Ai Systems Engineer in Canada

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.

Artefact is a next-generation data and AI consulting firm dedicated to accelerating the adoption of data and AI to create measurable business impact across the full enterprise value chain. We sit at the intersection of consulting, data science, AI technologies, data engineering, and digital transformation. We do not just advise — we build, implement, and deliver results our clients can measure. Our teams bring together consultants, data scientists, data engineers, AI engineers, analysts, and digital experts to solve complex business challenges with pragmatic, production-ready solutions. As Artefact continues to grow globally, we are building a team of entrepreneurial data and AI talent who can help clients move beyond experimentation and into scalable, governed, value-generating AI adoption. The Role Artefact is looking for a Senior AI & Agentic Engineer: a full-stack engineer who takes AI features from idea to production. You will design and build the interfaces, services, and agentic systems at the heart of our client work, such as conversational applications over enterprise data, multi-step agents that automate business workflows, and the retrieval and data pipelines that support them. You will own your components end to end: the React front end, the Python or Node service behind it, the RAG pipeline feeding it, and the evaluations proving it works. This role combines breadth and depth: the ability to take a feature from front end to cloud deployment, together with strong expertise in at least one major AI platform — Google (Gemini), Anthropic (Claude), or OpenAI. You will work with direct client exposure, and you will support the professional development of the junior engineers around you. What You'll Do Build Full-Stack AI Applications, End to End You will build AI products across the entire stack, from interface to infrastructure. Develop user-facing interfaces in TypeScript/React and the backend services and APIs behind them in Python or Node. Implement a

Director of Engineering, Physical AI Role Overview The Director of Engineering will report to the General Manager of Physical AI, and will be responsible for leading a multi-disciplinary engineering organization. In this senior leadership role, you will own the execution of the Physical AI Data Engine — the platform powering the next generation of Physical AI/Embodied AI. You will collaborate closely with Operations and GTM to guide product direction and help solve the data bottleneck that stands between today's robotics research and real-world deployment. This role requires significant ownership in a fast-paced environment and you will motivate internal teams to set the pace for business growth. Travel will come into play. Key Responsibilities: Set and drive the technical vision across data collection infrastructure, teleoperation systems, ML training pipelines, model evaluation frameworks, annotation tooling, and research Lead a multidisciplinary engineering organization—spanning engineering managers, software engineers, ML engineers, and ML research scientists—while designing the organizational structure, talent strategy, and culture required to scale rapidly without compromising on quality or strategic alignment Maintain exceptional technical and operational excellence by deeply understanding team deliverables, asking incisive questions, identifying slipping standards early, and knowing precisely when to step in Drive cross-functional alignment across Engineering, Operations, and GTM on platform architecture, release processes, and shared priorities Collaborate with researchers and clients to architect and deliver scalable, production-grade data infrastructure tailored for complex robotics workloads Required Qualifications: Bachelor's degree in Engineering, Robotics, Computer Science, or a related technical field 8+ years of engineering experience in fast-paced environments, including 4+ years direct people management demonstrated history of recruiting, mentorin

Scale AI is seeking a highly skilled and motivated Software Engineer, Frontier AI Infrastructure to join our dynamic Public Sector Engineering team. As a part of this team, you will own the model inference layer - enabling state of the art models, debugging the latest AI tools, managing networking, debugging latency, and tracking pricing/usage metrics for AI models. You will lead technical discussions on the frontlines with cloud vendors and customers to deliver on critical contracts and to debug platform issues. You will also work upstream with Product to understand features before they break, moving us from "infra-only debugging" to proactive integration testing. You will: Design and implement secure scalable backend systems for Public Sector customers, leveraging Scale's modern and cloud-native AI infrastructure. Own services or systems and define their long-term health goals, while also improving the health of surrounding components Re-architect the stack to run in compliant or restrictive environments. This requires designing swappable components (auth, storage, logging) to meet government/security mandates without breaking the product. You will work with Product to build integration tests that catch issues early, shifting the focus from "infra-only debugging" to preventing failures upstream. Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions. Contribute to the platform roadmap and product strategy for Scale AI's Public Sector business, playing a key role in shaping the future direction of our offerings. Must have: At least an active secret clearance and the ability & willingness to up level to TS/SCI with CI Poly. This is a requirement and candidates will not be considered who do not hold at least a secret clearance Ideally you'd have: Full Stack Development: Proficiency in both front-end and back-end development, including experience with modern web develo

About the Team OpenAI’s User Operations team shepherds our customers’ adoption of AI and ensures that our customers' product experience is nothing short of exceptional. We are building the very first post-AGI support team. We resolve complex issues, provide technical guidance, and support customers in maximizing value and adoption from deploying our products. We work closely with Sales, Technical Success, Product, Engineering and others, to deliver the best possible experience to our customers at scale. OpenAI's customers represent a range of diverse backgrounds and maturity, from early-stage startups to established global enterprises. About the Role We’re looking for dedicated, experienced, and deeply curious individuals to help solve some of the most complex challenges faced by our customers while building the future of post-AGI support alongside us. In this role, you’ll work directly with customers through support tickets and live calls, troubleshooting high-impact issues and resolving novel, often ambiguous technical problems in one of the fastest-moving environments in technology. As AI adoption rapidly accelerates, the work you do will directly support mission-critical systems being built on OpenAI’s platform, serving as a critical line of defense for customers operating at massive scale. Beyond resolving technical issues, you’ll help define what world-class support looks like in an AGI-driven future. You’ll partner closely with Engineering, Product, and Operations to improve systems, reduce bugs, and elevate the customer experience, while leveraging automation, agents, and our own AI technology to transform how support operates at scale. This Toronto-based role is currently remote and is expected to transition to an in-office arrangement. In this role, you will: Work directly with customers to troubleshoot and resolve their most complex technical issues, including API failures, integration challenges, authentication errors, and production incidents. Providing

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Backend Engineer on the GitLab Agent Observability team, you'll go beyond using AI tools and help define how we design, build systems that allow AI agents to interact with the full software delivery lifecycle, way beyond pure code creation. In this role you’ll contribute to the development of complex features and help establish architectural patterns both for how to interact with AI Agents across GitLab and how the resulting AI contributions manifest across GitLab. You’ll collaborate closely with engineers across the Agent Foundations stage and adjacent teams within AI engineering. This is a hi

Must be based in Vancouver The role We're hiring a dedicated data engineer to own the production data platform that our delivery, product, and engineering teams run on; designing integrated, governed data pipelines and delivering automated reporting, AI-assisted workflows, and predictive signals on top of them. You'll write production code, design systems, own CI/CD, and be accountable for the correctness of data that leaders make decisions on. What you'll do Design and operate our cloud data platform: ingestion, transformation, orchestration and serving. Integrate data from across the business (delivery tooling, CRM, product telemetry, finance, support and customer feedback systems) with shared identifiers, data contracts and lineage. Build automated and continuously refreshed reporting so teams manage by exception rather than chasing status. Connect approved AI agents to governed data with structured outputs, provenance, guardrails and human approval in the loop. Build feature pipelines and the MLOps controls behind predictive use cases: tests, versioning, promotion gates and drift monitoring. Own the engineering standards for data: testing, observability, environment promotion, PII classification and access control. What you'll bring Strong software engineering fundamentals: production-quality code, API and interface design, testing discipline, systems design. Real experience building and operating production data platforms on a cloud warehouse or lakehouse (Snowflake and AWS preferred) with dbt and a modern orchestrator. Practical AI tooling experience: something shipped, not prototyped. LLM-backed classification, extraction or structured-output pipelines; agent and tool-calling workflows; retrieval; evals. You can reaso

Join us in building the future of finance. Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading. ABOUT THE TEAM + ROLE We are building an elite team, applying frontier technologies to the world's biggest financial problems. We're looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn't a place for complacency, it's where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. The AI R&D team is at the core of Robinhood's product intelligence. Our mission is to build and scale high-impact models that power personalization, search, social feeds, fraud detection, and risk management for millions of Robinhood users. We operate as a cross-functional partner to growth, product, and data engineering—translating complex financial data into intelligent systems that make Robinhood smarter for every customer. We move fast, raise the bar, and care deeply about building things that matter. If you've ever wanted to solve personalization problems no one else has cracked—in one of the most data-rich, regulated industries on the planet—this is the team for you! As a Staff Machine Learning Engineer on the AI R&D team, you will own the design and delivery of sophisticated personalization and recommendation systems that directly shape what millions of users see and do on the Robinhood platform. You'll be a technical anchor on a growing, high-caliber team — collaborating with product, data engineering, and fellow ML engineers to take ambitious ideas from zero to one and into production at scale. You'll help define the team's technical direction, mentor engine

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is our evaluation platform — the unified evals backbone that lets teams measure, trace, and trust the quality of LLM and agent systems across the company, powering trace/score ingestion, LLM-as-judge workflows, agent simulations, and LLM observability for the tens of millions of daily requests flowing through our LLM Gateway. We also own core platform surfaces including the Agent Gateway, open-weights model serving and batch inference, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, with a primary focus on our evals and LLM observability platform: the systems that let teams evaluate, trace, and continuously improve the quality of LLM and agent products. You’ll work across evaluation frameworks and SDKs, OpenTelemetry-based trace/score ingestion, LLM-as-judge and offline/online eval pipelines, agent simulations, data pipelines, backend services, and observability. This role is ideal for an engineer who enjoys building reliable measurement and quality primitives in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and evaluation methodologies are evolving quickly. You’re excited about this opportunity because you will… Build the infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Work on our unified evals platform — evaluation SDKs, OpenTelemetry trace/score ingestion, LLM-as-judge, offline and online eval pipelines, and agent simulations — alongside the LLM Gatew

Engineering Manager, AI Conversation Platform Who we are About Stripe Stripe 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. About the team The newly formed Conversation Platform team aims to build a conversation platform for all merchants who use Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include customizing the Stripe landing page to suggest bespoke integrations, allowing users to command the Stripe API in natural language, and resolving user issues automatically. We are developing RAG based systems on the latest LLMs as well as fine-tuning our own models. We’re an end-to-end team going from ideas to models to shipping in production. What you’ll do Responsibilities Driving an ambitious vision for AI/ML that benefits our users Setting the technical & process direction for the team based on business goals Brainstorm and coordinate product integrations with partner teams Proposing new ideas and building prototypes Be an integral part of a larger ML community internally & externally Hire & develop a world-class team to deliver high-quality ML systems. Coach engineers to help them grow in their careers and maintain a high bar Who you are We are looking for ML Engineering Managers who are passionate about using ML to improve products and delight customers. You have experience leading teams that develop streaming feature pipelines, build ML models, and deploy them to production, even if it involves making substantial changes to backend code. You a

We are a global team of innovators and pioneers dedicated to shaping the future of observability. At New Relic, we build an intelligent platform that empowers companies to thrive in an AI-first world by giving them unparalleled insight into their complex systems. As we continue to expand our global footprint, we're looking for passionate people to join our mission. If you're ready to help the world's best companies optimize their digital applications, we invite you to explore a career with us! Your opportunity As the Manager, GTM Engineering (AI and Automation), you will be key to constructing and maintaining our "Revenue Velocity" engine with a specialized team of architects. In this high impact role, you will report to the Director of Enterprise Systems & Solutions. You will be responsible for the successful technical integration of advanced AI and automation tools throughout our Go-To-Market (GTM) technology stack. This role requires a proactive player-coach who balances thinking strategically about our revenue systems while remaining deeply involved in the creation of durable, data-driven automation frameworks. What you'll do GTM Tech Stack Management: Integrate with and optimize the core Sales and Marketing technology stack (Salesforce, Clay, Rox, Boomi, Salesloft) in alignment with customer-centric initiatives. Workflow & AI Automation: Execute end-to-end automated workflows (lead scoring, routing, alerts) and deploy AI/ML solutions to scale business outreach efficiency. Data Integrity & Analytics: Manage data pipelines and routine hygiene (deduplication, enrichment) across systems; maintain dashboards to track key GTM metrics and deliver forecasting to leadership. Process Execution & Enablement: Work with Program teams to map GTM workflows and drive technical projects from requirements gathering through implementation and user adoption. This role requires 2+ years of people management or relevant leadership experience, such as lead

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. At Okta, we’re building the next generation of authentication for the Agentic AI era. We’re looking for a Senior Engineer to join our AI Authentication team – a group of product-minded, deeply technical engineers delivering features to define what identity and access mean in a world increasingly powered by artificial intelligence. This is a great opportunity for an Senior engineer who thrives in collaborative environments, is eager to grow their technical depth, and wants to build scalable systems that solve real-world security problems. Auth0 Emerging Tech is the Engineering organization where we take care of the hottest technology out there: we ship fast, we don't break things. We are a dynamic and collaborative distributed and diverse team. We value ownership, learning and innovation. We launched our Auth for GenAI offering and we are looking for an Senior Engineer to join us to lead the team taking care of the Identity Protocols parts of the game. Auth0 works with NodeJS ( Javascript or Typescript), a hint of Go and MongoDB or PostgreSQL databases. What will you do: Bring expertise in identity and security while building innovative features and standards that will secure the Agentic AI world Build and maintain scalable services using TypeScript, NodeJS PostgreSQL/MongoDB. Collaborate with other product managers, designers, and senior engineers to deliver features that improve identity and access for GenA

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection. Agentic AI is at the center of how we scale that mission. We fine-tune and align open-source models, build AI-powered support agents, and develop end-to-end AI agents for safety case management, systems that reason over complex, high-stakes cases and drive them to resolution. SCC brings together ML, data, backend, and product engineers alongside data scientists and operations partners to transform these systems. As a Machine Learning Engineer on the SCC team, you will fine-tune and align models and build AI Agents that power how riders and drivers get help. Your work spans the full loop: post-training open-source models for our domain, composing them into multi-step agents, and building the evaluation that proves they are safe to ship in a customer-facing, safety-critical setting. Post-train and adapt open-source LLMs for SCC use cases using SFT, LoRA, and preference-tuning methods (RLHF, RLAIF, RLVR). Design and build AI-powered support agents and end-to-end agents for safety case management using LangGraph or equivalent agentic frameworks. Own the evaluation data flywheel, offline and online, that defines what "good" looks like and build benchmarks for the team to hill-climb. Turn interaction feedback into training data and learning signals, closing the data flywheel that continuously improves the models. Responsibilities: Conduct literature review and build post-training fra

Engineering Manager, AI Conversation Platform Who we are About Stripe Stripe 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. About the team The newly formed Conversation Platform team aims to build a conversation platform for all merchants who use Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include customizing the Stripe landing page to suggest bespoke integrations, allowing users to command the Stripe API in natural language, and resolving user issues automatically. We are developing RAG based systems on the latest LLMs as well as fine-tuning our own models. We’re an end-to-end team going from ideas to models to shipping in production. What you’ll do Responsibilities Driving an ambitious vision for AI/ML that benefits our users Setting the technical & process direction for the team based on business goals Brainstorm and coordinate product integrations with partner teams Proposing new ideas and building prototypes Be an integral part of a larger ML community internally & externally Hire & develop a world-class team to deliver high-quality ML systems. Coach engineers to help them grow in their careers and maintain a high bar Who you are We are looking for ML Engineering Managers who are passionate about using ML to improve products and delight customers. You have experience leading teams that develop streaming feature pipelines, build ML models, and deploy them to production, even if it involves making substantial changes to backend code. You a

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You’re excited about this opportunity because you will… Lead the design of infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Own and evolve our open-weights serving stack — real-time GPU endpoints, high-thr