Senior Industry Manager in Canada

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Reddit has a flexible workforce! If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence. Team Description Reddit is poised to rapidly innovate and grow like no other time in its history. We’re currently hiring across three distinct teams: Ad Safety & Verification Team The mission of the Ads Safety & Verification team is to protect the Reddit ads ecosystem so that users have a safe ads experience and legitimate advertisers trust Reddit. This team builds full stack, high scale products that span Reddit’s consumer-facing apps and ads systems. They own integrations with metrics verification and brand safety partners and operate services that scale to reliably handle tens of millions of requests per day. Ads Creative Management (ACM) Team The Ads Creative Management team focuses on the tools and systems advertisers use to create and manage their ad content. From images and videos to headlines and full post creatives, they enable efficient creative management and innovation across the campaign lifecycle. They work closely with AI teams to provide intelligent content suggestions and automation, helping advertisers launch high-quality campaigns faster and more effectively. Brand Innovation Team The Brand Innovation team drives the development of new ad formats that help advertisers maximize reach an

AI only answers correctly when it can trust the data underneath it. This role owns two connected parts of how Sigma shows up in that world: where Sigma's experience lives outside its own product (MCP, a CLI, the Claude and ChatGPT marketplaces, integrations like Slack, Teams, and Glean), and the semantic layer that makes every one of those surfaces trustworthy. The first mandate is Sigma's AI ecosystem: defining Sigma's approach to MCP, giving external agents structured, governed access to Sigma's data model; owning the CLI, giving developers a fast way to work with Sigma outside the UI; and building Sigma's presence in the Claude and ChatGPT marketplaces, plus integrations for Slack, Teams, Glean, and similar surfaces, so people can reach Sigma's data wherever they already work. This is some of the most visible, fastest-growing surface area in the product. The second mandate is the semantic layer underneath it all. Every agent, chat answer, and integration is only as reliable as the data model behind it — get a metric definition wrong here, and every surface built on top inherits the mistake. This includes setting the roadmap for how semantic views connect across data platforms and how the model evolves as new AI capabilities emerge. What you'll do Define Sigma's approach to MCP, giving external agents and tools structured, governed access to Sigma's data model. Own the CLI roadmap, giving developers a fast, scriptable way to work with Sigma outside the UI. Build Sigma's presence in the Claude and ChatGPT marketplaces, along with integrations for Slack, Teams, Glean, and similar surfaces, so people can reach Sigma's data wherever they're already working. Set the roadmap for Sigma's data modeling strategy, including how semantic views connect across data platforms and how the semantic layer evolves as new AI capabilities emerge. Partner with engineering and design to ship integration and semantic modeling capabilities that hold up at enterprise scale.

About the Role At Sigma, we’re not just adding AI—we’re building the future of how people work with data. Our platform already lets users explore billions of rows of data in seconds with a spreadsheet-like interface, analyze and present their data in workbooks, and build data apps and workflows. Now we’re pushing further, applying AI to reshape how people build in Sigma, discover insights, and make smarter decisions—fast. That’s where you come in. As an AI/ML Engineer, you’ll join a growing team focused on building the AI foundation that will power Sigma for the future. Your work will become an integral part of the workflow for the thousands of enterprises that run on Sigma. What You’ll Do Partner with product, design, and engineering teams to identify high-impact AI/ML opportunities Prototype and productionize AI systems that feel intuitive but do a lot under the hood—recommendations, natural language interfaces, agentic workflows, and more Develop and scale AI/ML infrastructure that powers both internal tooling and customer-facing features Tackle novel UX problems at the intersection of AI, BI, and apps What You Bring Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (required) 10+ years of experience building and deploying production-grade AI/ML systems Deep knowledge of machine learning, deep learning, and applied AI Experience across the full ML lifecycle: data curation, training, deployment, monitoring A track record of building things that ship—whether it’s recommendations, search, machine translation, or something equally complex Experience adapting or training foundation models (language or multimodal) for novel domains Bonus Points (or skills you’ll build here) You've built agents that can plan, reason, and use tools You know your way around cloud infrastructure (AWS, GCP, Azure) You’ve worked in a fast-moving startup or high-growth environment Additional Job details The base salary range for this posit

Sigma is transforming how businesses allow customers to build apps, agents and dashboards on top of governed enterprise data. Hence, we are growing the design team and looking for designers who are excited to solve challenging problems, deliver impactful capabilities throughout our stack to build world-class technology. You will be part of a talented team of designers with a shared mission to make data easily accessible for all users. We're looking for a Senior Product Designer / Design Engineer who sits at the intersection of interaction design and AI engineering: someone who uses AI to ship faster, builds the skills and evals that make AI more effective, and invents new interaction paradigms for how people work alongside intelligent systems. This isn't a traditional design role. Yes you'll be using Figma, but also writing code with AI, training it, evaluating it, and questioning every assumption about what a "UI" can be when the interface itself reasons. Please note this is a 4 day on-site role in our San Francisco office. What You'll Do Start with AI, stay with AI. Use LLMs to clarify scope, draft specs, surface edge cases, and align your team before committing to a direction, use AI coding tools to build and iterate on the solution itself, and merge code to prod when fits. Prototype in code. Build working interfaces with Cursor and Claude Code, guiding structure, behavior, interaction, motion and UX quality while AI handles implementation. Partner directly with engineering to decide what moves into the product and what stays as a validated spike. Bring it to production. Fix small interaction and refinement issues directly on prod code. Design new AI interaction paradigms for conversational interfaces. Invent and validate novel patterns for how users converse with, direct, and trust AI systems - especially in data contexts where precision and confidence matter. Write evals, skills, and help on tools. Build the scaffolding that makes AI reliabl

Overview We are seeking a hands-on Director of AI Software Engineering to lead and scale AI engineering efforts supporting multiple business units across Governance, Risk, and Compliance (GRC). This role sits at the intersection of product delivery, platform evolution, and applied AI—driving real-world impact across core workflows. This is not a pure management role. We are looking for a builder who leads from the front, someone who has recently written production code, shipped systems end-to-end, and can operate comfortably in ambiguity while aligning teams and stakeholders. What You’ll Do Lead AI Engineering Across GRC Own delivery of AI-powered capabilities embedded directly into business unit workflows (e.g., risk analysis, compliance automation, reporting, due diligence) Partner with product, data, and platform teams to translate business problems into scalable AI systems Stay Hands-On Contribute to architecture, code reviews, and critical path implementation Prototype and validate new approaches (LLMs, agents, retrieval systems, classification pipelines, etc.) Set engineering standards for performance, reliability, and cost efficiency Build and Scale Teams Lead and mentor a high-performing team of AI/ML and software engineers Drive hiring, coaching, and career development Establish a culture of ownership, speed, and technical excellence Drive Execution Deliver production-grade systems—not experiments Balance speed with rigor (security, privacy, compliance) Operate across multiple concurrent initiatives with clear prioritization Communicate and Influence Act as a bridge between engineering and business stakeholders Clearly articulate trade-offs, risks, and outcomes to senior leadership Align cross-functional teams around shared goals and timelines What We’re Looking For Proven Builder 10+ years in software engineering, with recent hands-on coding experience Demonstrated track record of shipping production systems at scale Experience with modern

Position Overview We are seeking a transformative Senior Director, Talent Acquisition to lead the overall recruiting function and deliver a high-impact, modern, and scalable hiring experience across the organization. In this role, the successful candidate will partner closely with senior business leaders and HR stakeholders to align hiring priorities, shape workforce plans, and support business growth through innovative, data-driven talent strategies. This leader will oversee end-to-end recruitment strategy and operations, guide team performance, and drive process improvements that strengthen hiring outcomes, scalability, speed, and quality of hire. A key focus of the role will be leading the successful implementation and optimization of Workday Recruiting and related processes, while advancing the use of AI-enabled recruiting tools, automation, talent intelligence, and analytics to improve sourcing effectiveness, decision quality, operational efficiency, and candidate experience. The ideal candidate is an AI-forward talent acquisition leader who brings strong leadership, stakeholder management, strategic thinking, and communication skills, along with deep expertise in recruiting operations, team development, talent analytics, recruiting technology, and enterprise process transformation. They will bring experience leading large-scale systems and process change, including Workday implementation or optimization, as well as a thoughtful approach to the responsible use of AI and technology across the hiring lifecycle. They will also bring a fresh and creative approach to attracting top talent and play a key role in advancing and strengthening our employer brand in the market. Key Responsibilities Lead the global Talent Acquisition function across a broad team or portfolio, with accountability for recruiting strategy, delivery, team capability, technology adoption, and performance against hiring goals. Partner closely with senior business leaders, HR

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

You love turning real business problems into working AI solutions — and you’re not afraid to roll up your sleeves to ship them. In this role, you’ll lead Diligent’s internal AI Solutions function , a small, high-impact team that is embedding AI and GenA I into the systems thousands of colleagues use every day across Marketing, Sales, Customer Success, Finance, HR, Legal, and Product & Engineering. You’ll set the AI vision and roadmap for internal tools, architect solutions, and still build hands-on — from prototypes and reference implementations through to production-grade integrations . You’ll own how AI shows up inside ERP, CRM, BI and core IT platforms, and you’ll be accountable for making those solutions reliable, secure, compliant, and measurably valuable for the business. If you enjoy being a “player-coach” who can move seamlessly between executive conversations and deep technical reviews, this role gives you the scope, visibility, and impact to shape how a global SaaS leader uses AI to run smarter and faster. Here’s a breakdown of what you’ll do (not all of it, just the important stuff) Lead, coach, and grow a high-performing team of AI Solutions Architects and Engineers, setting clear goals and building the capabilities the business needs as AI demand scales. Define and own the strategy, vision, and roadmap for internal AI solutions, translating business priorities into a focused portfolio of AI and platform initiatives. Design and deliver end-to-end AI/GenAI solutions — from ideation and prototyping through production deployment, monitoring, and continuous improvement. Embed AI capabilities (such as RAG, copilots, agents, summarization, and classification) into core business applications including ERP, CRM, BI, and other enterprise systems in a robust, maintainable way. &nbsp

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities. Role Overview As a Senior Staff Frontier Agents Engineer on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers. You'll work with enterprise clients to understand their unique challenges, architect custom AI solutions, and ensure successful deployment and adoption of AI systems in production environments. This is a hands-on technical role that combines deep engineering expertise with customer-facing problem solving. You'll work directly with customer engineering teams to integrate AI into their critical workflows. Key Responsibilities Customer Integration & Deployment Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs) Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows Deploy and configure AI models and agents within customer security and compliance boundaries AI Agent Development Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation Architect multi-agent systems that orchestrate between different models, tools, and data sources Implement evaluation frameworks to measure agent performance and iterate toward business objectives Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement Prompt Engineering & Optimization Create sophisticate

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations. About the ACE team The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. About This Role This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate t

The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes: Create multi-layered guardrails around agents Optimize data retrieval for agents Orchestrate fleets of asynchronous agents Automatically alerts users to deviations in data Illustrating how an agent reached a decision As a Senior Software Engineer, you will lead the development of a vertical feature or a horizontal capability to include defining requirements with stakeholders and implementation until it is accepted by the stakeholders. You will: Lead the design and implementation of scalable backend systems and distributed architectures for Federal customers. Manage the full lifecycle of feature development from requirement definition to deployment on classified networks. Direct the orchestration of asynchronous agent fleets to meet mission requirements. Lead customer engagements to translate mission needs into technical requirements. Own the communication with stakeholders to ensure implementation meets defined acceptance criteria. Conduct technical reviews and identify risks within machine learning infrastructure and model serving. Drive the platform roadmap by providing technical specifications for Federal product offerings. Ideally you will have: Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and contai

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Platform Engineering team. In this role, you will support the design and development of shared platforms used across Scale. This includes designing our foundational data platforms and lifecycle, architecting Scale’s core cloud infrastructure and orchestration stack, and redefining how engineers develop, build, test, and deploy software at Scale. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: Drive the design, and implementation of our foundational platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements. Collaborating with cross-functional teams to define, design, and deliver new features. Proactively identifying opportunities for, and driving improvements to, current p

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations. About Data Engine Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. Our Approach As part of the interview process, you’ll be considered for opportunities across several teams within the GenAI Engineering organization, based on your interests, expertise, and business needs. Potential team placements include Allocation, Growth, Frontier Data, Trust & Safety, Pay, Operator, or Tasking Experience. Together, these teams power Scale’s AI data operations - from building high-impact datasets that push the boundaries of LLM capabilities, to optimizing contributor onboarding and incentives, to safeguarding data integrity through advanced trust, safety, and security measures. They work at the intersection of ML, operations, and analytics to ensure we deliver the highest-quality data at scale. Responsibilities: Design, build, and maintain robust, scalable systems across the full stack, including front-end, back-end, and infrastructure layers Implement high-impact features using modern technologies such as TypeScript, React, Node.js, MongoDB, Elasticsearch, and Temporal Collaborate closely with internal operators (your use

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform providing APIs for knowledge retrieval, inference, evaluation, and more. We are seeking a strong Senior Full-Stack Engineer to help us build, scale, and refine our rapidly growing product. The ideal candidate is deeply grounded in software engineering best practices and experienced in developing and scaling modern web applications end-to-end. You will work across the stack—from React/TypeScript frontends to Python-based backends—while integrating with LLMs and machine learning systems. You will solve complex challenges in scalability, reliability, and product experience while owning significant product areas in a fast-paced environment. What You’ll Do Own major full-stack product areas , driving features from design through production deployment. Build modern frontend experiences using React and TypeScript, ensuring performance, usability, and responsiveness. Develop reliable backend services in Python, working with distributed systems, data pipelines, and ML/LLM components. Integrate with LLMs, vector databases, and AI infrastructure to power intelligent product experiences. Deliver experiments and new features quickly , maintaining high quality and tight feedback loops with customers. Collaborate across product, ML, and infrastructure teams to shape the direction of Scale GP. Adapt quickly —learning new technologies, frameworks, and tools as needed across the stack. Ideal Experience 5+ years of full-time engineering experience , post-graduation. Strong experience developing full-stack applications using React, TypeScript, and Python . Experience scaling or shipping products at high-growth startups . Familiarity with LLMs, vector databases, embeddings, or other modern AI tooling (tinkering or production experience welcome). Proficiency with SQL and modern API development. Experience with Kubernetes , containerization, and microservice architectures. Experience working with at leas

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities. About the General Agents Team The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications. The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers. About the Role As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you’ll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle—from model and system design to evaluation, deployment, and iteration—bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments. You will: Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases. Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints. Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings. Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs. Productionize frontier agent techniques (e.g.,