About the Team:

We’re looking for a Staff Software Engineer I/II to join our Data Ingestion Team, which builds and operates the critical infrastructure that serves as the “front door” of StackAdapt’s event flow. In this role, you’ll own technical direction for critical parts of our ingestion platform, lead large-scale initiatives, and shape how data flows across the company.

The Data Ingestion team tackles complex distributed systems challenges at massive scale, with a focus on scalability, performance, and reliability. Our systems process over 10 billion events per day, powering core functions across bidding, reporting, billing, analytics, and machine learning. You will work on challenging technical problems and partner with teams across the company to drive key initiatives.

We design + build low-latency, highly reliable distributed systems for impressions, clicks, conversions, and third-party signals, ensuring data is accurate and available to downstream teams in real time. This is a high-impact area at the core of StackAdapt’s platform, the systems you own will directly shape data quality, system reliability, and the speed at which teams across the company can build and innovate. Our work sits at the cutting edge of large-scale distributed systems, leveraging modern technologies and architectures to solve complex real-time data challenges. The core stack includes Golang, Kafka, TiDB, Aerospike, Vitess, Kubernetes, and more.

If you enjoy solving complex distributed systems problems at scale and driving technical impact across teams, this role would be a great fit for you.

StackAdapt is a Remote First company. We are open to candidates located anywhere in Canada or the United States for this position.

What You’ll Be Doing:

• Own and lead large technical initiatives end-to-end, from design through production and monitoring
• Provide technical leadership and mentorship to engineers across the team
• Design, build, and operate highly scalable, distributed ingestion services for real-time and near-real-time scenarios
• Contribute hands-on as needed while enabling the team to execute effectively
• Build and maintain event ingestion pipelines using streaming technologies like Kafka
• Work with high-volume storage systems and databases to persist and serve data efficiently
• Partner with Data, ML, Product, and Platform teams to support evolving requirements
• Improve code quality, testing practices, system reliability, and observability
• Participate in design reviews, code reviews, and architectural discussions
• Balance and prioritize projects to maximize impact and align with company objectives

What You’ll Bring to the Table:

• Extensive experience building distributed systems and high-throughput backend services
• Proven ability to lead technical direction and drive large, cross-team initiatives
• Strong problem-solving and communication skills, with comfort working in ambiguous, complex domains
• Collaborative mindset with the ability to influence across teams
• Ability to think strategically, lead technically, and drive high-impact, scalable solutions
• Hands-on experience with streaming systems (Kafka or similar)
• Strong understanding of distributed systems trade-offs (consistency, availability, partition tolerance, scalability)
• Maintains an AI-forward approach to software engineering - leveraging AI to boost productivity, collaboration, and business automation
• Bonus: Familiarity with AdTech, event-driven architectures, or analytics systems

#LI-TM8

Engineering

Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders.

What you’ll do

Brex’s Governance, Risk, and Compliance function is at an exciting and pivotal point in our maturity journey and we’re seeking a team member who can seamlessly bridge compliance expertise with technical execution. As a Senior GRC Engineer, you will drive critical GRC processes that mitigate risk, keep us compliant, and build trust with our customers and partners. You'll evolve the technical foundation of our Trust program by automating security controls, building integrations between security tools and GRC platforms, and creating scalable processes that enable Brex to maintain compliance efficiently as we expand into new markets. You'll work at the intersection of security, engineering, and compliance — translating regulatory requirements into technical solutions and building automation that eliminates manual toil.

You'll leverage your deep understanding of SOC 2, PCI DSS, ISO 27001, AI governance frameworks, and others to both design controls for emerging compliance requirements and mature existing programs through automation and continuous monitoring. You’ll support Trust Assurance, Third Party Risk Management, and other Security Risk Management initiatives. Working with our Engineering, Infrastructure, and Product teams, you'll translate compliance frameworks into technical controls and build automated systems that help us achieve world-class security as Brex expands.

Your contributions will directly accelerate Brex's maturity. You'll design workflows using Tines, build integrations between security and GRC systems, and create dashboards for security metrics. You'll implement controls across the technology stack, support multiple audits (SOC 2, PCI DSS, SOX/ITGC, FINRA, ISO), and contribute to AI governance framework implementation (ISO 42001, NIST AI RMF, EU AI Act).

You'll have autonomy to build innovative solutions, collaborating cross-functionally to implement controls that enable growth while communicating technical concepts effectively across the organization.

Where you’ll work

This role will be based in our Vancouver office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Manage and scale IT infrastructure, services and tooling
• Work with a diverse group of  IT partners to optimize our provided services
• Implement new services in support of Information Technologies vision
• Scale our services by implementing configuration as code via Terraform providers or APIs
• Operationalize and upskill IT and its partners by producing documentation and leading training sessions
• Evangelize best practices both internally and externally facing

Requirements

• 5+ years of experience in GRC, IT Governance, or Security Engineering with a strong track record of automating manual compliance workflows.
• Deep experience with security frameworks such as SOC 2, PCI DSS, ISO 27001, and NIST CSF, specifically within cloud-native environments.
• Technical proficiency in Python (or similar scripting languages) and experience building integrations using APIs to connect security tools with GRC systems. You can read code, design integrations, and understand technical implementations.
• Builder mindset with the ability to design and implement automated control testing, continuous monitoring, and data-driven security metrics. You see manual processes and immediately think about how to automate them.
• Exceptional cross-functional collaboration and communication skills. You can translate complex compliance requirements into technical specifications that engineering teams can actually implement and influence stakeholders across technical and non-technical domains.
• Strong systems thinking. You have the ability to design scalable GRC architectures that grow with the company, rather than just solving for the immediate audit.
• Bias for action. You’re a self-starter who ships solutions quickly and iterates based on feedback. 

Bonus points

• Previous experience in Fintech or banking environments navigating complex regulatory landscapes.
• Hands-on experience with Tines or other SOAR platforms to automate security operations.
• Familiarity with AI/ML governance frameworks (NIST AI RMF, ISO 42001) or securing agentic systems.
• Deep knowledge of Cloud Security (AWS/GCP), infrastructure-as-code (Terraform), or DevSecOps practices.
• Relevant industry certifications such as CISSP, CISA, or CCSP.
• Experience building metrics dashboards for security visualization and reporting.
• Active contributions to the GRC or Security community through open-source projects or public research.

Compensation

The expected salary range for this role is $153,600 - $192,000 CAD. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

Marqeta is looking for a talented Senior Software Engineer to independently identify and deliver software solutions on our Data Infrastructure team through a set of milestones spanning a specific platform focus or a multi-component system. You will own and deliver projects in service of quarterly goals on the team and independently identify solutions to solve ambiguous, open-ended problems. You will work in collaboration with your team and cross-functional partners.

The Data Infrastructure team, within our Data & ML Platforms organization, is responsible for the data lakehouse, streaming, orchestration, and catalog platforms that power analytics and AI across Marqeta. We're a platform engineering team building infrastructure that other engineering teams depend on.

At Marqeta, participation in a rotational on-call pager duty is a required part of the software engineering role. The specifics of the rotation may vary by team, depending on team size and structure, and will be discussed further during the interview process.

We work Flexible First. This role can be performed remotely anywhere within Ontario or British Columbia, Canada. We’d love for you to join us!

Learn more about our Product and Engineering team

The Impact You'll Have

• Define and deliver well-scoped milestones for projects, and may be a technical lead for projects on your team.
• Lead design for small-to-medium components and participate in design for larger components, moving from task-level impact to small-to-medium project-level impact.
• Have significant ownership of a component of the data infrastructure, including its reliability, performance, and developer experience.
• Identify gaps and propose plans towards addressing those gaps.
• Independently write code that is clear, well-tested, and appropriately flexible, reusable, and adaptable to changing requirements.
• Actively level up less-experienced members of your team by helping them with their craft, providing guidance, and setting a good example.
• Participate in on-call coverage.

Who You Are

• 5+ years of experience as a software engineer with a Bachelor's degree; or 3 years of experience with an advanced degree. In lieu of a degree, 8+ years of relevant experience may suffice.
• Strong proficiency in Python in a production context.
• Proficiency with SQL.
• Experience working with distributed systems.
• Production experience with multiple core data technologies, including Apache Kafka, Apache Spark, and Apache Airflow (or close equivalents).
• Production experience with modern table formats (Parquet, Hudi, Iceberg, or Delta Lake).
• Production experience with AWS or another major cloud.
• Experience with infrastructure-as-code (Terraform, CloudFormation, CDK, or equivalent).
• Experience monitoring and alerting on production systems.
• Ability to decompose business problems into solutions that may require designs with multiple interacting components.
• Ability to build correct, maintainable software components.
• Ability to work with cross-functional stakeholders to identify technical blindspots and clarify ambiguity.
• Ability to present clear and concise messages tailored to various audiences; able to communicate clearly and respectfully both verbally and in writing.
• A focus on quality and meeting deadlines.
• Ability to decompose business problems into solutions that may require designs with multiple interacting components.
• Ability to build correct, maintainable software components.
• Ability to work with cross-functional stakeholders to identify technical blindspots and clarify ambiguity.
• Ability to present clear and concise messages tailored to various audiences; able to communicate clearly and respectfully both verbally and in writing.
• A focus on quality and meeting deadlines.

Nice-To-Haves

• Experience with payments or the FinTech space.
• Experience with Apache Iceberg specifically.
• Experience with CDC tooling, particularly Debezium or Kafka Connect.
• Experience with data catalog tooling (DataHub or equivalent).
• Experience with Java in addition to Python.
• Exposure to Kubernetes and container-based workloads.
• Experience building scalable software platforms.

Our (typical) process:

1. Application Submission
2. Recruiter Video Call
3. Coding Interview & Hiring Manager Interview
4. Virtual “Onsite” consisting of three 60 minute rounds
5. Offer!

Compensation and Benefits

Marqeta is a Flex First company which allows you to choose your best working environment, whether that be from home or at a company office. To support Flex First, we calibrate pay to a competitive value according to working location. 

When determining salaries, we consider several factors including, but not limited to, skills, prior experience, and work location. The new-hire base salary range for this position, reflected in CAD, is: 116,000 - 145,000

We also believe in recognizing the contributions of our people. That's why we award annual bonuses to eligible employees, rewarding both individual performance and the success of the entire company.

Along with monetary compensation, Marqeta offers

• Multiple health insurance options
• Flexible vacation time
• Retirement savings program with company contribution
• Equity in a publicly-traded company 
• Monthly stipend to support our remote work model
• Annual “development dollars” to support our people growth and development
• Family-forming benefits and up to 20 weeks of Parental Leave

We’re seeking an experienced Senior Security Engineer with a strong passion for Identity and Access Management(IAM) and proven expertise in cloud-native environments, particularly AWS. In this role, you’ll help shape and implement modern identity strategies to secure access across all of Marqeta’s systems and services—100% cloud-based, with no data center footprint.

Join us in building a secure, scalable, and frictionless IAM program where you’ll play a crucial part in:

• Building and evolving our Identity Governance and Administration (IGA) capabilities.
• Implementing & Operating Privileged Access Management (PAM) in a cloud-first (AWS-focused) environment.
• Designing and architecting a Certificate Lifecycle Management solution that supports cloud-native workloads.
• Driving integration of IAM across AWS services, SaaS platforms, and developer/DevOps pipelines.
• Designing identity and access controls to protect AI/ML systems—ensuring secure access to training data, models, and inference APIs.

The Impact You’ll Have

• Develop and lead implementation of robust IAM strategies aligned with cloud-native architecture and security principles.
• Expand and operationalize the IAM program across IGA, PAM, SSO, MFA, access management, secrets management, and certificate lifecycle.
• Automate identity provisioning, de-provisioning, and access reviews using AI tools and infrastructure-as-code.
• Design IAM integrations for AWS-native services (Lambda, EC2, S3, IAM, etc.), SaaS platforms, and third-party identity tools (e.g., Okta, CyberArk).
• Promote and enforce least privilege and zero-trust principles through scalable access controls and policy automation.
• Mentor junior engineers and serve as a technical lead for IAM-related projects.
• Collaborate with Security, DevOps, and Infrastructure teams to embed IAM controls across the engineering lifecycle.
• Stay ahead of emerging trends and continuously refine IAM strategy based on evolving cloud threats and compliance requirements.

Who You Are

• A minimum of 8 years related experience with a Bachelor’s degree; or 5 years and a Master’s degree; or a PhD with 3 years’ experience; or equivalent combination of related education and work experience.
• Strong experience with IAM tools (e.g., Okta, CyberArk, Ping, SailPoint).
• Deep knowledge of IAM in cloud-native environments, especially AWS IAM, roles, policies, permissions boundaries, and federation.
• Proficiency in infrastructure-as-code (e.g., Terraform, CloudFormation).
• Familiarity with authentication and authorization protocols (SAML, OAuth2, OpenID Connect, Kerberos).
• Strong grasp of directory services like Active Directory, LDAP, and cloud-based alternatives.
• Hands-on skills in scripting (e.g., Python, PowerShell) to automate IAM operations.
• Solid understanding of compliance standards: NIST, SOC 2, PCI DSS, etc.
• Proven experience integrating IAM into CI/CD pipelines, secrets management, and DevOps workflows.
• Excellent communication skills and ability to influence and lead cross-functional teams.

Nice to have

• Relevant certifications such as CISSP, CISM, or IAM-specific credentials (e.g., CIAM/CAMS, CyberArk Certified, Okta Certified Consultant).
• Experience with AWS technologies such as Lambda, S3, DynamoDB, RDS, Aurora, SNS, SQS, CloudTrail, CloudWatch, Code Pipeline, AWS Developer Tools, and IAM roles and permissions
• Experience with DevOps tools and practices, including secrets management and CICD pipelines

Manager

• Sandeep Chotwani

Recruiter for this role

• Kayla Osuna

Compensation and Benefits

Marqeta is a Flex First company which allows you to choose your best working environment, whether that be from home or at a company office. To support Flex First, we calibrate pay to a competitive value according to working location. 

When determining salaries, we consider several factors including, but not limited to, skills, prior experience, and work location. The new-hire base salary range for this position, reflected in CAD, is: 136,800 - 171,000

We also believe in recognizing the contributions of our people. That's why we award annual bonuses to eligible employees, rewarding both individual performance and the success of the entire company.

Along with monetary compensation, Marqeta offers

• Multiple health insurance options
• Flexible vacation time
• Retirement savings program with company contribution
• Equity in a publicly-traded company 
• Monthly stipend to support our remote work model
• Annual “development dollars” to support our people growth and development
• Family-forming benefits and up to 20 weeks of Parental Leave

We’re seeking an experienced Senior Security Engineer with a strong passion for Identity and Access Management(IAM) and proven expertise in cloud-native environments, particularly AWS. In this role, you’ll help shape and implement modern identity strategies to secure access across all of Marqeta’s systems and services—100% cloud-based, with no data center footprint.

Join us in building a secure, scalable, and frictionless IAM program where you’ll play a crucial part in:

• Building and evolving our Identity Governance and Administration (IGA) capabilities.
• Implementing & Operating Privileged Access Management (PAM) in a cloud-first (AWS-focused) environment.
• Designing and architecting a Certificate Lifecycle Management solution that supports cloud-native workloads.
• Driving integration of IAM across AWS services, SaaS platforms, and developer/DevOps pipelines.
• Designing identity and access controls to protect AI/ML systems—ensuring secure access to training data, models, and inference APIs.

The Impact You’ll Have

• Develop and lead implementation of robust IAM strategies aligned with cloud-native architecture and security principles.
• Expand and operationalize the IAM program across IGA, PAM, SSO, MFA, access management, secrets management, and certificate lifecycle.
• Automate identity provisioning, de-provisioning, and access reviews using AI tools and infrastructure-as-code.
• Design IAM integrations for AWS-native services (Lambda, EC2, S3, IAM, etc.), SaaS platforms, and third-party identity tools (e.g., Okta, CyberArk).
• Promote and enforce least privilege and zero-trust principles through scalable access controls and policy automation.
• Mentor junior engineers and serve as a technical lead for IAM-related projects.
• Collaborate with Security, DevOps, and Infrastructure teams to embed IAM controls across the engineering lifecycle.
• Stay ahead of emerging trends and continuously refine IAM strategy based on evolving cloud threats and compliance requirements.

Who You Are

• A minimum of 8 years related experience with a Bachelor’s degree; or 5 years and a Master’s degree; or a PhD with 3 years’ experience; or equivalent combination of related education and work experience.
• Strong experience with IAM tools (e.g., Okta, CyberArk, Ping, SailPoint).
• Deep knowledge of IAM in cloud-native environments, especially AWS IAM, roles, policies, permissions boundaries, and federation.
• Proficiency in infrastructure-as-code (e.g., Terraform, CloudFormation).
• Familiarity with authentication and authorization protocols (SAML, OAuth2, OpenID Connect, Kerberos).
• Strong grasp of directory services like Active Directory, LDAP, and cloud-based alternatives.
• Hands-on skills in scripting (e.g., Python, PowerShell) to automate IAM operations.
• Solid understanding of compliance standards: NIST, SOC 2, PCI DSS, etc.
• Proven experience integrating IAM into CI/CD pipelines, secrets management, and DevOps workflows.
• Excellent communication skills and ability to influence and lead cross-functional teams.

Nice to have

• Relevant certifications such as CISSP, CISM, or IAM-specific credentials (e.g., CIAM/CAMS, CyberArk Certified, Okta Certified Consultant).
• Experience with AWS technologies such as Lambda, S3, DynamoDB, RDS, Aurora, SNS, SQS, CloudTrail, CloudWatch, Code Pipeline, AWS Developer Tools, and IAM roles and permissions
• Experience with DevOps tools and practices, including secrets management and CICD pipelines

Manager

• Sandeep Chotwani

Recruiter for this role

• Kayla Osuna

Compensation and Benefits

Marqeta is a Flex First company which allows you to choose your best working environment, whether that be from home or at a company office. To support Flex First, we calibrate pay to a competitive value according to working location. 

When determining salaries, we consider several factors including, but not limited to, skills, prior experience, and work location. The new-hire base salary range for this position, reflected in CAD, is: 136,800 - 171,000

We also believe in recognizing the contributions of our people. That's why we award annual bonuses to eligible employees, rewarding both individual performance and the success of the entire company.

Along with monetary compensation, Marqeta offers

• Multiple health insurance options
• Flexible vacation time
• Retirement savings program with company contribution
• Equity in a publicly-traded company 
• Monthly stipend to support our remote work model
• Annual “development dollars” to support our people growth and development
• Family-forming benefits and up to 20 weeks of Parental Leave

Join Okta’s Defensive Cyber Engineering team as a Staff Engineer responsible for safeguarding Okta’s environments. You’ll work closely with the Security, Business Technology Engineering and Product teams to implement and manage security solutions and ensure that core infrastructure applications are protecting our workforce, endpoints, and corporate data. 

A strong desire to make tools and people work together to solve complex security problems is central to this role. This approach mandates an engineering-first approach: maximizing the utility of existing security tools before strategically building or buying new solutions to address any remaining security gaps. 

To execute this vision, you will combine your enterprise security expertise with your hands-on engineering skills, leveraging automation, policy-as-code, and cloud-native technologies to deliver scalable, resilient, and secure solutions. Your work will ultimately set standards for security best practices across the organization and influence the architecture of business-critical systems.

What you bring:

• Hands on experience with enterprise security tools such as Okta, Crowdstrike and Palo Alto suite covering EDR (Endpoint Detection and Response), CASB (Cloud Access Security Broker), DLP (Data Loss Prevention), MDM (Mobile Device Management), SASE (Secure Access Service Edge), and SSPM (SaaS Secure Posture Management) capabilities.
• Strong coding and scripting skills are required for building automation and custom tooling. Python experience is preferred, but proficiency in other languages (e.g., Bash, PowerShell, Go) is a plus.
• Proven track record automating security controls and workflows using a cloud-first approach
• Experience with Terraform and other infrastructure-as-code tools to orchestrate security infrastructure
• Familiarity with CI/CD pipelines for security automation and drift management
• Strong communication skills across technical staff, support teams, executive leadership, and external vendors.

What you’ll be doing

• Serve as a security subject matter expert (SME) for solution engineering, architecture reviews, security assessment, and vulnerability mitigation
• Lead technical efforts evaluating, designing, and implementing new enterprise security systems and feature enhancements
• Build, maintain, and enhance custom automation and cloud infrastructure using Terraform or similar tools to support team workflows and the enforcement of security controls
• Develop integrations with APIs, cloud platforms (AWS, GCP, Azure), and security infrastructure to improve detection, response, and remediation
• Collaborate with cross-functional teams to tackle global technology and security challenges
• Write and maintain scripts and automation to streamline security operations, with an emphasis on Python-based solutions
• Establish monitoring and alerting for security posture, misconfigurations, and threats across endpoints, SaaS, and cloud workloads
• Proactively identify and remediate security gaps; stay updated on emerging threats, solutions, and tooling across the industry

And extra credit if you have experience in any of the following!

• Working with advanced identity management technologies (MFA, SAML, OAuth, OIDC, WebAuthn)
• Deep understanding of Okta's ecosystem, including advanced configuration and integrations
• Experience with continuous compliance solutions (e.g., policy-as-code, automated evidence gathering)
  
  

What you can look forward to as an Full-Time Okta employee!

World-class benefits, flexibility, and growth opportunities

The chance to shape the security posture of a global leader in identity

Opportunities to make a social Impact through technology and innovation

Ready to join Okta and make security the foundation of our innovation? Apply today!

#LI-HYBRID

P24217

About the Role

As a Staff Detection & Response Engineer at Okta, you will occupy a high-impact leadership role within our Security Operations organization. This is not just a technical role; it is a strategic one. You will influence security decisions across multiple teams, drive innovation in our defense-in-depth strategies, and act as a force multiplier for our global security posture.

You will be responsible for leading complex threat hunts, managing major security incidents, and architecting the detection frameworks that protect Okta’s infrastructure and customers. You will serve as a bridge between executive leadership and technical teams, ensuring our security objectives align with broader business trends and strategic goals.

What You’ll Do

Technical Leadership & Innovation

• Threat Hunting: Lead complex, hypothesis-driven threat hunts based on vague intelligence, and develop refined methodologies for the broader team to follow.
• Detection Engineering: Write novel detections and solve complex querying challenges. You will establish the frameworks and standards for detection engineering across the entire security organization.
• Architectural Influence: Design and validate the effectiveness of preventative control chains and influence the selection and architecture of new security solutions.
• Incident Response: Serve as the technical lead for major incidents, including investigating threats in "foreign" or uncommon environments by leveraging frameworks and your professional network.

Global Mindset: While Okta maintains a 24x7posture, there is a requirement to be available in a crisis out of (your business) hours, and as a thought leader, to interface with colleagues globally. 

Strategic Execution

• Project Ownership: Own multi-quarter objectives and drive them to success, developing project plans that align with Okta’s strategic VMTs (Vision, Metrics, and Targets) and budget.
• Problem Solving: Navigate issues of diverse scope, analyzing data against current business trends to provide innovative solutions.
• Innovation: Secure resources for and drive innovation initiatives across multiple teams, facilitating cross-functional workshops to generate new security solutions.

Team Advocacy & Mentorship

• Culture Building: Foster an inclusive, collaborative environment by sharing wins, rewarding team efforts, and encouraging diverse perspectives.
• Mentorship: Act as an advisor to junior colleagues, providing actionable feedback and helping others align their career goals with organizational objectives.
• Consensus Driving: Drive consensus and resolve issues within cross-functional teams, ensuring all stakeholders are aligned on security priorities.

Who You Are

• Expert Investigator: You have a deep understanding of threat actor TTPs and can navigate complex systems even when documentation is sparse.
• Effective Communicator: You can translate complex technical problems for any audience, from executive leadership to junior analysts.
• Resilient Leader: You have a "bottom-line" orientation, persisting through obstacles and setbacks to achieve results.
• Self-Aware & Empathetic: You proactively seek feedback, know your limits, and lead with empathy and compassion.
• Collaboratively Minded: You prioritize the development of others and establish internal and external networks of coaches and mentors.

Qualifications

• Proven experience in a high-growth SaaS or security-focused environment.
• Expertise in querying languages, detection frameworks (e.g., MITRE ATT&CK), and modern IR toolsets.
• Experience managing communications during crises or major security incidents.
• Demonstrated ability to lead project teams and manage budget/resource requirements.
• Strong background in automating response workflows and refining preventative controls.

Why Okta? Okta is the World’s Identity Company. We are building a world where anyone can safely use any technology. As a Staff Engineer, you will play a pivotal role in ensuring that vision remains a reality.

#LI-HYBRID 

P6513_3323862

Curious about how cutting-edge hardware actually comes to life? We're looking for someone who’s excited to dive into the core of next-gen systems and help make them real. In this role, you’ll validate high-speed interfaces, solve complex system-level puzzles, and collaborate across teams to shape the future of AI/ML computing. If firmware, hardware, and hands-on debugging sound like your kind of fun — let’s chat!

This role is hybrid and based in Vancouver, Canada. 

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are

• You love bringing hardware to life and enjoy the thrill of solving tricky problems across the hardware–firmware boundary.
• You’re hands-on in the lab and comfortable with tools like oscilloscopes, protocol analyzers, and JTAG — digging deep doesn’t scare you.
• You’re comfortable jumping into unfamiliar problems and figuring things out — whether it’s in the lab or in firmware.
• You’re curious, collaborative, and excited to work on technology that pushes the boundaries of performance.

What We Need

• Someone to take the lead in validating our next-gen PCIe interfaces — from controller to PHY, and everything in between.
• A strong collaborator who can work across hardware, firmware, and software teams to bring up new features and solve problems.
• A sharp debugger who’s comfortable triaging issues across hardware, firmware, and software layers.
• A detail-oriented thinker who thrives in fast-paced environments and enjoys seeing ideas come to life in real systems.

What You Will Learn

• How to work at the cutting edge of compute — validating next-gen hardware and making it production-ready.
  How different pieces of a system come together — from multi-die chiplets to firmware integration and beyond.
  How to use advanced tools and techniques to debug and tune complex system interactions.
  How to shape emerging technology in a collaborative, fast-moving engineering environment.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.

Engineering at Brex

Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders.

What you’ll do

As an Application Security Engineer, you will contribute to finding and responding to security vulnerabilities across the Brex platform. In this role, you will participate in code reviews, design reviews, penetration testing, and vulnerability management. You will contribute to tooling that performs static and dynamic testing of the Brex platform and supports secure developer workflows. Application Security is part of our wider Financial Scale organization, which means you will work closely with Security Operations, GRC, Product Security, Front End Platform, and IT Infrastructure teams.

We're looking for individuals with a solid foundation in penetration testing and a curiosity for finding vulnerabilities in complex systems. You should have experience identifying and documenting vulnerabilities across common vulnerability classes and be able to communicate their risk clearly to engineering and product teams. This role is highly cross-functional — you'll have the opportunity to collaborate with engineering teams across Brex and grow your security expertise in a fast-moving environment.

Brex is pioneering the next wave of AI-driven financial services for dynamic, high-impact companies like Coinbase, Robinhood, and Anthropic. As we integrate AI across our product suite, this role will give you hands-on experience contributing to AI security at Brex. You'll apply emerging AI security best practices to help secure our agentic features, identify attack surfaces introduced by LLM-powered systems, and partner with product and engineering teams to build AI capabilities our customers can trust with their critical financial operations.

Responsibilities

• Identify vulnerabilities across common vulnerability classes (e.g., OWASP Top 10), document findings clearly, and communicate risk to drive remediation efforts
• Participate in penetration testing and design reviews alongside senior engineers, contributing to the identification of vulnerabilities and insecure designs
• Contribute to internal tooling and automation efforts that support SAST and DAST testing of the Brex platform and promote secure development practices
• Collaborate with engineering and product teams to support the design of secure product features
• Actively contribute to a culture of security awareness through knowledge sharing and peer learning

Requirements

• 4+ years of work experience in Application Security or a related role
• Demonstrated ability to find and document vulnerabilities in complex systems, with clear communication of business risk
• Hands-on experience with a subset of secure development activities, such as code review, threat modeling, or penetration testing
• Experience identifying security risks in AI/ML systems — such as prompt injection, model manipulation, or data poisoning — through work experience, personal projects, CTFs, or bug bounties
• Familiarity with agentic workflows and the ability to reason about attack surfaces introduced by LLM-powered features
• Knowledge of Python or scripting languages to automate tasks and build tooling
• Collaborative mindset paired with strong written and verbal communication skills

Bonus points

• Experience with Kotlin, gRPC, GraphQL, Kubernetes
• Previous experience as a software engineer
• Experience with securing distributed systems in AWS and cloud environments
• Experience with web application security reviews
• Contributions to the wider technical community — open source, public research, CTF participation, blogging, CVEs, or presentations
• Experience submitting to bug bounty or responsible disclosure programs
• Published AI security research or contributions to AI security frameworks

Compensation

The expected salary range for this role is $152,000 - $190,000. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

Your role
As a Sr. SDET in Agentic QA, you will own the test automation and quality frameworks that support Dialpad’s AI Voice Agent services. You will develop automated tests for end-to-end product experiences, from frontend UI to backend services to APIs to audio/text interactions. You will test orchestration flows, agent configuration experiences, and guardian safeguards to create robust automated coverage for functionality, performance, reliability, UX, and more. 

In this role, you will develop substantial amounts of automated test infrastructure and partner deeply with the development team to make our fast-growing AI platform more testable, more stable, and more delightful for customers.

This position is based at one of Dialpad’s Canadian offices and reports to a QA Eng Manager in the United States.

What you’ll do

• Own end-to-end quality for agentic features and workflows, including strategy, development, execution, and release qualification.
• Design and build automation tooling and frameworks for AI/LLM-driven systems, including prompt flows, agent orchestration, and tool integrations.
• Develop and maintain evaluation frameworks (evals) to measure response quality, accuracy, and hallucination rates.
• Drive automation coverage (80%+ for critical AI workflows) using deterministic + probabilistic validation approaches.
• Integrate AI quality checks into CI/CD pipelines with fast feedback cycles (<15 minutes for PR validation).
• Build tooling for LLM observability and debugging, including prompt tracing and response analysis.
• Partner with Applied AI teams on prompt engineering, model selection, and evaluation strategies.
• Design and execute performance and load tests for AI services (latency, throughput, cost efficiency).
• Identify and mitigate risks related to hallucinations, bias, safety, and edge cases.
• Define and track AI quality KPIs (task success rates, precision/recall, latency, etc.).
• Participate in design and architecture reviews to ensure systems are testable, observable, and resilient.
• Mentor engineers and contribute to raising the bar on AI quality engineering practices.

What you’ll bring

• 5+ years of experience in software engineering or SDET roles with an emphasis on software development.
• Strong programming skills in Python (preferred), Java, or JavaScript.
• Experience testing distributed, cloud-native SaaS systems and APIs.
• Demonstrated proficiency in coding with AI agents to accelerate development and improve code quality.
• Hands-on exposure to LLMs or AI/ML systems (e.g., OpenAI, Claude, Gemini, or similar platforms).
• Understanding of non-deterministic systems and probabilistic testing approaches.
• Experience building test frameworks and scalable automation systems.
• Familiarity with AI evaluation techniques (benchmarking, golden datasets, human-in-the-loop validation).
• Experience with CI/CD pipelines (e.g., Jenkins, GitHub Actions).
• Strong collaboration skills with the ability to work across distributed teams and time zones.
• Bachelor’s degree in Computer Science or equivalent practical experience.
• Backend: Python, Go, Google Cloud Platform, Cloud Run / App Engine, Kubernetes, Datastore, Redis, ElasticSearch.
• Frontend: Vue3, React.
• AI Stack: LLM APIs, LiveKit, prompt orchestration frameworks, evaluation tooling.

Your role
As a Sr. SDET in Agentic QA, you will own the test automation and quality frameworks that support Dialpad’s AI Voice Agent services. You will develop automated tests for end-to-end product experiences, from frontend UI to backend services to APIs to audio/text interactions. You will test orchestration flows, agent configuration experiences, and guardian safeguards to create robust automated coverage for functionality, performance, reliability, UX, and more. 

In this role, you will develop substantial amounts of automated test infrastructure and partner deeply with the development team to make our fast-growing AI platform more testable, more stable, and more delightful for customers.

This position is based at one of Dialpad’s Canadian offices and reports to a QA Eng Manager in the United States.

What you’ll do

• Own end-to-end quality for agentic features and workflows, including strategy, development, execution, and release qualification.
• Design and build automation tooling and frameworks for AI/LLM-driven systems, including prompt flows, agent orchestration, and tool integrations.
• Develop and maintain evaluation frameworks (evals) to measure response quality, accuracy, and hallucination rates.
• Drive automation coverage (80%+ for critical AI workflows) using deterministic + probabilistic validation approaches.
• Integrate AI quality checks into CI/CD pipelines with fast feedback cycles (<15 minutes for PR validation).
• Build tooling for LLM observability and debugging, including prompt tracing and response analysis.
• Partner with Applied AI teams on prompt engineering, model selection, and evaluation strategies.
• Design and execute performance and load tests for AI services (latency, throughput, cost efficiency).
• Identify and mitigate risks related to hallucinations, bias, safety, and edge cases.
• Define and track AI quality KPIs (task success rates, precision/recall, latency, etc.).
• Participate in design and architecture reviews to ensure systems are testable, observable, and resilient.
• Mentor engineers and contribute to raising the bar on AI quality engineering practices.

What you’ll bring

• 5+ years of experience in software engineering or SDET roles with an emphasis on software development.
• Strong programming skills in Python (preferred), Java, or JavaScript.
• Experience testing distributed, cloud-native SaaS systems and APIs.
• Demonstrated proficiency in coding with AI agents to accelerate development and improve code quality.
• Hands-on exposure to LLMs or AI/ML systems (e.g., OpenAI, Claude, Gemini, or similar platforms).
• Understanding of non-deterministic systems and probabilistic testing approaches.
• Experience building test frameworks and scalable automation systems.
• Familiarity with AI evaluation techniques (benchmarking, golden datasets, human-in-the-loop validation).
• Experience with CI/CD pipelines (e.g., Jenkins, GitHub Actions).
• Strong collaboration skills with the ability to work across distributed teams and time zones.
• Bachelor’s degree in Computer Science or equivalent practical experience.
• Backend: Python, Go, Google Cloud Platform, Cloud Run / App Engine, Kubernetes, Datastore, Redis, ElasticSearch.
• Frontend: Vue3, React.
• AI Stack: LLM APIs, LiveKit, prompt orchestration frameworks, evaluation tooling.

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

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

Your Role at impact.com:

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

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

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

What You'll Do:

Yield Optimization & Pricing Models

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

Inventory Allocation & Supply Optimization

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

Data Architecture & Feature Engineering

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

Real-Time Inference & Production ML

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

Experimentation & Performance Measurement

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

Self-Learning Systems & Feedback Loops

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

Cross-Functional Collaboration

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

What You Bring:

Required

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

Preferred / Nice to Have

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

What Sets You Apart

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

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

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

Benefits and Perks: 

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

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

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

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

#LI_Vancouver_Canada

ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You'll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won't just contribute. You'll make things happen—fast.

The Opportunity

ZoomInfo has rebuilt around AI. We have the orchestration infrastructure, the tools platform, the intelligence layer. Now we need someone to turn that infrastructure into outcomes—someone who lives at the intersection of what's technically possible and what GTM teams actually need.

This role owns the agentic ecosystem: the core agents that power ZoomInfo's products, the Plays library that packages intelligence into deployable workflows, and the marketplace where customers discover, customize, and deploy automation. You'll build agents that book meetings, enrich pipelines, resurrect dead leads, and orchestrate multi-step GTM motions. Then you'll package them into Plays that non-technical users can deploy in clicks, not code.

You're not managing from the sidelines. You're building. You'll work in our Plays builder the way a RevOps leader works in their automation tools—hands-on, iterative, constantly shipping. You'll prototype agents, refine prompts, test orchestration patterns, and validate that what we build actually moves pipeline. When you identify a gap in what our agents can do, you'll partner with the tools and infrastructure teams to close it.

The marketplace you own is how ZoomInfo's agentic capabilities reach the world. Internal teams deploy your Plays across Copilot, GTM Studio, and MarketingOS. Customers browse, customize, and activate automation from your library. Partners and developers connect through MCP gateways you define. Every surface where ZoomInfo's agentic intelligence becomes accessible—you own it.

This is a rare role: part builder, part GTM strategist, part platform owner. You need to understand how SDR teams actually prospect, how marketing ops builds nurture flows, how CS teams identify expansion signals. Then you need to translate that understanding into agents and Plays that work reliably at scale. The infrastructure exists. The data exists. We need someone to turn it into the workflows that transform how GTM teams operate.

What You'll Do

Build Core Agents & Plays Design, build, and iterate on the agents and Plays that ship inside ZoomInfo's products. You'll work at multiple levels of abstraction: building agents from primitives when novel capabilities require it, composing existing agents into complex orchestrations, and packaging everything into Plays that non-technical users can deploy. This isn't spec-writing—you'll build in our Plays environment daily, testing what works and shipping what delivers.

Own the Plays Library Curate and expand ZoomInfo's library of pre-built GTM workflows. Identify high-value use cases through customer research, competitive analysis, and GTM intuition. Prioritize which Plays to build, define their logic and orchestration, validate they deliver measurable outcomes, and iterate based on adoption and performance data. The library is the product—its breadth, quality, and relevance determine whether customers see ZoomInfo as a data vendor or an automation platform.

Define the Marketplace Experience Own the customer-facing marketplace where users discover, evaluate, and deploy agentic workflows. Define the information architecture, discovery mechanisms, and activation flows that help users find the right Play for their use case. Balance curation with abundance—enough options to address diverse needs, enough guidance to prevent paralysis. Measure success through activation rates, time-to-value, and Play retention.

Translate GTM Expertise into Agentic Design The best agents encode deep understanding of the workflows they automate. Bring GTM operational expertise to agent design: How do top SDRs actually research accounts? What signals trigger effective outreach timing? How do marketing ops teams think about lead routing? Translate this knowledge into agent behaviors, Play logic, and orchestration patterns that feel like they were built by someone who's lived in RevOps.

Close the Loop with Platform Teams You're the voice of "what agents need to do" speaking to the teams building "what agents can do." When you discover that a critical GTM workflow requires a capability that doesn't exist, partner with the tools and infrastructure teams to prioritize it. When platform improvements unlock new possibilities, translate them into Plays that demonstrate value. You're the bridge between infrastructure leverage and customer outcomes.

What You'll Bring

• 6-15 years product management experience with 2+ years focused on GTM technology, workflow automation, or AI-powered products
• Direct experience building agentic workflows—you've shipped automation using tools like Relevance.ai, Lindy.ai, n8n, or similar no-code/low-code agent builders
• Hands-on familiarity with AI-assisted development (Claude Code, Cursor, or similar)—you prototype and build, not just specify
• Deep understanding of GTM operations: how sales, marketing, and success teams actually work, the tools they use, the metrics they track, the workflows they build
• Experience with complex workflow orchestration—multi-step, multi-system automation with branching logic, error handling, and human-in-the-loop patterns
• Track record shipping products where you personally built significant portions of the experience, not just managed engineers building it

Who You Are

Builder First You don't just write requirements—you build. You're comfortable in no-code workflow builders, and you reach for Claude Code or Cursor when you need to go deeper. You prototype to learn, ship to validate, and iterate based on what you discover. Your PRDs include artifacts you've built, not just wireframes someone else will implement.

GTM Operations Native You've lived in the world your users inhabit. You've built sequences in Outreach or Salesloft. You've configured lead routing in Salesforce or HubSpot. You've debugged why a Marketo program isn't firing. You understand the gap between how GTM workflows should work and how they actually work—the edge cases, the data quality issues, the human workarounds. This knowledge shapes every agent you design.

Agentic Workflow Intuition You understand what makes agents succeed or fail. You know how to write tool descriptions that guide reliable invocation. You've debugged orchestration flows where agents took unexpected paths. You think about failure modes, human oversight, and graceful degradation. You can look at a complex workflow and identify where it will break—then design around it.

Marketplace Product Sense You've thought about discovery, curation, and activation at scale. You understand that a library of 500 Plays is worthless if users can't find the right one. You think about information architecture, progressive disclosure, and the journey from "browsing" to "deployed and delivering value." You balance flexibility with guidance.

Customer Empathy with Technical Depth You can sit with a VP of Sales and understand their pipeline challenges, then translate that into agent specifications an ML engineer can build. You move fluidly between "we need to accelerate deal velocity" and "the agent needs to invoke the CRM lookup tool before the enrichment tool to avoid redundant API calls." You don't choose between business context and technical precision—you hold both.

Intellectual Curiosity The agentic landscape evolves weekly. You follow what's shipping—new agent frameworks, orchestration patterns, MCP developments, prompt engineering techniques. You try new tools not because it's your job but because you're genuinely curious about what's possible. You bring external learnings back to the team and challenge assumptions when better approaches emerge.

#LI-VC1

#LI-Hybrid

About The Team 

Cerebras builds wafer-scale AI processors—single chips delivering tens of PB/s of memory bandwidth and a dataflow architecture that accelerates at a granularity no multi-device system can match. The Advanced Technology Group (ATG) is Cerebras’ pathfinding organization. We work ahead of product to explore new architectures, demonstrate breakthrough performance on scientific and AI workloads, and shape the technical roadmap for future Cerebras hardware and software. Our work regularly appears at top-tier venues (Supercomputing, SIAM, IEEE, and NeurIPS) and directly influences the design of next-generation wafer-scale systems. 

About The Role 

We are seeking R&D Engineers to join Cerebras' Advanced Technology Group. You will design and implement workloads that establish new performance benchmarks on wafer-scale hardware, leveraging architectural features that no traditional platform offers. The 
scope ranges from large-scale scientific simulations to emerging AI/ML models, and the work sits at the intersection of algorithm design, compiler co-optimization, and hardware architecture. You will collaborate closely with Cerebras’ ASIC, compiler, kernel, and AI teams as well as external partners at universities and national laboratories. 

What You Will Do 

• Design and implement challenging scientific computing and AI workloads on Cerebras’ Wafer-Scale Engine, targeting performance results that advance the state of the art. 
• Lead algorithm–hardware co-design efforts with internal R&D teams and external research partners, turning architectural capabilities into measurable application-level advantages. 
• Build analytical performance models that quantify bottlenecks, guide optimization, and inform future chip and compiler design decisions. 
• Contribute to Cerebras’ multi-year technology roadmap by identifying high-impact workloads, proposing architectural experiments, and validating them on silicon. 
• Publish findings and present at top-tier conferences and journals; represent Cerebras in the broader HPC and AI research communities. 

What We Are Looking For 

• PhD in Computer Science, Engineering, Applied Mathematics, Physics, or a related quantitative field preferred. Exceptional candidates without a graduate degree who demonstrate equivalent depth through published research, significant open-source contributions, or a strong industry track record are encouraged to apply. 
• Deep experience in at least one of the following: computer architecture and accelerator design; parallel, distributed, or high-performance computing; numerical methods and scientific simulation; AI/ML theory and model design at a mathematical level. 
• Strong ability to analytically model and optimize the performance of complex systems and algorithms. 
• Track record of published research or patents in relevant venues. 
• Proficiency in C and Python; comfort working close to hardware. 
• Excellent communication and interpersonal skills: able to present complex technical material to both specialist and cross-functional audiences, and to collaborate effectively in a fast-paced, small-team environment. 

Areas Of Particular Interest 

We are hiring across several focus areas. Exceptional depth in one or more of the following is a strong signal: 

• Computational science: researchers who can bring insights from numerical methods and simulation into AI, or couple simulation and learning into joint computational workflows. Depth in hydrodynamics, solid mechanics, electromagnetics, molecular dynamics, or related PDE-based fields. 
• AI/ML foundations: deep understanding of model architecture, optimization methods, and their statistical underpinnings—the ability to design from first principles, not just apply established recipes. 
• Computer architecture: microarchitecture design, computing paradigms at the circuit and datapath level, memory hierarchy design. 
• Performance engineering: roofline modeling, bandwidth analysis, kernel optimization, communication-computation overlap, and compiler-level tuning for novel hardware. 

Why This Opportunity Is Exciting And Unique 

• Build on a fundamentally different architecture, unconstrained by GPU assumptions. 
• Publish and open-source your research. We present at Supercomputing, SIAM, IEEE, NeurIPS, and beyond. 
• Work on the fastest AI system in the world, with direct access to the hardware your compiler targets. 
• Join at a pivotal moment: Cerebras is pre-IPO with strong commercial traction and rapid growth. 
• Be part of a small, technical team with high autonomy, minimal bureaucracy, and a culture that values depth over hierarchy. 

We are hiring for multiple positions across experience levels. If this work resonates, we encourage you to apply. 

About The Team 

Cerebras builds wafer-scale AI processors—single chips delivering tens of PB/s of memory bandwidth and a dataflow architecture that accelerates at a granularity no multi-device system can match. The Advanced Technology Group (ATG) is Cerebras’ pathfinding organization. We work ahead of product to explore new architectures, demonstrate breakthrough performance on scientific and AI workloads, and shape the technical roadmap for future Cerebras hardware and software. Our work regularly appears at top-tier venues (Supercomputing, SIAM, IEEE, and NeurIPS) and directly influences the design of next-generation wafer-scale systems. 

About The Role 

We are seeking Compiler Engineers to join a small team of specialists working on our emerging Tungsten language compiler. Tungsten is Cerebras’ dataflow programming language, purpose-built for wafer-scale hardware. You will work on the Tungsten compiler from language design through code generation, building the toolchain that translates high-level intent into efficient execution across hundreds of thousands of cores with a memory and interconnect model unlike anything in conventional computing. 

This is not incremental work on an existing backend. The architecture is new, the programming model is new, and the compiler is where those two things meet. You will collaborate closely with Cerebras’ ASIC, kernel, and AI teams, and your design decisions will directly shape both the language and the hardware it targets. Beyond the compiler itself, the broader toolchain—runtime, debugger, simulator—is still being built, and we are equally interested in engineers who want to own those pieces of the developer experience on novel hardware. 

What You Will Do 

• Design and implement compiler passes across the Tungsten toolchain: mid-end optimization, backend code generation, instruction scheduling, register allocation, assembler, and linker. 
• Co-design language constructs that improve expressiveness and performance for dataflow execution on wafer-scale hardware. 
• Develop and iterate on code generation strategies for complex scientific and AI workloads, analyzing performance bottlenecks and closing the gap between peak and achieved throughput. 
• Extend the compiler to support future hardware architectures as they move from design to silicon. 
• Work directly with ASIC architects and application researchers to inform hardware-software co-design decisions. 

What We Are Looking For

• PhD in Computer Science or Computer Engineering preferred; exceptional candidates without a graduate degree who demonstrate equivalent depth through published research, significant open-source contributions, or a strong industry track record are encouraged to apply. 
• Substantial experience in compiler development: IR design, optimization passes, code generation, or backend implementation for novel or non-standard architectures. 
• Strong grasp of computer architecture: instruction sets, memory models, dataflow execution, and how hardware constraints shape compilation strategy. 
• Systems-level programming ability in C; comfort reasoning about performance at the instruction and memory-access level. 
• Ability to think about compilation as a design problem, not just an implementation task: you should have opinions about how language semantics, compiler IR, and hardware capabilities interact. 
• Excellent communication and interpersonal skills: able to work effectively in a small, fast-moving team where compiler, architecture, and application concerns are deeply intertwined. 

Valuable Assets 

• Experience with compilers for spatial, dataflow, or CGRA architectures where the compilation model diverges significantly from conventional CPU/GPU targets. 
• Exposure to ML compiler frameworks (MLIR, XLA, TVM) and understanding of how AI workloads map to hardware. 
• Experience with multi-dimensional data representations, tiling strategies, and vectorized operations. 
• Track record of published research or patents in compilers, programming languages, or architecture. 
• Experience building runtime systems, debuggers, or architecture simulators, particularly for non-standard hardware. 
• Understanding of parallel/distributed systems and cluster computing. 

Why This Opportunity Is Exciting And Unique 

• Build a compiler for hardware that doesn’t exist anywhere else. The architecture is the constraint and the opportunity. 
• Publish and open-source your research. We present at Supercomputing, SIAM, IEEE, NeurIPS, and beyond. 
• Work on the fastest AI system in the world, with direct access to the hardware your compiler targets. 
• Join at a pivotal moment: Cerebras is pre-IPO with strong commercial traction and rapid growth. 
• Be part of a small, technical team with high autonomy, minimal bureaucracy, and a culture that values depth over hierarchy. 

We are hiring for multiple positions across experience levels. If this work resonates, we encourage you to apply. 

What You'll Be Doing:

• Design and build scalable data pipelines, batch jobs, and streaming processes that power analytics and machine learning.
• Implement and optimize data storage systems (warehouses, lakes, analytical databases) with a focus on reliability, lineage, and governance.
• Develop tools and frameworks to support data orchestration, ETL workflows, and big data processing.
• Collaborate with engineers, data scientists, and product managers to align platform capabilities with business needs.
• Ensure data systems meet requirements for quality, scalability, and cost efficiency in cloud environments.
• Contribute to technical discussions, code reviews, and system design sessions.
• Take ownership of features and projects, driving them from concept to production.

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.

Location: Remote (U.S. or Canada)

Type: US Applicants – Full-Time; Canadian Applicants – Independent Contractor

About Human Agency

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

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

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

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

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

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

The Opportunity

Human Agency is an AI-native consultancy that builds, ships, and operates alongside our clients. We don’t advise from the sideline — we embed inside enterprise organizations and deploy production AI systems that do real work.

Our current anchor engagement is inside one of the largest privately held construction enterprises in the United States — LLM orchestration, document intelligence, workflow automation, enterprise integration. Not strategy decks. Not innovation labs. Systems that run.

We need a Managing Director of Technology to own the technical side of the house as we scale Human Agency. That means leading client delivery, making architecture calls, building the engineering team, and being the technical voice in rooms with C-suite executives. You’ll work closely on strategy with both the Founder and the Managing Director of AI — but the technical execution, the team, and the platform are yours.

This role requires someone who can move between writing production code and presenting to a client’s CEO in the same week. If that sounds like whiplash, this isn’t the right fit. If that sounds like Tuesday, keep reading.

What You’ll Do

Lead technical delivery for enterprise clients. You’re the most senior technical person in the room on our biggest engagements. You decide what’s feasible, design the architecture, and make sure it ships. When a client executive asks “can we do this?” — you’re the one who answers.

Build how we deliver, not just what we deliver. One client engagement works because of talented people and brute effort. Ten engagements work because of systems. You design the repeatable model — how we scope, staff, build, and deploy — so the team can run engagements without you in every meeting. That means building discovery frameworks, reference architectures, deployment templates, and evaluation routines that actually get used.

Hire and develop the engineering team. You set the bar, own the pipeline, and make the calls on who joins. We need people who can build with high autonomy in ambiguous environments, and you know how to find them. You also know how to make good engineers better through architecture guidance, code review, and honest feedback.

Own platform and infrastructure decisions. Build vs. buy, tooling, deployment, security — you make the calls. A Principal Engineer handles day-to-day infrastructure execution; you set priorities and architectural direction.

Shape our AI strategy through the lens of what actually ships. You have real opinions on LLM orchestration, RAG, agents, evaluation, and fine-tuning — formed by building things, not reading about them. You decide which technical bets we make and which we skip, based on what creates value for clients, not what looks good on a slide.

What We’re Looking For

• You’ve built and shipped AI/ML systems in production. Not prototypes. Not demos. Things that run and that people depend on.
• You’ve hired and led a technical team — at least 5 people, ideally more. You’ve seen what breaks when a team grows and you’ve fixed it.
• You can explain a technical trade-off to a non-technical executive in a way that helps them make a decision. You’ve done this in high-stakes settings.
• You have strong opinions on AI architecture grounded in hands-on experience — and you change those opinions when the evidence says to.
• You’re comfortable in the weeds. When something is broken or a team is stuck, you get in and help, not schedule a meeting about it.
• You know your way around cloud infrastructure and engineering practices well enough to make platform calls with confidence.

Bonus points for: you’ve scaled a startup or early-stage company before — built the team, built the systems, lived through the growing pains; experience in a services or consultancy model (SOWs, staffing, client delivery); a network of engineers you’ve worked with and would hire again.

You Should Apply If

• You’d rather build AI systems than talk about AI strategy
• You want to own a technical organization, not manage a lane inside someone else’s
• You’re comfortable making big decisions with imperfect information — and owning the outcome
• You want to work somewhere with real revenue, real clients, and real problems to solve
• You’re energized by building a company — not just writing code, but shaping how the whole thing works

You Should Not Apply If

• You want a CTO-like title with a strategy focus and minimal hands-on building
• You’re uncomfortable getting into production-level detail when the situation calls for it
• You expect an established engineering org with playbooks, processes, and a support structure already in place
• You’d rather delegate technical depth than engage directly
• Your experience is solely in product companies and you’re not interested in building for clients

Considerations

Education: Bachelor’s degree or equivalent experience; advanced degrees are a plus but not required

Ethics: Commitment to responsible AI practices — transparency, human oversight, privacy

Travel: Periodic (10–20%) for client work, team offsites, and events

Location: Remote-friendly; U.S. or Canada, with preference for EST timezone alignment

Why Join Human Agency

We’re profitable, growing, and building AI systems that run in production for billion-dollar companies. The team is small, the clients are real, and the work matters.

The work is real and the opportunity is massive. We’re building a company — the systems, the team, the delivery model, the culture — and we need someone to own the technical side of that build.

If you’re the kind of person who’d rather build the engine than describe the engine, we want to talk.

Equal Opportunity Commitment

Human Agency is an Equal Opportunity Employer. We value diverse perspectives and are committed to building inclusive, high-performing teams where everyone can do their best work.

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

About Human Agency

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

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

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

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

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

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

The Opportunity

At Human Agency, we are shaping the future by combining cutting-edge AI solutions with intuitive software development. We specialize in building scalable and secure SaaS applications that empower enterprise clients to solve complex problems efficiently. As part of our team, you'll work on high-impact projects at the intersection of AI, machine learning, and secure software engineering.

We’re seeking a Senior Full-Stack Engineer to join our growing team. In this role, you'll lead development initiatives, contribute to our SaaS products, and tackle exciting challenges in backend architecture, frontend design, and AI/ML integrations with a particular focus on generative AI technologies.

Responsibilities

SaaS Application Development

• Detection:
• Develop backend algorithms to process articles, chunk content, and integrate with LLMs for predictive text generation.
• Build APIs and services to manage and scale the application’s content workflow.
• Protection:
• Create secure PDF upload workflows with encryption to ensure compatibility with Adobe Acrobat.
• Integrate authentication mechanisms like Okta for enterprise customer admin management.
• Enable data recovery, auditing capabilities, and enterprise-grade administrative tools.

AI/ML and Algorithm Development

• Collaborate with AI/ML teams to operationalize models such as OCR (Optical Character Recognition) and generative AI-based solutions (e.g., GPT models).
• Implement RAG (Retrieval-Augmented Generation) use cases using vector databases and data embedding techniques to enhance application functionality.
• Design workflows for large-scale data processing that enable efficient AI integrations and create dynamic AI-first features, such as custom GPTs tailored to enterprise needs.

DevOps & Operationalization

• Establish CI/CD pipelines and formal release structures for software delivery.
• Conduct security evaluations to identify vulnerabilities and optimize performance across AI-driven SaaS applications.
• Manage infrastructure and workflows for custom AI solutions, including tools like Snowflake and LangChain.

Future-Focused Development

• Build integrations and proof-of-concept applications leveraging company-specific datasets and AI technologies.
• Evaluate and implement tools for classification, indexing, and secure data management that incorporate the latest in generative AI advancements.
• Support the development of enterprise-grade AI solutions, including internal AI models and custom LLMs.

Requirements

Core Technical Skills

• Proficiency in Python and Ruby on Rails, with significant backend experience.
• Experience in designing secure systems, including encryption workflows and document protection mechanisms.
• Hands-on expertise in integrating generative AI tools, such as OpenAI APIs, LangChain, or Hugging Face frameworks, into SaaS applications.
• Strong knowledge of RAG techniques, vector databases (e.g. Pinecone, Weaviate), and embedding pipelines.
• Ability to operationalize AI/ML models and build workflows for enterprise clients.
• Frontend development experience in modern frameworks (e.g, React, Vue), with the ability to assess and recommend appropriate tech stacks.

Nice-to-Have Skills

• Computer vision and OCR expertise, especially for processing and indexing complex document workflows.
• Experience with authentication systems (e.g., Okta) and enterprise administrative tools.
• Experience in DevOps, CI/CD pipelines, and secure release management.
• Background in security evaluations, vulnerability assessments, and tool integrations.

Other Requirements

• Strong testing background with experience in automated and manual testing.
• Ability to manage data recovery, audits, and compliance workflows.
• Strong problem-solving and analytical skills, with attention to detail in complex environments.
• Excellent communication skills and the ability to work collaboratively across teams.

Why Join Us?

• Be part of a cutting-edge team solving complex problems in AI and SaaS development.
• Work on impactful projects with enterprise clients and cutting-edge technologies.
• Grow your career in a collaborative, innovative, and supportive environment.

If you're excited about working on secure, scalable systems while integrating advanced AI and ML capabilities, we’d love to hear from you.

Equal Opportunity Commitment

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

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 

The Team:

Our Business Applications and Analytics team plays a pivotal role in driving business success through data-driven decision-making and operational excellence. This dynamic team manages enterprise-wide applications, including ERP, CRM, and BI systems. By harnessing the power of data and advanced analytical tools, we provide valuable insights that inform strategic initiatives, optimize processes, and enhance overall performance.  

Position Overview:  

Diligent is ‘All-In’ on AI and is seeking a dynamic and strategic AI leader to join our team as an AI Solutions Architect. As part of the Business Applications and Analytics team, this crucial role is designed to accelerate our Internal AI capabilities across multiple business functions, including Marketing, GTM/Sales, Customer Success & Support, Finance, HR, Legal, and Product and Engineering teams. 

The primary goal of this position is to identify, prototype, deploy, and scale AI technologies and solutions that drive greater operational efficiency and effectiveness. The ideal candidate will be a technically adept individual with business acumen, able to bridge the gap between robust technical implementations and business needs, with the ability to partner with various business leaders to spot and act on opportunities for AI-driven transformation and innovation. 

Key Responsibilities:

• Partner with business leaders to understand challenges, identify AI opportunities, and implement effective AI/GenAI solutions.
• Lead the ideation, prototyping, deployment, and scaling of AI/GenAI technologies and agentic solutions tailored to business needs.
• Develop and communicate strategic frameworks and roadmaps for AI initiatives aligned with business goals.
• Champion best practices in AI/ML and GenAI development—including responsible AI, privacy, security, and ethical guidelines.
• Collaborate with cross-functional teams to integrate AI tools within core business processes, ensuring seamless, high-value adoption.
• Stay abreast of AI best practices, tools, and methodologies and introduce them to the organization.
• Drive AI and Data literacy across the company through training, workshops, and continuous education initiatives.
• Ensure compliance with data protection and privacy regulations in all AI implementations.
• Monitor and evaluate the performance and effectiveness of AI solutions and continually refine them.
• Collaborate with stakeholders to define and achieve key performance indicators (KPIs) and return on investment (ROI) for AI projects.
• Advocate and implement AI ethical guidelines and best practices.

Required Qualifications:

• Solid Foundations: Bachelor’s degree in Computer Science, AI/ML, Data Science, or a related technical field.
• Practical Tinkerer: 5+ years in a technical role (software, data, DevOps, etc.), with hands on experience building end-to-end prototypes using GenAI tools (GPTs, Hugging Face, open-source LLMs, LangChain, Scikit-learn, etc.).
• Curious Explorer: Demonstrates a hands-on approach to learning in prompt-engineering, fine-tuning, RAG pipelines, or API Integrations, with a commitment to rapid failure, iteration, and sharing findings.
• Data Wrangler: Possesses the ability to source, clean, and transform raw data, including spreadsheets, logs, APIs, or CRM exports, into structured inputs for AI experiments and prototypes.
• Code Crafter: Capable of writing scripts or small applications to connect models, cloud services, or data sources. Prior experience with TensorFlow, PyTorch, Azure, AWS, or GCP is beneficial, but willingness to learn is equally important.
• Business Translator: Experienced as a technical sales engineer or solutions consultant, capable of turning AI experiments into compelling stories and metrics that resonate with non-technical stakeholders.
• Responsible AI Mindset: Demonstrated experience in addressing privacy, security, bias, and ethics in professional work, with a strong familiarity with industry-specific data privacy, AI ethics, and compliance requirements.
• Team Player: Essential qualities include clear communication and a growth mindset, as you will collaborate with marketers, sales professionals, engineers and leaders.

Preferred Qualifications:

• Demonstrates strong knowledge of AI use cases, including NLP, computer vision, data analytics, predictive modelling, and translating technical capabilities into business value, along with proficiency in large language models (LLMs), prompt engineering, RAG architectures, MLOps, LLMOps, and GenAI application deployment.
• Advanced programming and data engineering skills, with Python preferred, and experience in cloud-based AI solutions (AWS, GCP, Azure).
• Ability to architect and deploy enterprise-scale AI solutions, collaborating across sales, product, and implementation teams to ensure adoption and measurable ROI.
• Strategic thinker with strong analytical and problem-solving skills, capable of driving measurable business outcomes through AI/GenAI.

Who we are:

MasterClass is the streaming platform where the world’s best come together so anyone, anywhere, can access and be inspired by their knowledge and stories. We put you in the room with the creators, thinkers, makers and leaders who have changed the world, so that you can change yours.

Members get unprecedented access to 200+ instructors and classes across a wide variety of fields, including Arts & Entertainment, Business, Design & Style, Sports & Gaming, Writing and more. Step into Nas’ recording studio and Gordon Ramsay’s kitchen, and go behind the big screen with James Cameron. Design your career with Elaine Welteroth, get ready to win with Lewis Hamilton, perfect your pitch with Shonda Rhimes and discover your inner negotiator with Chris Voss.

We’re a remote-first workforce with collaborative work spaces in San Francisco and Kitchener, Ontario, and employees in several U.S. states. If you’re interested in joining a dynamic, culture-driving company—where learning invaluable
skills is all in a day’s work—we invite you to apply.

Snapshot of the Role:

We’re looking for a Staff Infrastructure  Engineer who operates as a technical leader and force multiplier across the platform. This is a senior individual contributor role for someone who designs systems that scale beyond a single team, sets technical direction, and elevates how infrastructure is built and operated across MasterClass.

You’ll lead and provide direction for  cloud infrastructure, developer platforms, video infrastructure, and emerging AI/ML infrastructure—partnering closely with product engineering, content production, data, and AI teams. This role is ideal for someone who combines deep systems expertise with strong software engineering, thrives in ambiguity, and takes ownership from strategy through execution.

This is not a people-management role, but it carries significant technical leadership responsibility.

What You Will Do:

• Set technical direction for core infrastructure systems, balancing reliability, scalability, security, and developer velocity.
• Design and lead implementation of complex, cross-team infrastructure initiatives (AWS, EKS, CI/CD, shared platforms).
• Build production-grade software to power infrastructure automation, tooling, and internal platforms.
• Drive org-wide improvements in observability, incident response, reliability engineering, and cost efficiency.
• Lead video infrastructure initiatives  to evolve video delivery and CDN strategies for global scale and performance.
• Partner with AI and Data teams to define and build GPU-powered infrastructure, model-serving platforms, and GenAI inference/training workflows.
• Act as a technical mentor and reviewer, raising the bar for infrastructure design and operational excellence across teams.
• Lead by example during incidents, postmortems, and critical architectural decisions.

About You (Requirements):

• 10+ years building and operating cloud-native production systems.
• Strong expertise in:
  • AWS architecture and large-scale cloud operations
  • Kubernetes / EKS (networking, RBAC, Helm, autoscaling, cluster-level debugging)
  • Infrastructure as Code (Terraform preferred)
  • CI/CD and automation (GitHub Actions, Argo, or similar)
• Proven ability to design and build infrastructure software, not just operate systems.
• Deep understanding of distributed systems, networking, Linux internals, and cloud security.
• A track record of driving step-function improvements in reliability, scalability, or developer experience.
• Strong written and verbal communication skills; able to influence technical decisions across teams.

Who You Are

• You think in systems and leverage, not just tasks.
• You care deeply about craftsmanship, clarity, and long-term maintainability.
• You enjoy solving ambiguous, cross-functional technical problems.
• You’re pragmatic—you optimize for impact over novelty.
• You raise the bar through mentorship, design reviews, and thoughtful collaboration.
• You’re energized by both strategic architecture and hands-on implementation.

Nice To Haves

• AI/ML infrastructure, GPU scheduling, or distributed training
• Model serving frameworks (e.g., Triton, vLLM, TensorRT-LLM)
• Infrastructure autoscaling strategies, including application workloads, databases, and GenAI workloads
• KEDA, Karpenter, service mesh, or multi-cluster architectures
• HLS/DASH packaging or video encoding pipelines
  
  

  Nice-to-haves are not required; each represents a direction in which our infrastructure is evolving.

At MasterClass, we believe we put our best work forward when our employees bring together ideas that are diverse in thought. We are proud to be an equal opportunity workplace and are committed to equal employment opportunity regardless of race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or any other characteristic protected by applicable federal, state or local law. In addition, MasterClass will provide reasonable accommodations for qualified individuals with disabilities.  If you have a disability or special need, we would like to know how we can better accommodate you.

Sony Pictures Imageworks is located on the unceded traditional territory of the Musqueam, Squamish, and Tsleil-Waututh First Nations. We are committed to respecting traditional lands, and working with communities towards reconciliation.

Sony Pictures Imageworks Canada Inc.
658 Homer St

Vancouver, BC V6B 0T5

Sony Pictures Imageworks is an Academy Award®-winning visual effects and animation studio known for photoreal live-action visual effects, dynamic creature and character animation, and full-CG features.

Role Review:

We are looking to expand our development team dedicated to enhancing our Creature Effect, Crowds, Environment, and FX tools and workflow. We are looking for a highly proficient Technical Engineer to focus on the development and implementation of robust, high-performance, and domain-specific procedural toolsets using SideFX Houdini.

This role requires strong technical leadership and the ability to work effectively with minimal supervision alongside a Lead Software Engineer or Architect. The ideal candidate thrives in a fast-paced production environment where priorities can shift quickly.

What You’ll Be Doing:

• Design, develop, and implement scalable procedural systems and workflows within Houdini for various departments (FX, Creature, Crowds, Environments). 
• Create, maintain, and optimize robust Houdini Digital Assets (OTLs), ensuring stability, ease of use, and adherence to production standards.
• Extend the core pipeline by developing, integrating, and supporting tools that manage data flow, asset publishing, and version control.
• Collaborate closely with artists (Creature, FX, Lighting) to gather requirements and troubleshoot technical issues, providing support for both in-house and commercial tools.
• Partner with engineers across other teams (Shading, Lighting, Rendering) to ensure seamless integration of procedural assets into the overall rendering and compositing pipeline.
• Document tools and workflows thoroughly for use by the wider artistic and engineering teams.

Required Technical Experience & Skills:

• 3-5+ Years of professional experience in developing procedural workflows and tools in a VFX, Animation, or Game production environment.
• Demonstrated knowledge of the Houdini environment, including tool creation, optimization, and workflow techniques.
• Strong proficiency in Python and VEX for writing efficient, custom nodes, and tool wrappers.
• Proven experience developing and supporting production pipeline tools and asset delivery.
• Experience leveraging the Houdini API and other DCCs like Katana.
• Strong grasp of 3D math, linear algebra, and data structures as applied to geometry processing and simulation.
• Strong verbal and written communication skills, with a collaborative approach to problem-solving.
• Capable of delivering on multiple competing priorities with little supervision.

Preferred skills:

• Applies curiosity and judgment to identify broader or systemic issues and recommends creative approaches that address wider issues.
• Documented experience with desktop application development using PyQt/PySide to create custom user interfaces.
• Experience with C++ for high-performance plugin development.
• Experience with data science, machine learning, or complex simulation techniques (e.g., fluid dynamics, cloth) in a production context.
• Bachelor’s or Master's Degree in Computer Science, Digital Media, or a related technical field.

The anticipated base salary for this position is within the range of $100,000.00/yr - $120,000.00/yr CAD. Benefits are per company policy: which include healthcare, tuition reimbursement, RRSP's, Sick and Vacation leave, standard increases as applicable. The actual base salary offered will depend on a variety of factors, including without limitation, the qualifications of the individual applicant for the position, years of relevant experience, level of education attained, certifications or other professional licenses held, and if applicable, the location of the position.

We value unique perspectives, and want diverse, unique talent to work with us.  We encourage candidates from all identities to apply.

*Sony Pictures Entertainment is an equal opportunity employer. We evaluate qualified applicants without regard to race, colour, religion, sex, national origin, disability, age, sexual orientation, gender identity, or other protected characteristics.