree in Computer Science or equivalent practical experience.
• 5+ years of SaaS professional experience maintaining large-scale production backend systems.
• Demonstrated proficiency in coding with AI agents to accelerate development and improve code quality.
• Deep expertise in Python/Java/C and at least one cloud-native architecture (e.g., GCP).
• Familiarity with async and MapReduce design patterns, as well as NoSQL databases, distributed caches, and large-scale data lakes.
• Experience in profiling and optimizing backend code for low-latency requirements (p50/p90 metrics).
• A strong sense of urgency, personal agency, and customer empathy.
• Passion for data-driven decisions, building resilient systems, and solving classes of problems.

Our tech stack

• Backend: Python, Google Cloud Platform, Cloud Run / k8s, Datastore, Redis, ElasticSearch, BigQuery, Google Cloud Pub/Sub.
• Frontend: Angular / Bootstrap.

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

The Opportunity

We're looking for a Senior Software Engineer to join our Web Data team and help build the next generation of ZoomInfo's web crawling and data extraction infrastructure.

This is a hands-on engineering role with high impact. You'll work alongside experienced engineers building large-scale crawling and extraction systems that process billions of pages. Your day-to-day will involve solving real distributed systems problems, writing production code, and shipping features that directly impact data quality across the platform.

You'll partner with a dedicated people manager who handles HR and administrative responsibilities, a product manager who connects business needs with technical work, and a senior manager who removes roadblocks and supports career growth. Your focus is on engineering execution, technical excellence, and collaboration.

What You'll Do

As a Senior Software Engineer, you'll contribute to enterprise-scale crawling and extraction platforms that process massive volumes of web data.

You will:

• Design and implement components of scalable, fault-tolerant web crawling and extraction pipelines
• Write clean, production-grade code in Java and Python
• Build and operate ETL/ELT pipelines for large-scale data extraction and transformation
• Work with cloud infrastructure on GCP and AWS, primarily on GKE
• Improve observability, reliability, and operational excellence across the systems you contribute to
• Partner with product and data science teams to deliver impactful solutions
• Contribute to code reviews, documentation, and knowledge sharing across the team
• Stay current with evolving web technologies, anti-crawling mechanisms, and AI-powered extraction approaches

Must-Have Qualifications

We're prioritizing strong software engineering fundamentals over deep crawling-specific experience. The right candidate is a great engineer first; the domain can be learned on the team.

Software Engineering Fundamentals

• 5+ years of professional software engineering experience building production systems
• Strong CS fundamentals: algorithms, data structures, concurrency, distributed systems
• Proficiency in Java and/or Python
• Track record of owning features end-to-end from design through deployment and operation
• Comfortable making sound architectural decisions at the component level

Data Engineering

• Hands-on experience with cloud data warehouses such as BigQuery or Snowflake
• Experience designing and operating large-scale ETL/ELT pipelines
• Experience with orchestration tools such as Apache Airflow
• Experience with streaming or event-driven systems such as Apache Kafka

Cloud and Infrastructure

• Production experience on GCP (preferred) or AWS; multi-cloud exposure is a plus
• Hands-on experience with Kubernetes (GKE/EKS) for distributed workloads
• Familiarity with infrastructure-as-code tooling such as Terraform

Background and Mindset

• Strong communicator who can explain technical decisions clearly
• Comfortable operating in ambiguity and iterating quickly
• Bias toward action and pragmatic problem solving
• Self-starter who thrives in fast-paced, evolving environments

Nice to Have

• Experience with web crawling at scale (Scrapy or similar frameworks)
• Familiarity with proxy infrastructure, rotation strategies, or anti-bot evasion techniques
• Experience in extracting structured and unstructured web data from diverse site architectures
• Knowledge of SERP (Search Engine Results Page) extraction
• Comfort with AI/LLM-based extraction approaches, applying language models to HTML at scale
• Experience working in a B2B data company or data-as-a-product environment

Core Technical Stack

• Java and Python
• Apache Kafka
• GCP (BigQuery, GKE, Vertex AI)
• Snowflake and Starburst/Trino
• Terraform
• Scrapy and web scraping frameworks
• Proxy management systems
• Distributed systems and Kubernetes
• Apache Airflow
• Large-scale ETL pipelines

Ideal Profile

• 5+ years of software engineering experience
• Experience operating systems that process meaningful volumes of data
• Strong CS fundamentals (algorithms, data structures, distributed systems)
• Excellent communicator who can explain complex ideas to diverse audiences
• Passion for solving hard problems and building elegant, scalable systems
• Self-starter who thrives in fast-paced, evolving environments
• Experience working in a B2B data company or data-as-a-product environment is a strong plus

Why This Role Matters

This role sits at the heart of ZoomInfo's data platform. You'll contribute to the infrastructure that powers our business intelligence products and help shape how web data acquisition is done at ZoomInfo, while working at a massive scale with cutting-edge technologies.

You'll be joining at a pivotal moment: the team is growing, the ambition is high, and there's a real opportunity to make a lasting impact and grow into deeper technical leadership over time.

#LI-AR2

#LI-REMOTE

About impact.com
impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products - Performance (affiliate), Creator (influencer), and Advocate (customer referral) - unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands - including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics - rely on impact.com to power more than 350,000 partnerships that deliver measurable business results.

Your Role at impact.com:

We're seeking a Senior Data Scientist to serve as an embedded Data Scientist within our Programmatic Experience Group. You'll own the design and deployment of machine learning models that optimize yield, pricing, and inventory allocation at scale — sitting at the intersection of data science, platform engineering, and marketplace economics.

This is a high-craft, high-ownership individual contributor role. You'll work end-to-end: architecting the data pipelines that feed your models, engineering the features that drive performance, and deploying real-time inference systems that make decisions at speed. Your work directly determines how effectively Impact's programmatic marketplace balances advertiser performance with publisher monetization — making this one of the highest-leverage technical roles in the business.

You'll collaborate closely with Product, Data Science, and Programmatic Delivery Engine Engineering, but you operate with significant autonomy. You're expected to bring both the modeling rigor of a data scientist and the production instincts of an ML engineer — and to be genuinely excited about both.

What You'll Do:

Yield Optimization & Pricing Models

• Design and deploy ML models that optimize auction pricing, bid shading, floor price setting, and yield across Impact's programmatic inventory.
• Build and iterate on real-time pricing algorithms that balance short-term revenue efficiency with long-term publisher and advertiser health.
• Develop and maintain feedback loops that allow pricing models to adapt to shifting market conditions, inventory mix, and demand patterns.
• Quantify the revenue impact of pricing model improvements; communicate tradeoffs between yield maximization, fill rate, and partner ROI to stakeholders.

Inventory Allocation & Supply Optimization

• Own ML-driven inventory allocation logic: routing, pacing, and matching supply to demand across partner segments, deal types, and campaign objectives.
• Build models that forecast inventory availability, demand curves, and clearing prices to support proactive allocation decisions.
• Identify and address inefficiencies in inventory utilization — including unsold inventory, suboptimal deal matching, and allocation imbalances across the publisher base.

Data Architecture & Feature Engineering

• Design and own the data infrastructure that feeds programmatic models: event pipelines, feature stores, training datasets, and real-time feature serving.
• Engineer high-signal features from auction logs, bid stream data, user signals, contextual attributes, and historical performance — at the scale of programmatic data volumes.
• Build robust data pipelines with production-grade standards: reliability, observability, versioning, and efficient reprocessing.

Real-Time Inference & Production ML

• Deploy models to production real-time inference environments; own latency, reliability, and throughput requirements for auction-time decision-making.
• Build monitoring systems that track model performance, data drift, and system health in production; define alerting thresholds and retraining triggers.
• Partner with MLOps and Platform Engineering to ensure scalable, low-latency serving infrastructure meets SLOs under high-volume auction traffic.
• Own the full model lifecycle: training, evaluation, deployment, A/B testing, and iteration.

Experimentation & Performance Measurement

• Design and execute rigorous A/B and holdout experiments to measure the causal impact of model changes on yield, fill rate, advertiser performance, and publisher revenue.
• Build evaluation frameworks that go beyond offline metrics — validating model behavior in live auction environments where feedback signals are delayed or noisy.
• Translate experimental results into clear business narratives; present findings and recommendations to Product and business stakeholders.

Self-Learning Systems & Feedback Loops

• Research and implement adaptive, self-learning components within the programmatic stack — including contextual bandits, reinforcement learning signals, and online learning approaches where appropriate.
• Design feedback mechanisms that close the loop between auction outcomes, model updates, and system behavior; reduce reliance on manual tuning and rule-based overrides.
• Stay current with advances in programmatic ML, auction theory, and online optimization; evaluate applicability to Impact's specific marketplace dynamics.

Cross-Functional Collaboration

• Serve as the primary ML technical partner for the Rubicon product and engineering teams; translate business requirements into modeling approaches and communicate technical tradeoffs clearly.
• Collaborate with Data Science peers on shared infrastructure, modeling standards, and cross-domain feature reuse.
• Document models, architectures, and experimental findings to a standard that enables review, replication, and knowledge transfer across teams.

What You Bring:

Required

• Experience: 5+ years in data science, ML engineering, or quantitative research, with at least 2+ years building and deploying ML models in programmatic advertising, ad tech, marketplace optimization, or a closely related domain (e.g., real-time bidding, dynamic pricing, auction systems).
• Programmatic & marketplace depth: Demonstrated understanding of programmatic auction mechanics (RTB, header bidding, floor pricing, deal types, bid shading) and how ML can be applied to optimize outcomes across the supply-demand stack.
• Production ML engineering: Proven ability to take models from prototype to production independently — including real-time inference, monitoring, retraining pipelines, and SLO ownership.
• Data architecture: Experience designing and building data pipelines, feature stores, and training infrastructure for high-volume, low-latency ML systems.
• Technical skills:
• Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and large-scale data tools (Spark, Kafka, or equivalent streaming/batch frameworks).
• Experience with real-time feature serving and low-latency model deployment (REST APIs, gRPC, or streaming inference).
• Familiarity with production ML workflows: model versioning, drift monitoring, A/B testing, evaluation, and retraining.
• Experience processing and modeling at programmatic data scale: high-cardinality auction logs, bid stream data, impression and click events.
• Experimentation rigor: Strong grasp of causal inference and experiment design in online, delayed-feedback environments (auction holdouts, switchback tests, variance reduction techniques).
• Communication: Ability to explain complex modeling decisions and tradeoffs to Product and business stakeholders; comfortable presenting in cross-functional forums.
• Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, Economics, or similar); Master's/PhD preferred.

Preferred / Nice to Have

• Direct experience with SSP, DSP, or exchange-side yield optimization — particularly floor price optimization, bid landscape modeling, or deal matching algorithms.
• Familiarity with auction theory (first-price vs. second-price dynamics, optimal reserve pricing, revenue equivalence) and its practical implications for programmatic ML.
• Experience with contextual bandits, multi-armed bandits, or reinforcement learning applied to real-time decisioning problems.
• Knowledge of online learning and adaptive algorithms in production environments with non-stationary data distributions.
• Familiarity with privacy-preserving ML techniques relevant to programmatic (differential privacy, federated learning, cookieless attribution modeling).
• Experience with GCP tools (BigQuery, Vertex AI, Dataflow, Pub/Sub) and/or Databricks/Spark for large-scale event processing and model training.
• Exposure to supply forecasting, inventory management, or capacity planning in programmatic or marketplace contexts.
• Familiarity with Impact's affiliate and partnership ecosystem, or prior experience at the intersection of performance marketing and programmatic delivery.

What Sets You Apart

• Marketplace intuition. You understand programmatic auctions not just as an engineer but as an economist — you think about incentive structures, equilibrium dynamics, and how model decisions ripple through the supply-demand stack.
• Full-stack ML ownership. You're as comfortable designing a feature store schema as you are tuning a gradient boosting model or debugging a latency spike in production. You own the whole chain.
• Feedback loop thinking. You don't just deploy models — you design the systems that make them smarter over time. You think about how today's decisions become tomorrow's training signal.
• Rigor under real-world constraints. You know how to run clean experiments in environments where feedback is delayed, data is noisy, and business pressures create tradeoffs. You don't let imperfect conditions become an excuse for imprecise thinking.
• Pragmatic delivery. You ship. You balance the perfect with the production-ready, iterate fast, and know when an MVP outperforms a six-month research project.
• Collaborative depth. You build genuine technical trust with engineering and product partners — not just by having good ideas, but by following through, communicating clearly, and making the integration easy.

Salary Range: $165,000 - $185,000 per year, plus an additional 5% variable annual bonus contingent on Company performance and eligible to receive a Restricted Stock Unit (RSU) grant.

*This is the pay range the Company believes is equitable for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time.

Benefits and Perks: 

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Strong extended health benefits: Health & Prescription coverage, vision and dental care, virtual health care, out-of-country medical coverage
• Insurance coverage (life, short-term disability, long-term disability, and more)
• Health Care Spending Account
• Two different Employee Assistance Programs
• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses. 

Note: Benefits may vary by location and are subject to local regulations.

impact.com is proud to be an equal-opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

#LI_Vancouver_Canada

Synthesis Health

Who We Are

We’re a mission- and values-driven company with tremendous dedication to our customers. Our 100% remote team is dedicated to a common goal – to revolutionize healthcare through innovation, collaboration, and commitment to our core values and behaviors.

About the Opportunity

We are looking for an experienced Sr. Platform Engineer to help build the next generation of our cloud-native healthcare platform. You will be a key technical contributor, responsible for designing, building, and owning critical components that form the backbone of our systems. You will focus on improving the scalability, performance, and reliability of our services, enabling our product teams to deliver innovative features to our customers faster and more securely.

If you are a builder who thrives on solving complex distributed systems problems, enjoys mentoring others, and wants to make a significant impact on healthcare, this is the perfect opportunity for you.

Key Responsibilities

• Design and Build Core Platform Services: Design, develop, and maintain scalable, resilient, and secure platform components and microservices that are critical to project success and product capabilities.
• Drive Performance and Reliability: Promote and implement best practices for observability, monitoring, and performance testing. You will analyze and optimize application-level performance for our critical workflows, ensuring they meet strict uptime and speed requirements.
• Improve System Architecture: Contribute to the analysis of our platform architecture, helping to identify performance bottlenecks, improve data and caching flows, and simplify architectural complexity.
• Elevate Code Quality: Write clean, maintainable, and well-tested code for core platform services. You will set a high standard for code quality and testing within the team by leading design reviews and providing constructive feedback.
• Lead with Technical Expertise and Mentorship: Formally or informally mentor junior engineers, helping to level up the team through code reviews, knowledge sharing, and hands-on guidance. You will significantly influence your team's technical direction and decisions.
• Create Excellent Documentation: Produce clear, high-quality architectural diagrams and technical documentation for the components you own, contributing to the team's shared knowledge base.

What We’re Looking For

• Experience in Distributed Systems: 5+ years of software engineering experience with a strong track record of designing, building, and operating scalable and high-performance services.
• Strong Architectural and Design Skills: You can independently tackle complex technical challenges within your domain, architecting robust solutions and making sound technical trade-offs with minimal guidance.
• Performance and Debugging Expertise: You have hands-on experience instrumenting and debugging performance issues across a full stack—from application code and databases to caching layers and infrastructure—using modern performance profiling and monitoring tools.
• Hands-On Expertise with Modern Tech Stacks: You have practical knowledge of the principles and technologies behind our platform:
  • Microservices, event-driven design, and distributed architectures.
  • Cloud platforms (GCP preferably), with hands-on experience in Docker and Kubernetes (e.g., GKE, Cloud Run).
  • Expertise across relational and NoSQL databases (e.g., PostgreSQL/AlloyDB, MongoDB, Redis) and data warehousing (e.g., BigQuery).
  • Real-world experience with Pub/Sub messaging systems (e.g., Google Cloud Pub/Sub, Kafka).
  • Statically typed languages (e.g., TypeScript, Go, C/C++, Java, Rust).
  • Gitflow branching model.
• Clarity in Communication: You can clearly articulate complex technical ideas to both technical and non-technical stakeholders. Your diagrams and documents are a source of clarity.
• Leadership Through Influence: You have a demonstrated ability to influence your team's technical direction, driving consensus, and elevating the bar for engineering excellence.

Preferred Qualifications

• Healthcare Domain Experience: While engineering excellence is paramount, your application will stand out if you have experience in regulated industries. Deep familiarity with healthcare data standards (DICOM, HL7) and compliance regulations (HIPAA) is a significant advantage.

Why You Should Join Us

• Solve Challenging Problems: This is a high-leverage role. You will be working on impactful technical challenges that are critical to the company's success.
• Build the Future of Healthcare: You won't just be maintaining a system; you will be a primary builder of its future state, with the autonomy to own major components.
• Make a Major Impact: This is a chance to establish yourself as a key technical voice on a team that is transforming healthcare.
• Competitive Compensation & Benefits: We offer a strong salary, meaningful equity, a 100% remote culture, and significant opportunities for growth.

We are a values-driven company. Our values:

1. Clinical service first.
2. Collaborate with our customers.
3. Listen, respect, learn.
4. Innovate to excel.

The behaviors we look for:

1. Be nice.
2. Be creative.
3. Be honest.
4. Be helpful.

Compensation and Benefits

Typical salary range for this position is $85,000 - $110,000. However, Synthesis participates in location based hiring and salary ranges can be adjusted based on candidate's residence. 

Other benefits include, but are not limited to: Medical, Dental, Vision, “Use as needed” vacation policy, and participation in our employee option program.

Synthesis Health

Who We Are

We’re a mission- and values-driven company with tremendous dedication to our customers. Our 100% remote team is dedicated to a common goal – to revolutionize healthcare through innovation, collaboration, and commitment to our core values and behaviors.

About the Opportunity

We are looking for a Staff Data Engineer who serves as the universal data architect for our platform.

In this role, you will bridge the gap between application engineering and data infrastructure. You will possess a "big picture" understanding of our entire data ecosystem—OLTP, OLAP, NoSQL, and Relational. You will own the optimization of our high-volume data pipelines, but equally, you will tune the operational databases that power our services and design the warehousing strategies that drive our analytics.

You will be the authority on data physics: defining how we ingest massive bursts of medical information, how we model it for high-performance transactional locking, and how we transform it for analytical querying without friction.

This is a high-leverage leadership role. You will set the standard for data engineering excellence, mentoring Senior engineers and defining the architectural patterns that keep our platform performant as we scale 100x and beyond.

Key Responsibilities

Polyglot Data Architecture & Performance

• Universal Store Optimization: You will be the ultimate authority on the performance of our persistent stores. You will tune AlloyDB (PostgreSQL) for complex transactions (OLTP), optimize NoSQL layers, and structure BigQuery for analytical speed (OLAP).
• Prevent Congestion & Latency: You will proactively identify and resolve "hot spots" in our data architecture. You will design strategies for sharding, partitioning, and active-archiving that ensure our operational systems remain lean while our analytical history grows indefinitely.

High-Volume Pipeline & Ingestion Strategy

• Ingestion at Scale: You will architect robust, backpressure-aware pipelines to handle high-throughput ingestion of HL7 and DICOM metadata. You will ensure our systems can absorb bursty traffic without degrading the user experience.
• Stream & Batch Convergence: You will design the flows that move data from operational stores to our data warehouse, ensuring freshness and consistency. You will determine when to use real-time streams versus batch processing to balance cost and latency.

Service Design & Data Governance

• Define Data Contracts: You will act as a key voice on the Architecture Review Board (ARB), defining the "rules of the road" for how microservices interact with the data layer. You will enforce strict data contracts that decouple services from the underlying storage engine.
• Mentorship: You will elevate the data engineering capabilities of the entire organization. You will mentor other engineers on advanced query optimization, data modeling (Star Schema vs. 3NF), and the nuances of distributed data consistency.

What We’re Looking For

• Elite Data Engineering Experience: 8+ years of experience designing and optimizing data-intensive applications. You have a track record of solving hard scaling problems across the entire data lifecycle.
• Universal Data Fluency: You have deep expertise across the spectrum: Relational (AlloyDB/PostgreSQL internals, MVCC, locking), NoSQL (Document stores, Key-Value), and Warehousing (BigQuery/Snowflake internals, columnar storage).
• Pipeline Proficiency: Expert-level knowledge of building and optimizing data pipelines using tools like Kafka/PubSub, Beam/Dataflow, or dbt. You understand how to handle late data, duplicates, and exactly-once processing.
• Service & System Design: You understand how data stores fit into a broader microservices architecture. You can design synchronous vs. asynchronous data access patterns that protect the database from application-layer thundering herds.
• Coding Skills: Strong proficiency in Python, Java, SQL. You write clean, maintainable code for infrastructure and data transformation.
• Performance Obsession: You have a proven ability to debug complex data performance issues, from tuning query execution plans and resolving lock contention to optimizing high-throughput ingestion pipelines for minimal lag.

Preferred Qualifications

• Healthcare Data: Familiarity with the specific challenges of healthcare data (DICOM hierarchy, HL7 message structures) and compliance (HIPAA).
• Real-Time Analytics: Experience architecting real-time analytics dashboards or hybrid transactional/analytical workflows.
• Migration Experience: Experience leading zero-downtime database migrations or major schema refactors in a 24/7 environment.

Why You Should Join Us

• Solve Our Toughest Puzzles: This is a high-leverage role. You will be working on the most impactful technical challenges that are critical to the company's success.
• Define the Architecture: You won't just be maintaining a system; you will be a primary author of its future state, with the autonomy to make it happen.
• Lead from the Front: This is a chance to establish yourself as a key technical voice in a rapidly growing company.
• Competitive Compensation & Benefits: We offer a strong salary, a 100% remote culture, and significant opportunities for growth.

We are a values-driven company. Our values:

• Clinical service first.
• Collaborate with our customers.
• Listen, respect, learn.
• Innovate to excel.

The behaviors we look for:

• Be nice.
• Be creative.
• Be honest.
• Be helpful.

Compensation and Benefits

Typical salary range for this position is $120,000 - $150,000 (CAD). However, Synthesis participates in location based hiring and salary ranges can be adjusted based on candidate's residence. 

Other benefits include, but are not limited to: Medical, Dental, Vision, “Use as needed” vacation policy, and participation in our employee option program.

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 

Location: Remote (US or Canada)
Type: US Applicants - Full-Time; Canadian Applicants - Independent Contractor

About Us

We’re scaling rapidly and have a growing pipeline of opportunities that demand exceptional talent across disciplines. Our mission is to bring on individuals, from creative producers to technical experts to entrepreneurial leaders, who can help us realize this next chapter of growth.

We are a company of doers. Leaders roll up their sleeves, teams work flat, and everyone contributes to what ships. Titles don’t insulate us from feedback or basics. We invite critique, learn quickly, and keep raising the bar. The best ideas win here, no matter where they come from, because clients trust us to deliver the strongest outcomes every time.

Our clients’ missions, products, and bottom lines are sacred. We immerse ourselves in their world, becoming stewards of their goals and partners in solving big problems. Every product, strategy, or asset we create must be both beautiful and functional; practical, usable, and designed for real-world impact.

Humans are our most valuable resource, and we only grow by hiring people who push us forward. Across strategy, engineering, design, data, and operations, we seek out teammates who raise the bar and make us better. Always hire up, never down.

We partner with organizations of all sizes to explore, design, and implement AI strategies that are secure, scalable, and human-centered. We believe AI should amplify human potential, not replace it and we build with that conviction in every engagement. From advisory and tooling to implementation and education, we meet clients where they are at and help them integrate AI in ways that align with their mission and values. Our goal is to empower teams to work smarter, move faster, and unlock new possibilities through thoughtful, responsible innovation.

And through it all, we lead with purpose, love, and adventure. We do meaningful work with people we care about, and we make the ride an adventure worth taking. Because at Human Agency, who we are and how we work are one and the same.

About the role

We’re hiring a Product Lead to join our team and embed directly inside one of our client organizations — a leader in design-build. This role is for a hands-on product builder who thrives on embedding with teams, understanding their workflows, and delivering AI-powered software that drives real value. 

This is not a “strategy-only” role. You’ll spend your time with users, engineers, and executives alike — uncovering needs, setting priorities, and getting solutions shipped. You’ll bring modern product practices into a complex, real-world industry, while raising the bar for excellence in how software gets built.

What you’ll do

Discovery & Prioritization

• Embed with client teams to observe workflows, run discovery, and translate insights into actionable software requirements.
  
  
• Develop and apply structured prioritization methods (balancing user value, business outcomes, and technical feasibility) to guide trade-offs.
  
  
• Keep teams focused on the highest-leverage opportunities, not just the loudest requests.
  
  

Delivery & Execution

• Scope projects with realistic estimates and clear milestones that deliver value early and often.
  
  
• Know how to ship a prototype, gain feedback, iterate to value, and release production-grade solutions.
  
  
• Lead small pods of engineers and operators to ship high-quality, on-time solutions.
  
  
• Facilitate leadership updates and reporting, ensuring execs are aligned and confident in progress.
  
  
• Above all, be able to roll up your sleeves to solve the problem in a way that advances us and the organization.
  
  

AI & Technical Fluency

• Work directly with APIs, data pipelines, and AI models to shape and validate solutions.
  
  
• Partner with engineers to design and ship AI-driven tools that fit seamlessly into existing workflows.
  
  
• Establish best practices for evaluating tools — weighing accuracy, usability, and long-term fit.
  
  
• Stay current on AI capabilities and help translate them into practical applications inside the client’s environment.
  
  

Culture & Excellence

• Raise the bar for product craft, from clear scopes to clean communication and transparent evaluations that anyone can quickly understand.
  
  
• Codify lessons into playbooks so the client builds lasting product capability.
  
  
• Build a culture of excellence: sharing wins, measuring outcomes, celebrating progress, and owning areas of improvement.
  
  

Minimum qualifications

• 8+ years of product management experience, ideally building tools for complex workflows or mission-critical environments.
  
  
• Proven ability to balance discovery and delivery — finding the right problems and shipping solutions that stick.
  
  
• Experience leading small engineering teams or pods with high accountability.
  
  
• Consulting mindset: able to embed inside client organizations, guide practices, and earn trust quickly.
  
  
• Excellent communicator who can flex for end users, engineers, and executives.
  
  
• Comfortable working with APIs and data pipelines — able to shape requirements, validate inputs, and build against structured data.
  
  

• Experience with modern data platforms, ideally Snowflake (or equivalent warehouses like BigQuery, Redshift).
  
  
• Able to make smart build choices with AI — knowing when to call APIs, when to use custom GPTs, and how to combine them into solutions that actually work for teams.
  
  
• Operates confidently in cloud environments, especially Microsoft Azure and AWS ecosystems.
  
  
• Bonus: familiarity with enterprise integrations (e.g. Tableau) to connect insights into user workflows.
  
  
• Above all, be someone that people admire, because you’re having fun doing great work.
  
  

Why this role is different

Most product roles optimize features in a SaaS app. This one is about embedding with real teams across architecture, engineering, and construction, understanding how they work, and building AI-powered tools that change how projects get designed and delivered in the real world. You’ll see your work land quickly, and you’ll set the playbook for how we approach product for years to come. 

Equal Opportunity Commitment

Human Agency is an Equal Opportunity Employer. We value diverse backgrounds and strive to build an inclusive culture where everyone feels welcomed and empowered.