#LI-Hybrid

Our Database Experience (DBX) Team

The Database Experience team builds the libraries and tools that developers use day-to-day working with MongoDB. Our mission is to increase developer adoption, satisfaction and retention by providing a reliable, enjoyable interface for developers and other end-users.

For this role, we're looking for someone who would enjoy designing, writing, and supporting open-source libraries using MongoDB. You will have the opportunity to work with other veteran Java programmers, as you deepen your knowledge of the Java ecosystem, and help streamline and increase the adoption of MongoDB.

We are looking to speak to candidates who are based near one of our Canada office for our hybrid working model.

You might be right for this role if you

• Have 4+ years of experience writing high-quality software
• Have experience implementing libraries or applications in Java, or other languages that run on the JVM
• Are fascinated by, and have experience working with concurrency, parallelism and distributed systems
• Want to participate in open-source software development and communities
• Communicate well, internally and externally, both in writing and face-to-face
• Advocate for testing and modern development best practices
• Can make pragmatic design tradeoffs while balancing features, maintainability, and delivery time
• Elevate the skills and expertise of those around you
• Are excited by our mission and values

 You'll be on the team responsible for

• Java driver, Java Reactive Streams Driver, Scala Driver, Kotlin Driver, Spark Connector, and other JVM-based projects
• Integrations with other open-source software, such as ORM frameworks and AI tools
• Testing, delivering and supporting the above across multiple operating systems, JDKs, and MongoDB releases

You'll have responsibilities beyond writing code, including

• Working with our Product, Server, Cloud, and other driver teams on designs and specifications for new customer-facing MongoDB features
• Investigating bugs and support requests from commercial and open-source customers for the libraries and tools our team maintains
• Seeking out speaking or writing opportunities to engage with our developer community
• Mentoring junior engineers in both technical and non-technical professional skills

Success in this role means

• Within three months, you will contribute effectively day-to-day, fix bugs, implement planned features, and thoroughly review your colleagues’ pull requests
• Within six months, you will confidently lead projects, provide mentorship and technical guidance to colleagues, and contribute to process improvements
• Within a year, you will demonstrate strong technical ownership, influence team and product growth through effective communication and collaboration, and contribute to high-impact product enhancements

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform—the most widely available, globally distributed database on the market—helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and nearly 60,000 customers—including 75% of the Fortune 100 and AI-native startups—relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB.

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB is an equal opportunities employer.

Req ID: 3263228272

The Payment Platform Team powers Asana’s subscription, billing, and payment infrastructure — the systems responsible for processing all revenue and enabling how we monetize the product. Our mission is to enable engineering teams to safely and rapidly build on billing capabilities, while empowering business teams to launch and iterate on pricing and packaging strategies.

Our work sits at the intersection of every major revenue lever at Asana — from incentives that drive user growth, to the product catalogue that defines how we package and sell, to the core billing systems through which every dollar of revenue flows. This gives the team a unique opportunity to directly influence both product strategy and business outcomes. Engineers on this team have the opportunity to shape architecture, reliability, and long-term scalability of revenue-critical systems.

This role is based in our Vancouver office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements.

What you’ll achieve

• Design, build, and maintain backend services using Scala and gRPC within a modern microservices architecture to support Asana’s global growth.
• Own features end-to-end, from initial problem definition and design to implementation, launch, and iteration.
  Lead large, cross-team initiatives from ambiguous problem spaces to execution, driving key architectural decisions for revenue-critical systems.
• Collaborate closely with cross-functional stakeholders in Product, Design, Finance, and partner engineering teams to deliver high-impact monetization solutions.
• Ensure systems are reliable, scalable, and performant, specifically focusing on evolving cell-based distributed architectures.
• Mentor fellow engineers and contribute to a high-performing technical culture by participating in design and code reviews.

About you

• Strong experience building and scaling backend or full-stack systems, ideally with distributed systems or complex data models.
• Proven ability to take full ownership of technical problems and drive them to resolution across the entire stack.
• Excellent collaboration skills with the ability to influence both engineering and non-engineering partners.
• Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.
• Experience with (or a strong interest in) billing, payments, or financial systems and cloud infrastructure like Kubernetes.
  Thrives in an environment where you can evolve cell-based architectures, sharded systems, or multi-tenant distributed systems.

What we’ll offer

Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit.

For this role, the estimated base salary range is between $176,000 - $200,000. The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. In addition to base salary, your compensation package may include additional components such as equity and benefits.

We strive to provide equitable and competitive benefits packages that support our employees worldwide and include:

• Mental health, wellness & fitness benefits
• Career coaching & support
• Inclusive family building benefits
• Long-term savings or retirement plans
• In-office culinary options to cater to your dietary preferences

#LI-Hybrid #LI-HC1

We’re looking for a Lead, Software Development to help us build and lead a high-performance team that solves challenging engineering problems as we build the next generation of social products. You will support the Senior Manager, Development to hire, grow, coach, and lead a team of developers to build, scale, monitor, and optimize our billing infrastructure working on account management, pricing and packaging, subscription lifecycle, compliance, payment processing and more. While based in Canada (in locations where we can legally hire), you will report to the Senior Manager, Software Development. 

WHAT YOU’LL DO: 

• Build, support and lead a high performance, cohesive team of developers, in close partnership with the Senior Manager, Development
• Facilitate ongoing development conversations with your team to support their learning and career growth
• Proactively share information, actively solicit feedback, and facilitate communication, within teams and portfoliosParticipate in the hiring process, with an aim of attracting and hiring the best developers
• Assess and prioritize the team's work, appropriately delegating to others and encouraging team ownership.
• Maintain high standards of security, reliability, scalability, performance, and quality in all delivered projectsHelp shape our technical roadmap as we scale our services and build new products
• Design, write, test, and deploy high quality scalable code
• Work with the following technologies: Go, Scala, PHP, Docker, Jenkins, Kafka, Redis, Cassandra in an environment that’s using NoSQL databases, Domain Driven Design, Event Sourcing, Test Driven Design and Agile

WHAT YOU’LL NEED: 

• A degree in Computer Science or Engineering, and at least 5 years of experience in designing, developing and maintaining software or an equivalent level of work experience, and a track record of substantial contributions to software projects with high business impact
• You are proficient in Go, Scala or a similar language and have a desire to learn other languages
• Understanding of distributed systems and how to work in high availability environments
• Experience with mentoring, coaching, and pairing with coworkers
• An understanding of SaaS software development practice, previous experience working on a SaaS product will be viewed favorably
• Collaboration and Teamwork: works with others to deliver results, meaningfully contributing to the team and prioritizing group needs over individual needs
• Creativity and Innovation: seeks new and better ways of doing things, generating original and imaginative ideas, products, and/or solutions
• Problem Solving: uses an organized and logical approach to find solutions to complex problems and looks beyond the obvious to understand the root cause of problems and proposes several possible solutions
• Resilience, Tolerance for Change/Ambiguity: can effectively cope with change, finding ways to advance work and projects

WHO YOU ARE:

• Tenacious. You are determined to succeed, and you are motivated by the success of customers, colleagues and the community
• Curious. You are always learning and seeking ways to make things better
• Conscientious. You keep your promises, taking your commitments to others seriously, and you have strong integrity
• Humble. You lead with humility and empathy, respecting and learning from the perspectives of others

Share our values: We champion the power of human connection. We’re united globally by our shared values of innovation, grit, humility, and passion for customer success. 

Make an impact: Working at the speed of social, we create value for our customers by delivering solutions that power relationships at scale. Our pace of work enables fast learning and fosters an environment where you can stretch yourself and make an impact.

Learn and grow: We’re committed to growing the capabilities of our people. We are building a learning community where you can work with diverse individuals, explore new ways of thinking, and expand your capabilities. Our employees are teachers and learners who work out loud and share their knowledge to enhance each other’s growth.

We are dedicated to building a diverse community, one where employees feel a sense of belonging, and are valued for their contributions and the perspectives they bring. Our purpose is to champion the power of human connection and the heart of connection is inclusion. You belong here.

Accommodations will be provided as requested by candidates taking part in all aspects of the selection process.

#LI-NS1

We’re looking for a Junior Software Developer who can design, build, test, and ship high quality software that delivers value to our customers. You’ll work on a small and agile team that continuously incorporates feedback to refine and improve the products and features you deliver. You will learn and follow established patterns and best practices and be supported in delivering high quality work as part of the team. The team will own the entire delivery lifecycle from beta releases to production ready, scalable products suitable for new and existing customers. This job is open to candidates across Canada, in provinces where we can legally hire and will report to the Senior Manager, Software Development.

WHAT YOU’LL DO:

• Deliver low-complexity projects ensuring alignment with coding and design standards, testing practices, and company guidelines for security, accessibility, and privacy.
• Contribute to the successful execution of the team’s roadmap by asking questions, providing input, and promptly escalating identified risks and scope changes.
• Actively engage in team activities, working out loud, learning from peers, contributing to code reviews and documentation as you work towards achieving team goals.
• Actively engage in troubleshooting, addressing minor bugs independently, and collaborating with senior team members to resolve more complex issues.
• Work within a cross-functional team, fostering positive team relationships, and maintain effective communication with peers and stakeholders. 
• Use modern web technologies to have a positive impact on developer workflows and infrastructure
• Repositories that are majority Typescript, and React and are depended upon by other development teams across the company (with exposure to a variety of other technologies such as Scala and GO services). 
• Work with Jenkins, our CI/CD platform, to improve developer experience and support delivery of a reliable, high-quality product—contributing to pipeline maintenance, optimization, and automation efforts.
• Assist in the investigation and execution of technical initiatives that drive meaningful, sweeping improvements across our software development lifecycle—helping the team move faster and ship with greater confidence.
• Engage with our dependency management tooling and internal package ecosystem to keep our codebase secure, current, and well-maintained—contributing to decisions around library adoption, upgrades, and deprecation.
• Gain broad, hands-on exposure to the full spectrum of tools, technologies, and architectural decisions that underpin modern front end development—think of it as front end DevOps, where you’re as comfortable with the build pipeline as you are with the product itself.

WHAT YOU’LL NEED:

• A degree in Computer Science or Engineering, and 1-2 years of experience developing and maintaining software (including co-op terms and internships) or an equivalent level of education or work experience.
• Learn and follow existing patterns and best practices in delivering code and writing tests for individual stories.
• Ability to learn about and use root cause analysis to identify and address bugs and outages. Follow logging, monitoring and debugging best practices in the components and classes you work on.
• Learn and follow existing architecture concepts and best practices to extend existing software modules and components that meet system performance, modifiability, maintainability and reliability needs.
• Learn about and follow basic security and compliance practices in daily work.
• Follow common agile processes and continuous integration and delivery practices to ship stories and own low complexity epics/projects.
• You can collaborate and communicate effectively within a team. Participate in technical discussions, listen actively and understand non-technical input from design and product.
• Actively seek guidance, feedback, and learning opportunities within the team to foster personal and professional growth.
• Display eagerness to learn and collaborate on goal-setting. Supported in creating and aligning goals with personal development.
• Understand roadmaps and participate in delivering on them effectively by communicating risks and opportunities at the individual level.
• Customer Focus: demonstrates a desire to proactively help and serve internal/external customers meet their needs
• Perseverance: pursues everything with energy, drive, and a need to finish—doesn’t give up
• Resilience, Tolerance for Change/Ambiguity: can effectively cope with change, finding ways to advance work and projects

WHO YOU ARE:

• Solution seeker. You’re focused on tackling new challenges, solving problems, and moving the business forward—and you don’t wait to be asked.
• Lifelong learner. You have a growth mindset – you’re here to learn, experiment, seek, apply, and provide feedback, share what works with your team, and move on from what doesn’t.
• Resilient adapter. In the face of change and challenges, you bring a thoughtful, calm approach, and a focus on finding the new opportunity.
• Intentional collaborator. You build positive working relationships across the business, bringing people together to foster new opportunities and to facilitate the efficient flow of information.
• Critical challenger. You have the trust in your team to ask difficult questions in order to get to the best end result.
• Active communicator. You listen actively and communicate ideas and information clearly, inclusively, and proactively.
• Integrated thinker. You look beyond your role and responsibilities to understand how your team’s work drives broader organizational goals.
• Accountable owner. You take pride in the work you’re responsible for with a mindset of ultimate accountability and reliability for the outcomes.
• Bar-raiser. You step up to help your team grow and succeed, even when that means going beyond what might be expected.

In all we do, our six guiding principles light the way:

Step Up: Dare to go beyond the expected to achieve greatness. #StepUp

One Team: Make Hootsuite a place we soar together by respecting each other's individuality, building trust, and showing up for the team. #OneTeam #FreeToBeMe

Customer Obsessed: Focus relentlessly on helping our customers succeed. #CustomerObsessed

Go Fast, Be Agile: Widen our competitive advantage by committing to speed and simplicity over perfection and complexity. #GoFastBeAgile

Play to Win: Commit to building an incredible, profitable company for our customers, our employees, and our stakeholders. #PlayToWin #NoExcuses

Neighbours & Allies: Give back to our communities and be an ally. #SocialForGood #Allies

Accommodations will be provided as requested by candidates taking part in all aspects of the selection process.

This posting is for a current vacancy for which we are actively hiring.

#LI-AD #LI-Remote

The Data Engineering team’s mission is to ensure high-quality data to enable data-informed decision-making across Asana. You will build data artifacts that are leveraged by Product and Business Data Science teams to optimize our user adoption, growth, and experience. In this role, you will partner with the Infrastructure team to build a self-service analytics platform for the company. This role is based in our Vancouver office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday; most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements.

What you’ll achieve

• Design, implement, and scale end-to-end data products that support growing data processing and analytical needs
• Transform raw data into actionable insights to drive product strategy and power in-depth analyses and reporting
• Leverage AI to build self-serve tools and accelerate Data/GTM workflows
• Partner with data scientists, domain experts, and engineering teams to develop a roadmap that aligns with our business goals
• Implement systems that guarantee data quality, governance, and availability

About you

• 5+ years of experience in Data Engineering or Software Engineering
• Experience in data modeling and building scalable data pipelines involving complex transformations
• Proficiency in data processing and storage technologies like Databricks, AWS/S3, Python/Scala/Java, SQL, Spark, and Airflow
• Proactive and innovative in identifying and addressing performance bottlenecks in existing workflows
• Motivated to work closely with cross-functional partners to evolve our analytical data model
• Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making

At Asana, we're committed to building teams that include a variety of backgrounds, perspectives, and skills, as this is critical to helping us achieve our mission. If you're interested in this role and don't meet every listed requirement, we still encourage you to apply.

What we’ll offer

Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit.

For this role, the estimated base salary range is between CAD $138,000 - $168,000. The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. The listed range above is a guideline, and the base salary range for this role may be modified. In addition to base salary, your compensation package may include additional components such as equity, sales incentive pay (for most sales roles), and benefits.

We strive to provide equitable and competitive benefits packages that support our employees worldwide and include:

• Mental health, wellness & fitness benefits
• Career coaching & support
• Inclusive family building benefits
• Long-term savings or retirement plans
• In-office culinary options to cater to your dietary preferences

#LI-Hybrid

At Asana, we believe AI represents the future of work, and APIs are at the heart of how AI connects with the tools where work happens. Asana’s API team owns the gateways that enable data flow into and out of Asana, whether via the public API used to build new apps and agents or our MCP server that enables LLMs to take action in response to natural language. Asana’s rich developer platform and app ecosystem help ensure we can support every use case out there–no matter how big or small the team, and no matter which tools or workflows they use.

We’re looking for a staff-level Technical Lead to drive execution and technical vision for our developer platform. Our API team has huge, cross-cutting scope: one day you’ll be partnering with Product to define what a great agentic experience looks like and contributing to the direction of industry-wide protocols, the next you’ll be setting best practices for exposing new Asana functionality via the public API and architecting foundational changes to our API’s core feature set and frameworks, all while mentoring the engineers and developer advocates on the team. If you care deeply about building an agentic future for the enterprise and leading high-performing engineering teams, then we’d love to hear from you!

This role is based in our Vancouver office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements.

What you’ll achieve

• Set the technical strategy for the API platform within Asana and advocate for engineering-driven investments, with a vision for keeping our systems flexible and maintainable
• Drive the team to continually and holistically better itself, enabling the engineers around you to deliver at a high velocity while helping them grow their skills in the process
• Be a key collaborator with other groups in Engineering and a technical counterpart to the API team’s Engineering Manager, serving as a public face for API engineering and representing our interests across team boundaries
• Partner with Product Management, Design, Data Science, and User Research to deeply understand developer and agentic needs, and then propose elegant solutions to meet them
• Own and shape the guidelines for what good API design looks like, partnering with product engineering teams to help them implement those best practices in a self-serve manner
• Develop across the entire stack to make developers’ experiences better by writing clean, beautiful code and leaving things better than you found them: this includes writing Scala, Javascript, and Typescript

About you

• 6+ years of experience working in large codebases
• 2+ years leading engineering teams
• 1+ years building public-facing API products or Developer Platforms
• Excellent communication, collaboration, and delegation skills
• Quick learner and comfortable transitioning between different contexts and codebases, enabling you to jump in and rapidly unblock teammates facing thorny obstacles
• Deep project management experience including high-level timeline planning, risk mitigation, stakeholder management, and coordinating the work of others
• Passionate about mentoring engineers at all skill levels; able to identify technical growth opportunities and deliver thoughtful, candid feedback
• Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.
• Excited to be a part of an inclusive culture where everyone brings the aspects of themselves to the workplace that they need to thrive, fostering an environment of psychological safety within your team

At Asana, we're committed to building teams that include a variety of backgrounds, perspectives, and skills, as this is critical to helping us achieve our mission. If you're interested in this role and don't meet every listed requirement, we still encourage you to apply.

What we’ll offer

Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit.

For this role, the estimated base salary range is between $211,000-$240,000. The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. The listed range above is a guideline, and the base salary range for this role may be modified.

In addition to base salary, your compensation package may include additional components such as equity, sales incentive pay (for most sales roles), and benefits. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

We strive to provide equitable and competitive benefits packages that support our employees worldwide and include:

• Mental health, wellness & fitness benefits
• Career coaching & support
• Inclusive family building benefits
• Long-term savings or retirement plans
• In-office culinary options to cater to your dietary preferences 

These are just some of the benefits we offer, and benefits may vary based on role, country, and local regulations. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

#LI-Hybrid

The Product team builds features end-to-end. From designing our data models to implementing the subtle interaction behaviors that differentiate good software from great software. We work closely with UI designers and are supported by our infrastructure team. We aim to delight users with both large new features and smaller, daily product enhancements—thanks to our continuous deployment architecture. We want to create a superlative user experience, down to the smallest details.

We are looking for a Staff Software Engineer to join the Admin Foundations team in our Vancouver Office. The team plays a vital role in developing tools for Administrators to manage their Asana instance at Scale while establishing frameworks/patterns for Asana engineers to easily create admin controls for the features they develop.

Being part of this team will provide you with the opportunity to get a deep understanding of Asana’s administrative controls and permissions. This comes with an exposure to various other surfaces of the product that will enable you to work with different teams. You will also be working across different levels of our technical stack, from frontend tools like React to backend systems like the remote jobs framework. 

This role is based in our Vancouver office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements.

What you’ll achieve:

• Work in a high-performance, dynamic team with a strong focus on velocity and software quality
• Contribute to building a complex SaaS application that is a market leader in its segment 
• Work closely with Product Managers, Designers and Engineers to deliver delightful software and tools for customers to help administer their Asana instance at scale.
• Drive technical strategies that cross over other team surfaces and align with such teams and stakeholders to get buy-in and implement such strategies.
• Make significant individual contributions by developing across the entire stack and leaving things better than you found them – this includes writing Typescript, Javascript and occasionally Scala.
• Drive large, impactful projects, delivering features that will be rolled out and used in large organizations

About you:

• Have 5+ years of software development experience with 2+ years mentoring/coaching other engineers on impactful software projects.
• Experience working in ambiguous and complex technical spaces and creating clarity and alignment with partners and stakeholders.
• Have a track record of designing and implementing solutions to complex business problems. You have skillfully influenced and got buy-in from stakeholders and other teams to adopt/implement these solutions.
• Comfortable and autonomously debugging and fixing tricky issues in various parts of the software stack, particularly on the backend.
• Have a strong breath of knowledge of software concepts and design patterns that you can comfortably apply to make technical solutions scalable and robust.
• Have experience driving projects of medium to large complexity.
• You have a continuous improvement mindset and are never satisfied with the status quo, whether it's about the architecture, the code or the team's processes
• Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.

At Asana, we're committed to building teams that include a variety of backgrounds, perspectives, and skills, as this is critical to helping us achieve our mission. If you're interested in this role and don't meet every listed requirement, we still encourage you to apply.

What we’ll offer:

Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit.

For this role, the estimated base salary range is between $211,000 - $240,000 CAD. The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. The listed range above is a guideline, and the base salary range for this role may be modified.

In addition to base salary, your compensation package may include additional components such as equity, sales incentive pay (for most sales roles), and benefits. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

We strive to provide equitable and competitive benefits packages that support our employees worldwide and include:

• Mental health, wellness & fitness benefits
• Career coaching & support
• Inclusive family building benefits
• Long-term savings or retirement plans
• In-office culinary options to cater to your dietary preferences 

These are just some of the benefits we offer, and benefits may vary based on role, country, and local regulations. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

#LI-Hybrid

We're looking for a Staff Data Scientist to help define Asana's growth strategy as users increasingly discover and adopt software through LLM interfaces like ChatGPT, Claude, and Gemini. With millions of knowledge workers now starting their workday in conversational AI tools, we're at an inflection point in how productivity software reaches users. You'll drive Asana's acquisition playbook for the emerging LLM-driven channels and re-imagine new customer onboarding in the AI-era. As part of the PLG organization, you'll leverage data to influence how new users discover, learn about,  and decide to start using and paying for Asana.

Asana's Data Science team helps us fulfill our mission by informing strategy, defining success metrics, and identifying new ways to deliver user value. Data scientists are at the crux of deepening our understanding of the customers and driving more business outcomes by leveraging experimentation, causal inference, statistical and machine learning techniques, and data storytelling.

This role is based in our Vancouver office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements.

What you’ll achieve

• Work closely with Product Managers and User Researchers (UXR) to understand the needs of our new customers in their first few minutes of interaction with Asana, employing data frameworks and methodologies to understand our user segments, visualize the new user funnel, identify levers for driving conversion revenue, and personalize the onboarding journey to ensure better adoption outcomes. 
• Act as the "Data Lead" in a triad with Product and Engineering stakeholders to identify multi-year growth opportunities and prioritize the roadmap.
• Design experiments that measure product progress, and own key performance metrics.
• Collaborate and mentor fellow Data Scientists in the team to research, identify and disseminate best practices across teams.
• Work in a high-performance, dynamic team with a strong focus on velocity and software quality and learn about the upcoming field of AI-Optimization and how LLMs can help boost Asana’s value proposition. 

About you

• 5-7 years of data science experience, with majority in growth-focused roles at B2B SaaS companies 
• Proven track record driving acquisition and activation for B2B SaaS products, with specific experience in top-of-funnel growth and new user acquisition.
• 0→1 builder mentality. You've demonstrated the ability to thrive in highly ambiguous environments where you're inventing new playbooks rather than executing existing ones. You're energized by pioneering work.
• Experience with AI/ML products, LLM APIs, or third-party platform integrations. Strong preference for candidates who've worked with conversational AI or built features integrating ChatGPT, Claude, or similar platforms.
• Strong analytical foundation with deep fluency in experimentation, A/B testing, and funnel optimization.
• Exceptional cross-functional leadership. You write crisply, tell clear stories, and can influence across product, engineering, design, partnerships, sales, and executive stakeholders. You bring clarity to ambiguity.
• Strategic business acumen to balance user experience with revenue objectives while translating emerging technology trends into actionable product strategy.
• Genuine AI enthusiasm. You're curious, adaptable, and genuinely excited about how LLMs are transforming software discovery and adoption.
• Expert in relational data modeling and SQL and fluent in at least one modern language useful for data processing (e.g. R, Python, or Scala).
• Familiarity with statistical methods including experimental design, regression, causal inference with observational data and time series modeling.
• Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making

At Asana, we're committed to building teams that include a variety of backgrounds, perspectives, and skills, as this is critical to helping us achieve our mission. If you're interested in this role and don't meet every listed requirement, we still encourage you to apply.

What we’ll offer

Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit. 

For this role, the estimated base salary range is between CAD 211,000-240,000. The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. The listed range above is a guideline, and the base salary range for this role may be modified.

In addition to base salary, your compensation package may include additional components such as equity, sales incentive pay (for most sales roles), and benefits. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

We strive to provide equitable and competitive benefits packages that support our employees worldwide and include:

• Mental health, wellness & fitness benefits
• Career coaching & support
• Inclusive family building benefits
• Long-term savings or retirement plans
• In-office culinary options to cater to your dietary preferences 

These are just some of the benefits we offer, and benefits may vary based on role, country, and local regulations. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

#LI-Hybrid

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

Our co-op/intern experience is unique and designed to prepare you for professional success. Our ultimate goal is to give you the mentorship, training and work experience you need to start your career successfully!

As part of Visiers R&D organization, our data science team is responsible for the development of advanced numerical algorithms and statistical techniques that generate business insights from human resource data. Mentored by an experienced Data Scientist you will collaborate on projects that match both your interests and Visier business needs. This could be software engineering focused (e.g. enhancing our machine learning pipelines or prototyping of automated insight generation), or research focused (e.g. unveiling compelling insights into the workforce from our unique multi-million employee cross-customers data set).

What you'll be doing...

• Modelling the problem at hand, such that it directly solves underlying business problems
• Brainstorming to identify the correct data required to solve business problems
• Performing statistical analysis on data to build statistical/machine learning models
• Identifying the right metrics to measure the success of the models and projects
• Communicating and presenting your work to stakeholders across the organisation
• Conducting independently research, in order to gain domain knowledge and learn cutting-edge techniques

What you'll bring to the table...

• Strong understanding of statistics and how to apply scientific methods to data using algorithms
• Coding skills in Scala, Python or other relevant languages
• Attention to detail and big picture thinking
• Currently pursuing your Masters or PhD
• Knowledge in one of the following areas would be an advantage:
• Hypothesis testing
• Machine learning techniques
• Scientific analysis and knowledge generation

The base pay range for this position in Canada is $5,500k / month.

The compensation offered will be determined by factors such as relevant qualifications, experience, knowledge and skills. Many of our positions are eligible for additional types of compensation (e.g., commission plans, bonus, etc.) which our Talent Acquisition team will share with you if you interview for the role.

As a Test Developer Co-op at Visier, you will work with the Test Enablement Team and will be responsible for ensuring the integrity of the Visier application.

Our co-op experience is unique and designed to prepare you for professional success as you work on real, impactful work from the beginning...not features that may be mothballed after 8 months of hard work! Our ultimate goal is to give you the mentorship, training, and work experience you need to start your career. A number of our students return to us after they graduate and start developing their professional career at Visier! We use a variety of frontend technologies including Angular, NgRx, Play Framework, TypeScript, Less, and others. We deal with large volumes of data every day. And in our backend areas we work with technologies like Scala, Kafka, Consul, Cassandra DB, Docker, AWS, etc. So, you’ll be developing knowledge and skills that are transferable anywhere.

What you'll be doing...

• Manual testing, building automated tests, and data validation
• Working with developers to create and sign off on test plans
• Writing end-to-end UI automated tests and REST tests
• Setting up Jenkins CI pipeline to automate common tasks
• Performing final checks to ensure the software we release is beautiful

What you'll bring to the table...

• Coding skills in writing functional tests using Selenium, Playwright, and REST Assured
• Some knowledge in one of the following areas:
• Knowledge of data structures and algorithms
• Knowledge of web technologies (REST, XML, JSON, Angular14)
• Knowledge of SQL databases and SQL language is an asset
• Attention to detail, tendency to spot mistakes and identify potential improvements

Most importantly, you share our values...

• You roll up your sleeves
• You make it easy
• You are proud
• You never stop learning
• You play to win

The base pay range for this position in Canada is $3,600 - $4,500 per month. 

Our co-op experience is unique and designed to prepare you for professional success as you work on real, impactful work from the beginning...not features that may be mothballed after 8 months of hard work! Our ultimate goal is to give you the mentorship, training, and work experience you need to start your career. A number of our students return to us after they graduate and start developing their professional career at Visier! 

And we use state-of-the-art technology to build our innovative full-stack software solution that helps people see the truth and drives organizations to perform their best. We use a variety of frontend technologies including Angular, NgRx, Play Framework, TypeScript, Less, and others. We deal with large volumes of data every day. And in our backend areas we work with technologies like Scala, Kafka, Consul, Cassandra DB, Docker, AWS, etc. So, you’ll be developing knowledge and skills that are transferable anywhere.

What you'll be doing...

• Writing production-level code that will directly impact customers
• Implementing cool new features
• Fixing complex bugs
• Collaborating with people in all stages of the software development cycle

What you'll bring to the table...

• Coding skills in relevant programming languages (for example, like Java, JavaScript, Typescript, Scala or other similar languages)
• Coding skills in writing functional tests (eg using Selenium and ScalaTest)
• Knowledge of data structures and algorithms
• Attention to detail, tendency to spot mistakes and identify potential improvements
• Knowledge of web technologies (REST, HTML/CSS, JSON, Angular) is an advantage
• Knowledge of relational and non-relational databases and SQL language

The base pay range for this position in Canada is $3,600 - $4,500 per month. 

Databricks is on a mission to simplify and democratize data and AI — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.

As a Fullstack software engineer, you will work with your team and product management to make insights from data simple. We are looking for engineers that are customer obsessed, who can take on the full scope of the product and user experience beyond the technical implementation. You'll set the foundation for how we build robust, scalable and delightful products.

Vancouver will be the newest R&D center for Databricks, expanding our presence in the Pacific Northwest. We are actively hiring world-class engineers to join us on our mission to democratize data + AI.  

We envision the Vancouver site becoming a key driver of product innovation at Databricks. To start, we’re bringing a few strategic areas to the Vancouver site and we have several open roles across the teams below, including:

• Log Analytics - Our customers increasingly use Databricks to analyze petabyte-scale logs in real time. This creates new challenges across the entire data processing pipeline, including ingestion, indexing, processing, and the user experience itself.
• AI/BI - AI/BI is redefining Business Intelligence for the AI age. We launched this product last summer and have already seen tremendous adoption (98.7% of our data warehousing customers are already using AI/BI!). From rich dashboarding and advanced visualizations to powerful talk-to-your-data solutions, the products we are building involve exciting technical challenges across the entire stack. 
• Unity Catalog Business Semantics - Context is everything for AI. For enterprise data, that context needs to be governed and managed, which is what Unity Catalog Business Semantics offers. We recently launched our first Semantics modelling capability, Unity Catalog Metrics, this past Data + AI Summit but we have a lot more in store. Engineers on this team work at the intersection of large scale distributed systems, data modeling, governance, and AI enablement.
• Databricks Apps - Databricks Apps is one of the fastest growing products at Databricks, used by more than 2,500 customers who have created more than 20,000 apps — and it was only GA’ed this past June. The Apps team is one of the few teams that are exposed to low-level platform components (k8s, networking), owns fundamental tech (apps runtime and proxy), and is heavily investing in app builder AI agents.

What we look for:

• 5+ years of experience with HTML, CSS, and JavaScript.
• Passion for user experience and design and a deep understanding of front-end architecture.
• Comfortable working towards a multi-year vision with incremental deliverables.
• Motivated by delivering customer value.
• Experience with modern JavaScript frameworks (e.g., React, Angular, or VueJs/Ember).
• 5+ years of experience with server-side web technologies (eg: Node.js, Java, Python, Scala, C#, C++,Go).
• Good knowledge of SQL.
• Experience with cloud technologies, e.g. AWS, Azure, GCP, Docker, or Kubernetes.
• Experience developing large-scale distributed systems.

P-1440

Databricks is on a mission to simplify and democratize data and AI — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.

Vancouver will be the newest R&D center for Databricks, expanding our presence in the Pacific Northwest. We are actively hiring world-class engineers to join us on our mission to democratize data + AI.  

We envision the Vancouver site becoming a key driver of product innovation at Databricks. To start, we’re bringing a few strategic areas to the Vancouver site and we have several open roles across the teams below, including:

• Log Analytics - Our customers increasingly use Databricks to analyze petabyte-scale logs in real time. This creates new challenges across the entire data processing pipeline, including ingestion, indexing, processing, and the user experience itself.
• AI/BI - AI/BI is redefining Business Intelligence for the AI age. We launched this product last summer and have already seen tremendous adoption (98.7% of our data warehousing customers are already using AI/BI!). From rich dashboarding and advanced visualizations to powerful talk-to-your-data solutions, the products we are building involve exciting technical challenges across the entire stack. 
• Unity Catalog Business Semantics - Context is everything for AI. For enterprise data, that context needs to be governed and managed, which is what Unity Catalog Business Semantics offers. We recently launched our first Semantics modelling capability, Unity Catalog Metrics, this past Data + AI Summit but we have a lot more in store. Engineers on this team work at the intersection of large scale distributed systems, data modeling, governance, and AI enablement.
• Databricks Apps - Databricks Apps is one of the fastest growing products at Databricks, used by more than 2,500 customers who have created more than 20,000 apps — and it was only GA’ed this past June. The Apps team is one of the few teams that are exposed to low-level platform components (k8s, networking), owns fundamental tech (apps runtime and proxy), and is heavily investing in app builder AI agents.

What we look for:

• BS (or higher) in Computer Science, related technical field or equivalent practical experience.
• Comfortable working towards a multi-year vision with incremental deliverables.
• Motivated by delivering customer value and impact.
• 5+ years of production level experience in either Java, Scala or C++.
• Strong foundation in algorithms and data structures and their real-world use cases.
• Experience developing large-scale distributed systems.
• Experience working on a SaaS platform or with Service-Oriented Architectures.
• Experience with cloud technologies, e.g. AWS, Azure, GCP, Docker, or Kubernetes.
• Experience with security and systems that handle sensitive data.
• Good knowledge of SQL.

Databricks is on a mission to simplify and democratize data and AI — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.

As a Fullstack software engineer, you will work with your team and product management to make insights from data simple. We are looking for engineers that are customer obsessed, who can take on the full scope of the product and user experience beyond the technical implementation. You'll set the foundation for how we build robust, scalable and delightful products.

Vancouver will be the newest R&D center for Databricks, expanding our presence in the Pacific Northwest. We are actively hiring world-class engineers to join us on our mission to democratize data + AI.  

We envision the Vancouver site becoming a key driver of product innovation at Databricks. To start, we’re bringing a few strategic areas to the Vancouver site and we have several open roles across the teams below, including:

• Log Analytics - Our customers increasingly use Databricks to analyze petabyte-scale logs in real time. This creates new challenges across the entire data processing pipeline, including ingestion, indexing, processing, and the user experience itself.
• AI/BI - AI/BI is redefining Business Intelligence for the AI age. We launched this product last summer and have already seen tremendous adoption (98.7% of our data warehousing customers are already using AI/BI!). From rich dashboarding and advanced visualizations to powerful talk-to-your-data solutions, the products we are building involve exciting technical challenges across the entire stack. 
• Unity Catalog Business Semantics - Context is everything for AI. For enterprise data, that context needs to be governed and managed, which is what Unity Catalog Business Semantics offers. We recently launched our first Semantics modelling capability, Unity Catalog Metrics, this past Data + AI Summit but we have a lot more in store. Engineers on this team work at the intersection of large scale distributed systems, data modeling, governance, and AI enablement.
• Databricks Apps - Databricks Apps is one of the fastest growing products at Databricks, used by more than 2,500 customers who have created more than 20,000 apps — and it was only GA’ed this past June. The Apps team is one of the few teams that are exposed to low-level platform components (k8s, networking), owns fundamental tech (apps runtime and proxy), and is heavily investing in app builder AI agents.

What we look for:

• 10+ years of experience with HTML, CSS, and JavaScript.
• Passion for user experience and design and a deep understanding of front-end architecture.
• Comfortable working towards a multi-year vision with incremental deliverables.
• Motivated by delivering customer value.
• Experience with modern JavaScript frameworks (e.g., React, Angular, or VueJs/Ember).
• 10+ years of experience with server-side web technologies (eg: Node.js, Java, Python, Scala, C#, C++,Go).
• Good knowledge of SQL.
• Experience with cloud technologies, e.g. AWS, Azure, GCP, Docker, or Kubernetes.
• Experience developing large-scale distributed systems.

Databricks is on a mission to simplify and democratize data and AI — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.

Vancouver will be the newest R&D center for Databricks, expanding our presence in the Pacific Northwest. We are actively hiring world-class engineers to join us on our mission to democratize data + AI.  

We envision the Vancouver site becoming a key driver of product innovation at Databricks. To start, we’re bringing a few strategic areas to the Vancouver site and we have several open roles across the teams below, including:

• Log Analytics - Our customers increasingly use Databricks to analyze petabyte-scale logs in real time. This creates new challenges across the entire data processing pipeline, including ingestion, indexing, processing, and the user experience itself.
• AI/BI - AI/BI is redefining Business Intelligence for the AI age. We launched this product last summer and have already seen tremendous adoption (98.7% of our data warehousing customers are already using AI/BI!). From rich dashboarding and advanced visualizations to powerful talk-to-your-data solutions, the products we are building involve exciting technical challenges across the entire stack. 
• Unity Catalog Business Semantics - Context is everything for AI. For enterprise data, that context needs to be governed and managed, which is what Unity Catalog Business Semantics offers. We recently launched our first Semantics modelling capability, Unity Catalog Metrics, this past Data + AI Summit but we have a lot more in store. Engineers on this team work at the intersection of large scale distributed systems, data modeling, governance, and AI enablement.
• Databricks Apps - Databricks Apps is one of the fastest growing products at Databricks, used by more than 2,500 customers who have created more than 20,000 apps — and it was only GA’ed this past June. The Apps team is one of the few teams that are exposed to low-level platform components (k8s, networking), owns fundamental tech (apps runtime and proxy), and is heavily investing in app builder AI agents.

What we look for:

• BS (or higher) in Computer Science, related technical field or equivalent practical experience.
• Comfortable working towards a multi-year vision with incremental deliverables.
• Motivated by delivering customer value and impact.
• 10+ years of production level experience in either Java, Scala or C++.
• Strong foundation in algorithms and data structures and their real-world use cases.
• Experience developing large-scale distributed systems.
• Experience working on a SaaS platform or with Service-Oriented Architectures.
• Experience with cloud technologies, e.g. AWS, Azure, GCP, Docker, or Kubernetes.
• Experience with security and systems that handle sensitive data.
• Good knowledge of SQL.

P-1381

At Databricks, we are passionate about enabling Data & AI teams to solve the world's toughest problems - from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers, we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for data interaction to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.

About the Team:

The Backline Engineering Team serves as the critical bridge between Frontline Support and Engineering. We handle complex technical issues and escalations across the Data and AI ecosystem. With a strong focus on customer success, we are committed to delivering exceptional customer satisfaction by providing deep technical expertise, proactive issue resolution, and continuous platform improvements. We emphasise automation and tooling to enhance troubleshooting efficiency, reduce manual efforts, and improve the overall supportability of the platform and the health of our products. By developing smart solutions and streamlining workflows, we drive operational excellence and ensure a delightful experience for both customers and internal teams.

What your impact will be:

• Deep Dive Troubleshooting: Conduct deep-dive forensics into Spark core internals and the broader Databricks Data and AI ecosystem to resolve high-priority architectural failures and complex system anomalies.
• Root Cause Analysis: Perform advanced code-level analysis and resource profiling to identify and mitigate systemic root causes, ensuring the stability and reliability of high-scale production workloads.
• Architectural Optimization: Optimise architectural performance across the Data and AI stack by refining execution parameters and enforcing best practice strategies to maximise resource efficiency and throughput.
• Product Improvements: Analyse global issue trends and patterns to partner directly with Product Engineering, influencing the product roadmap and driving initiatives that enhance long-term supportability.
  Scalability & Tooling: Develop reproduction frameworks, automated workflows, and AI-driven diagnostic tools that translate complex backline findings into standardised resolution paths to empower and scale the broader organisation.

What we look for:

We are looking for customer-obsessed candidates with 10+ years of relevant experience, including deep expertise in one of the following three specialized tracks, along with proven experience in managing both customers and technical stakeholders. Since each track calls for a different set of technical capabilities, we’re looking for excellence in one area rather than proficiency in all: 

• Data Engineering Track: Expertise in large-scale big data solutions and ETL pipelines using Spark, Delta Lake, or Hive. Strong experience troubleshooting failures, diagnosing performance issues, and identifying root causes. Demonstrated problem-solving ability and understanding of data engineering best practices to ensure reliable, efficient workflows. Solid hands-on programming skills in Python, SQL, or Scala.
• Product Supportability Track: Deep understanding of distributed system internals. Ability to perform code-level root-cause analysis and profiling (using metrics and heap/thread dumps) in Java, Scala, or Python. Proven record of contributing to bug fixes and mentoring other engineers.
• AI Track: Experience with large-scale machine learning and generative AI systems, including LLM-based applications and agent-driven workflows. Strong grasp of model training, evaluation, and deployment in distributed environments. Experience managing the ML lifecycle, including governance and operationalisation. Skilled in diagnosing and optimising distributed ML workloads for performance and scalability.

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