Production Tech in Canada

About Snorkel At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! In September 2026 we raised a $350 million Series E at a $3.5 billion valuation , and we are scaling our engineering and research teams to meet demand. The role Frontier AI data is expensive to make and hard to measure. Every task we deliver is tested against the strongest models, often through many long-running agent rollouts. Your job is to make that process faster, cheaper, and more rigorous with ML and AI You will be one of the early members of ML & Research Engineering at Snorkel. You will study how frontier-grade data is generated and evaluated, form hypotheses, validate them against real production data, and ship the winners at scale. You will shape the discipline's direction, its standards, and the team that grows around it. What you'll work on Efficient agentic evals. Cut the cost of long-horizon agent evaluation with adaptive sampling, statistically grounded early stopping, model cascades, caching, and cheap-first gating. AI model routing. Route every eval and judge call to the cheapest model that clears the quality bar, with fallback, monitoring, and cost attribution. Fine-tuned small models. Fine-tune and serve open-weight models (LoRA and other

About Snorkel At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! We’re looking for a Research Scientist to advance how high-quality data and environments for AI agents are created. You’ll build and optimize pipelines that combine real-world data, automated generation, and human expert input. Working with domain experts, academic partners, customers, and our product and engineering teams, you’ll scale these pipelines to target frontier model performance gaps and expand data and environment diversity. Your work will amplify human knowledge and judgement, enabling experts to create and refine data and agentic environments that strengthens Snorkel’s position as the frontier data lab. This role is ideal for someone who wants to advance frontier AI through data and environment creation and enjoys turning research into reusable, scalable systems. Location: San Francisco, New York, OR REMOTE Main Responsibilities Design, implement, and optimize reusable pipelines that combine AI capabilities with expert judgment to accelerate data and agentic environment creation. Design and run rigorous experiments to validate proof-of-concept approaches, measure their impact on data quality, pipeline efficiency, and model performance, and communic

About the Team DoorDash Labs is an independent team within DoorDash. We explore robotics and automation to transform last mile logistics in the long term. If you have a passion for applying robotics solutions in a service used by millions of people, then we want to talk to you! About the Role We are hiring an Autonomy Platform Engineer to build and evolve the foundational software that runs our autonomy stack across robot and compute platforms. The Autonomy Platform team works across embedded Linux, compute and sensor enablement, robotics middleware, process orchestration, data capture and replay, system observability, and performance. You will develop production software and tooling that enables autonomy engineers to bring up new hardware, deploy services reliably, diagnose failures, and validate system performance across robot generations. You will work closely with autonomy, firmware, electrical, hardware, manufacturing, and validation engineers and report to the Autonomy Platform Lead. We expect this role to be hybrid with some time in-office and some time remote. You’re excited about this opportunity because you will… Build and maintain core runtime, middleware, and platform services used by autonomy applications. Enable new compute, camera, lidar, and other sensor platforms. Improve process orchestration, messaging, configuration, startup and shutdown behavior, resource isolation, and fault recovery. Develop system observability, tracing, performance measurement, diagnostics, and regression-detection capabilities. Build reliable data capture, replay, and debugging workflows. Create provisioning, packaging, deployment, integration-test, and platform-readiness tooling. Lead complex debugging across application, middleware, OS, driver, networking, timing, and hardware boundaries. We’re excited about you because… Strong production C++ and Python experience. Experience with embedded Linux, robotics, autonomous vehicles, or complex mechatronic systems. Solid u

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose. Position Overview: Do you want to fast track your career? Are you an adept communicator with internal and external stakeholders? We are looking for an analytical Rockstar to join our fast-paced and hardworking Freddie Mac Multifamily Underwriting team. We’re looking for someone who is smart, a fast learner, strong with numbers and can hustle. Apply now to contribute to our mission of Making Home Possible. Our Impact: We are responsible for underwriting conventional multifamily loans originated by our Production partners Innovate based on market behaviors while efficiently analyzing, mitigating, and clearly defining credit risk Evaluating the overall story and making decisions on the credit risk profile Your Impact: Build toward credit approval and closing individual mortgage loans collateralized by multifamily properties Accurately prepare concise, complete, and clear Investment Briefs for loan approval and loan commitment Apply company principles/policies and critical thought to complete assigned tasks accurately, completely, and in a timely manner Collaborate and communicate with external and internal business partners to solve problems and achieve shared success Qualifications: Bachelor’s degree in real estate, finance, economics, business administration, or related field 1 to 3 years of related work experience in the commercial/multifamily real estate industry Knowledge of real estate property fundamentals and real estate lending/underwriting Strong written and verbal communicati

About the Role: We're hiring Senior and Staff Data Platform Engineers to join the Data Infrastructure teams in Toronto. Together these teams own the infrastructure that processes billions of events per day: Spark-on-Kubernetes, Flink and Kinesis pipelines, a multi-petabyte Delta Lake, a large-scale MemoryDB feature store, Databricks multi-environment operations, and the catalog and lifecycle systems that govern it. The team is small and senior. Each engineer owns major platform components: you design it, build it, and support it in production. This is a hybrid-role based out of our Toronto office. You must be willing to travel to our Toronto office two days/week. What You'll Do: Spark-on-Kubernetes — EKS-based compute platform for Spark workloads: cluster configuration, Pod Identity IAM, job environment setup, Kustomize overlays, and shadow canary validation Event ingestion — Rust services and Flink jobs processing billions of events per day over Kinesis; throughput, reliability, on-call response, and AI-assisted operational tooling to reduce toil Platform infrastructure — Terraform modules for environment provisioning, cross-account AWS IAM, ARC runner infrastructure, and CI/CD for data platform changes Feature store and ML compute — Flink-based real-time feature pipelines feeding a large-scale MemoryDB cluster; GPU capacity governance and Databricks multi-environment operations for ML training workloads Workflow orchestration and CDC — Airflow-based DAG deployment, change data capture pipeline operations, and data quality monitoring Your Background: 3+ years building and operating production data platform infrastructure at the cluster or platform level, across Spark, Flink, Kinesis, Kubernetes, or equivalent Deep experience in at least one of: Spark-on-K8s cluster operations, Rust-based data or systems engineering, Kubernetes platform engineering and IaC, or data catalog and governance tooling Production AWS experience or equivalent: EKS, S3, Kinesis, and mu

About Snorkel At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! The Role We're looking for our founding AI Data Product Manager to own Snorkel's Agentic Data and RL Environments roadmap. In this role, you'll lead the product strategy for a variety of data types (e.g. Agentic Coding, Computer Use). You will shape the roadmap for the datasets Snorkel invests in by understanding the market, incorporating frontier lab needs and collaborating with researchers at Snorkel and our academic partners. This role is highly cross-functional, sitting between Research, GTM and Operations. As a founding member for this role, you will be in charge of setting up the frameworks to build the roadmap, gather data from relevant sources, and share the roadmap with both internal and external stakeholders. What You'll Do Own the "data as a product" roadmap for Snorkel's Agentic and RL Environment focus areas, working x-functionally with research, academic partners, and GTM to define the skills and capabilities for our datasets Shape new "data" product areas and work with academic partners and research leaders to build Snorkel's competitive edge in the market Collaborate cross-functionally to help shape the roadmap and data strategy and influence bu

The Opportunity Join Enigma's Growth & KYB Team at a pivotal moment as we continue to create sales and marketing solutions for small businesses. We're seeking an experienced Senior Software Engineer to develop and build innovative data products and systems that directly impact our customers and accelerate our growth. Your work will influence decisions for companies that employ half the U.S. workforce, creating tremendous value for our clients. The Role As a member of the Growth & KYB Team, you will: Design, develop and deliver data products that solve critical customer pain points and build scalable, high-quality systems that deliver rich interactions with our data. Engage with software engineers, data scientists, and product managers to create experiences that move a customer from discovery to evaluation to purchase. Analyze and extract value from data at scale, building efficient, maintainable production-grade data solutions. Create scalable and highly maintainable systems deployed in cloud environments. Own architecture and design decisions as well as hands-on implementation, ensuring that customer experiences are compelling and have low costs of ownership. What You’ll Do Operates with a bias for action and knows how to deliver value in the short, medium, and long term. Loves interacting with product teams and works hard to solve customer problems in a repeatable way. Adopts a principled, metrics-driven approach to difficult data problems and demonstrates excellence in their analytics and engineering craft. Operates transparently, collaboratively, and with low ego—loves learning from others and having their ideas questioned and challenged. Has a “can-do” attitude and thrives on a highly collaborative, cross-functional team that plays a critical role in the company’s success. Is eager to own architecture and design decisions as well as hands-on implementation. What Makes This Role Exciting? Impact : Develop products that take an innovative data-first appro

About the Team The Code Quality team sits within the Developer Platform organization and owns the systems that keep DoorDash's codebase healthy and secure as it scales: static analysis, quality gates, test frameworks, regression infrastructure, and tooling. Our job is to make sure the signals engineers rely on before shipping — test results, coverage, performance feedback etc — are fast and trustworthy. The decisions we make about tooling and standards directly shape how confidently and quickly engineering teams at DoorDash can ship to production. About the Role We're looking for Software Engineers to help build and maintain the systems that validate code quality across DoorDash's engineering org, treating our tooling as a critical product for the engineers who rely on it every day: static analysis and quality gates, test frameworks and regression infrastructure. You’ll design the tooling and automation that will help derive trustworthy quality signals, integrate them into the development lifecycle, and make it easy for engineers to execute reliable, repeatable workflows. You will collaborate across the engineering org, partnering directly with the teams who use what you build to understand the accuracy, reliability and performance of their functionality. You will report into the Engineering Manager on our Code Quality team in our Developer Platform organization. You must be located in either San Francisco, CA, Sunnyvale, CA, Los Angeles, CA, Seattle, WA, or New York, NY. You're excited about this opportunity because you will… Build and maintain quality tooling — static analysis, quality gates, coverage reporting, test frameworks, regression infrastructure — and integrate it directly into our developer workflows and CI/CD pipelines Define and derive quality signals - flakiness, pass rate, coverage, performance, scale readiness etc - Build tooling that improves everyday engineering workflows, including local development, CI/CD, debugging, and rollou

About the Team The Consumer Engineering Team is responsible for helping consumers discover and order everything they love globally. Our work spans the entire consumer journey across homepage, search, store discovery, item exploration, checkout and post checkout. We aim to craft a hyper-personalized, delightful and frictionless experience for millions of our customers. About the Role As a Senior Staff Machine Learning Engineer on Core Cx, you will set the personalization (P13n) strategy for the entire consumer shopping journey and bring that strategy to life. You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across restaurant, grocery, retail and all business at DoorDash . You will modernize the recommendation system leveraging AI. You will demonstrate a strong command of production level machine learning, experience with solving end-user problems, and collaborate well with multi-disciplinary teams. You're excited about this opportunity because you will… Drive the engineering vision, strategy, and execution for an organization of 150+ Grow, build, and nurture impactful business-focused product engineering teams. Scale the team by developing leaders internally and attracting world-class talent Mentor and guide a fast-growing organization in setting the right architectural patterns, working with various vendors in the space, and making judicious investments in the right areas anticipating what the company needs a few years down the road. Partner with Business, Product, and other Engineering teams to transform DoorDash from local commerce to agentic commerce We're excited about you because you have… B.S. or M.S. in Computer Science or equivalent. 10+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. Proficiency in using AI coding tools (e.g., Claude Code) in th

About the Role We are a small team of AI builders in Paytm Labs. As a Staff AI Platform Engineer, you will work across inference and agentic systems. You will contribute to Paytm's AI inference platform (Pi), serving internal teams and enterprise customers - running our own coding and domain-specific models (voice, vision, risk, fintech workflows) as well as third-party models. You will also architect and build the platform that enables autonomous AI agents to operate safely and reliably in production - the runtime, orchestration, and developer tooling for agents to reason, plan, use tools, and execute complex multi-step workflows, automating both software development and business processes. You will work at the intersection of LLMs, distributed systems, and production fintech infrastructure, helping define how inference and agentic AI are built and deployed across payments, risk, fraud, collections, support, and developer experience.

A World-Changing Company Palantir builds the world’s leading software for data-driven decisions and operations. By bringing the right data to the people who need it, our platforms empower our partners to develop lifesaving drugs, forecast supply chain disruptions, locate missing children, and more. The Role We are a software engineering team with expertise in enabling ML models in production. We deploy AI models to run in variety of environments: air-gapped government networks, forward-deployed defense environments, edge nodes, and enterprises with strict data sovereignty requirements. Our customers rely on us for frontier AI capabilities running on hardware they control, often with constrained GPU resources and limited direct access. Rising to that challenge and meeting those expectations is what Palantir's excels at. We treat models like any other software: continuously tested, continually delivered, packaged for reproducible deployment, and built for long-term maintainability. You will own services end-to-end, and work across the full stack, from inference engines, GPU scheduling to deployment pipelines, observability, and integration with Palantir's platform. The goal is to deliver new models and capabilities quickly and continuously. Join us if you want to solve problems at the intersection of infrastructure and machine learning that directly enable critical customers.

Who we are About Stripe Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. What you’ll do Responsibilities Design state-of-the-art ML models and large-scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints. Design systems to speed up the time from idea to deployment of new models. Experiment and iterate on ML models (using tools including PyTorch and TensorFlow) to achieve key business goals and drive efficiency. Develop pipelines and automated processes to train and evaluate models in offline and online environments. Integrate ML models into production systems and ensure their scalability and reliability. Collaborate with product and strategy partners to propose, prioritize, and implement new product features. Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions. Who you are Minimum requirements Must have a Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of experience in Building and shipping ML systems in production. Must have two (2) years of experience in each of the following: ML algorithms and model architectures; Designing, training and evaluating machine learning models; Productionizing and deploying machine learning models at scale; Orchestrating data pipelines and leveraging large-s

About the role There’s a wide gap between an agent that works in a demo and one that works across millions of live transactions. Closing it is the job. You’ll embed with the teams and customers who depend on AI — risk, fraud, collections, payments, support, developer experience — and design, build, and ship agentic systems into their production environments. You’ll also help build the platform underneath: Paytm’s AI inference platform (Pi) and the agentic runtime, orchestration, and tooling that lets agents reason, plan, use tools, and run multi-step workflows safely.

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. We are looking for an experienced Staff Software Engineer in Test to join our Identity Management Engineering (IDM) team serving the Privileged Access Team (PAM). This team is passionate about delivering large-scale, mission-critical software in a fast-paced Agile environment. In this role you'll be working with a team of highly-skilled and talented engineers, responsible for delivering sophisticated backend solutions that help Okta reliably operate at large scale and be highly available. As part of the team, you’ll be ensuring projects are completed with the highest quality and reliability using automation at every level for fast, robust and secure releases. Job Duties and Responsibilities: Review requirements and design specs to develop relative test plans and test cases Automate API tests, end-to-end tests, reliability/scale tests Work with engineering management to scope and plan engineering efforts Communicate and document QE plans for scrum teams to review Review application code, identify bug and other areas of weakness, architect tools for future coverage Automate all critical features to maintain zero-debt cadence Release features with solid quality Respond to production issues/alerts and customer issues during on-call rotation Be a strong customer advocate with a strong quality DNA. Requirements: 5+ years of QE experience preferably in an enterprise SaaS company 3+ years experience in quality engineering for enterprise level software. 5+ yea

Role Summary Scale builds AI applications for organizations where reliability, security, and measurable results matter. Our core platform, SGP, gives teams the capabilities to build, deploy, evaluate, and operate those applications in customer environments. We're looking for a product leader to own the strategy and roadmap for SGP's core platform and lead the PM team responsible for it. You will work with customers, platform engineering, and forward deployed teams to build capabilities customers need, productize work developed in the field when it has broader value, and improve how teams build, deploy, and operate AI applications. This is a hands-on leadership role. You will connect work across teams into a coherent product, make difficult scope and investment decisions, and stay close enough to the details to know whether what we ship works for the people using it. What you'll do Own the platform strategy and roadmap. Prioritize across developer experience, capabilities customers need, and productizing work built in the field where it has broader value. Sequence investments against customer value, commitments, and dependencies. Define how the product should work. Work with users and engineers to write clear requirements and vibe code prototypes, user journeys, and measure success through customer outcomes, capability adoption, delivery time, and production performance, and follow through on gaps after launch. Align teams across the business. Work with platform engineering, forward deployed PMs, and business leaders to agree on shared capabilities, rollout priorities, and necessary differences across environments. Make clear what is available, where it works, and what remains to be delivered. Build and lead a strong PM team. Establish clear ownership across related product areas, hire and coach PMs, and raise the quality of product thinking and written requirements. Stay directly involved in the most consequential decisions. What we're looking for A track record of