Engineering Manager Jobs

ROLE: SOFTWARE ENGINEER TEAM: MEDIA LOCATION: TORONTO (HYBRID) COMPANY OVERVIEW Salt is a North American marketing agency that creates connected experiences through creative, digital & media innovation. Our mission is to “Earn The World’s Attention” and Salt is built to find, develop, execute, and amplify the ideas that are worthy of our clients’ audiences. We’ve structured our agency to do what’s right for our clients – to connect different perspectives, to work across mediums, and to focus on delivering meaningful, effective results. We’re committed to living up to the values in our name. “Salt of The Earth” means we value collaborative, humble, hard-working people here. We’re looking for people who are as smart as they are kind because we believe the right talent and the right culture help us do what’s right for our clients. ROLE OVERVIEW Join us as a Software Engineer building the next generation of AI products. This hands-on engineering role combines Platform Engineering, Security, and Software Development to create the cloud platform that powers our applications at scale. You'll spend approximately 50% of your time building secure, automated, and reliable infrastructure, and the other 50% developing innovative product features - working with the latest cloud, AI, and developer technologies to deliver solutions used across the organization. You will build, secure, and operate modern cloud-based applications and AI platforms, developing new product features while helping ensure our infrastructure is secure, scalable, and reliable. Working closely with product and engineering, you will develop new product features while managing cloud infrastructure, CI/CD pipelines, application security, and production operations across our AI platforms. CORE RESPONSIBILITIES Infrastructure & Data Platform Engineering Maintain efficient cloud Infrastructure, and development environments. Develop i

About us Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation. Job Summary We are seeking an experienced Principal Hardware Diagnostics Engineer to design and develop diagnostics software used to monitor hardware health and diagnose system-level issues across Graphcore’s AI infrastructure platforms. This role focuses on building diagnostics agents, tools, and analytics frameworks that enable engineers and automation systems to identify, isolate, and resolve hardware issues across blade-level servers and rack-scale clusters. The Team Graphcore is a globally recognised leader in Artificial Intelligence computing systems. The company designs advanced semiconductors and data centre hardware that provide the specialised processing power needed to drive AI innovation, while delivering the efficiency required to support its broader adoption. The Systems Engineering and Platform Validation team ensures Graphcore’s AI compute platforms are reliable, diagnosable, and operationally robust at scale. The team co

We are looking for a disciplined and dynamic, Lead System Engineer – compute blade and rack Validation to join our growing compute rack validation team. As a diligent leader in Systems Engineering, you will drive multiple aspects of validation throughout the life cycle of the program. In this high visibility position, you will be part of a leading team to innovate and improve system bring-up and enablement abilities, as well as silicon and system validation to deliver the highest quality, industry leading technologies to market. Your technical leadership skills, validation and debug expertise will be necessary towards product development, definition, root cause and resolution. Your agility and collaborative approach will be essential to work within System Validation & other engineering teams (System Architects, SoC and Rack FW etc). The technical leader will be driving keys areas of system validation including leading first silicon & system bring-up (nodes and rack level systems) - rack level systems and blades will be based of ARM server architecture. Candidate will be immersed in challenging system enablement work, system validation (end-to-end) methodology, tests development and execution as well as triage/debug of critical issues to meet critical program milestones at POR quality. The candidate will also be a key contributor to state-of-the-art HW and lab capabilities for Grapchore’s system engineering. The candidate should be able to work in a global environment while maintaining a synergetic culture. Primary Responsibilities: Lead the systemenablement (including first silicon and other FW components) to ensure system capabilities are brought up as per plan of record and system architecture spec. Drive organization wide methodology for Firmware integration and best known configuration (HW/FW/SW) usage model by leading the release of deployment ready solutions. Develop key methodologies, lab HW and system SW capabilit

Manufacturing Test Engineer – Server Hardware Graphcore is a globally recognised leader in Artificial Intelligence computing systems. The company designs advanced semiconductors and data centre hardware that provide the specialised processing power needed to drive AI innovation, while delivering the efficiency required to support its broader adoption. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. We are opening a new AI Engineering Campus in Austin, which will play a central role in Graphcore's work building the future of AI computing. Role Overview We are seeking an experienced Manufacturing Test Engineer to support high-volume server manufacturing from board-level test through system-level production test. This role will work closely with an ODM manufacturing partner to define, implement, validate, and optimize the manufacturing test strategy for L6 board-level products , including ICT, MDA, and Board Functional Test , as well as support L10 system-level manufacturing test . The ideal candidate has strong experience in server hardware manufacturing, Linux-based test environments, diagnostic test coverage, fixture requirements, yield improvement, and root cause corrective action processes. This role requires both technical depth and hands-on manufacturing execution experience, with the ability to drive best practices across test development, factory readiness, quality planning, and ongoing production support. Key Responsibilities Manufacturing Test Strategy and Planning Work with ODM partners to define and execute the manufacturing test strategy for L6 board-level production . Develop and review test plans covering: In-Circuit Test, or ICT Manufacturing Defect Analyzer, or MDA Board Functional Test Diagnostic coverage requirements Manufacturing line test flow Fai

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 autonomous technologies in a service used by millions of people, then we want to talk to you! About the Role As an Industrial Designer on our team, you will own the design of physical products and help elevate our brand’s experience through form, function and manufacturability. You’ll be responsible for leading concept ideation, CAD modelling and prototyping, working across mechanical, electronics and user-experience domains. You’ll collaborate closely with engineering, manufacturing, sourcing, user-research and marketing, and participate in decision-making around product direction. This role offers ownership across the design lifecycle — from early sketches through production launch. You're excited about this opportunity because... Lead end-to-end design projects: develop concept sketches, CAD/3D models, renderings and physical prototypes. Conduct research into user needs, market trends, materials, manufacturing processes (injection moulding, thermoforming, additive manufacturing) and competitive products. Translate design intent into detailed specifications: materials, geometry, finishes, ergonomics, manufacturing constraints. Collaborate cross-functionally with engineering, manufacturing and product to ensure feasibility, cost-effectiveness and alignment with brand and product vision. Build and iterate prototypes (3D prints, machined models, mock-ups), validate usability, aesthetics, functionality and manufacturability. Present design concepts, flows and prototypes to stakeholders, incorporate feedback and drive decisions. Contribute to design language, visual identity and brand consistency across products. Why You’ll Love This Role You’ll shape real, tangible products that users will interact with and rely on. You’ll work in a small, high-impact team where your contributions

Overview: Guidepoint seeks an experienced Data/AI Engineer as an integral member of the Toronto-based AI team. The Toronto Technology Hub serves as the base of our Data/AI/ML team, dedicated to building a modern data infrastructure for advanced analytics and the development of responsible AI. This strategic investment is integral to Guidepoint’s vision for the future, aiming to develop cutting-edge Generative AI and analytical capabilities that will underpin Guidepoint’s Next-Gen research enablement platform and data products. This role demands exceptional leadership and technical prowess to drive the development of next-generation research enablement platforms and AI-driven data products. You will develop and scale Generative AI-powered systems, including large language model (LLM) applications and research agents, while ensuring the integration of responsible AI and best-in-class MLOps. The Senior AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other state-of-the-art tools across all of Guidepoint’s products. Guidepoint’s Technology team thrives on problem-solving and creating happier users. As Guidepoint works to achieve its mission of making individuals, businesses, and the world smarter through personalized knowledge-sharing solutions, the engineering team is taking on challenges to improve our internal application architecture and create new AI-enabled products to optimize the seamless delivery of our services. This is a hybrid position based in Toronto. What You'll Do: Architect and Build Production Systems: Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems. Own the AI Application Lifecycle: Own the end-to-end lifecycle of AI-powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization

Overview: Guidepoint seeks an experienced AI Engineer as an integral member of the Toronto-based AI team. The Toronto Technology Hub serves as the base of our Data/AI/ML team, dedicated to building a modern data infrastructure for advanced analytics and the development of responsible AI. This strategic investment is integral to Guidepoint’s vision for the future, aiming to develop cutting-edge Generative AI and analytical capabilities that will underpin Guidepoint’s Next-Gen research enablement platform and data products. This role demands exceptional leadership and technical prowess to drive the development of next-generation research enablement platforms and AI-driven data products. You will develop and scale Generative AI-powered systems, including large language model (LLM) applications and research agents, while ensuring the integration of responsible AI and best-in-class MLOps. The AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other state-of-the-art tools across all of Guidepoint’s products. Guidepoint’s Technology team thrives on problem-solving and creating happier users. As Guidepoint works to achieve its mission of making individuals, businesses, and the world smarter through personalized knowledge-sharing solutions, the engineering team is taking on challenges to improve our internal application architecture and create new AI-enabled products to optimize the seamless delivery of our services. This is a hybrid position based in Toronto. What You'll Do: Architect and Build Production Systems: Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems. Own the AI Application Lifecycle: Own the end-to-end lifecycle of AI-powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization in production

SUMMARY STATEMENT We are looking for a Solution Architect to design the technical solutions behind our client engagements and give delivery teams a clear, workable path from concept to production. You will work across enterprise data, software applications and GenAI - translating complex business problems into practical architectures that delivery teams can build and scale. This could include architecting an agentic workflow for clinical operations, a conversational analytics product grounded in enterprise data, or an AI-enabled decision platform for commercial teams. You will work directly with clients, define the architecture, test the most important technical decisions yourself and establish the foundations for successful delivery. This is an architecture-first role with meaningful hands-on engineering: you will stay close enough to implementation to prove the architecture works and support it through production delivery, without becoming the primary engineer for every component. You will also help shape the reusable patterns, technical standards and accelerators behind Lynx’s growing AI-native life sciences practice. KEY RESPONSIBILITIES Solution Architecture Own the end-to-end solution architecture for client engagements, including data models, system design, integration patterns and technology choices. Translate business requirements into clear technical designs and implementation paths that delivery teams can build from. Design solutions spanning enterprise data, APIs, applications, cloud platforms and GenAI capabilities. Lead technical discovery with clients: understand requirements, assess existing systems and identify dependencies, constraints and delivery risks. Present architectural options and trade-offs clearly to technical teams, business stakeholders and senior leaders. Make pragmatic decisions across build speed, cost, scalability, security and maintainability. Review key implementation decisions and remain

About the Role OpenAI’s Industrial Compute organization is responsible for ensuring our compute infrastructure scales efficiently to support millions of users and increasingly sophisticated AI models. We’re looking for a Data Scientist to partner closely with Capacity Systems Engineering, Infrastructure, Product, and Research to optimize inference capacity across our global GPU fleet. This role combines statistical modeling, large-scale data analysis, forecasting, and systems thinking to drive critical decisions around infrastructure investments, performance-efficiency trade-offs, and customer experience. You’ll transform complex operational data into actionable insights that directly influence how OpenAI allocates and scales one of the world’s largest AI compute environments. Key Responsibilities Build statistical and machine learning models to profile and improve GPU utilization, latency, throughput, and overall fleet efficiency. Develop forecasting models for inference demand across products, regions, and model families. Analyze production workloads to identify latency bottlenecks and capacity constraints, highlighting optimization opportunities. Partner with Capacity Systems Engineering to inform infrastructure planning and long-term GPU investment strategies. Design experiments and simulations to evaluate scheduling policies, serving strategies, and infrastructure tradeoffs. Build dashboards and operational metrics that enable leadership to make data-driven capacity decisions. Collaborate with Product, Research, Finance, and Infrastructure teams to align compute planning with business growth and model roadmaps. Communicate technical findings clearly to both engineering teams and executive leadership. Qualifications MS or PhD in Statistics, Computer Science, Operations Research, Applied Mathematics, Economics, or related quantitative discipline (or equivalent industry experience). 5+ years of experience working in the infrastructure data science space. Strong ex

About the Team The GTM Enablement team helps OpenAI’s customer-facing organizations turn rapidly evolving AI capabilities into consistent, high-quality customer outcomes. We build the onboarding, learning experiences, playbooks, and knowledge systems that help teams develop technical depth, stay current, and confidently guide customers through successful AI adoption. About the Role We’re hiring a Field Enablement Lead, Technical Success to design and scale enablement for our rapidly growing technical customer-facing teams. You will own programs spanning onboarding, continuous skill development, technical pitches and demos, and subject-matter-expert knowledge sharing. Working closely with Technical Success leaders, Product Enablement, and technical SMEs, you will turn complex product knowledge and field experience into practical systems that improve readiness, consistency, and the quality of customer interactions and deployments. In this role, you will: Design, implement, and scale comprehensive enablement programs aligned to Technical Success onboarding, role-based skill development, and ongoing readiness needs. Redefine and operate the Subject Matter Expert (SME) program, creating clear pathways for technical experts to share knowledge and raise technical depth and consistency across GTM. Own and proactively maintain a versioned repository of technical pitches, demos, playbooks, and launch-ready assets. Partner with Technical Success leadership, Product Enablement, and product SMEs to identify skill gaps and deliver targeted learning interventions. Capture, vet, organize, and make field- and SME-generated technical content easy to discover, trust, and reuse. You might thrive in this role if you have: 5+ years of experience in technical enablement, solutions engineering, solutions architecture, technical success, or a related role. A proven track record of designing and scaling technical enablement programs in high-growth SaaS or technology environments. Exceptional

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We’re looking for a product manufacturing & quality engineer, who will be responsible for driving technical initiatives related to the manufacturing, quality and reliability of our AI supercomputer hardware systems to ensure product success from concept to launch and through mass production. You’ll have the opportunity to coordinate with functional SMEs and work with a wide range of stakeholders, from design engineering and operations teams, TPMs, external industry vendors and partners to ensure that all products are developed and delivered on time and to the highest quality standards. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees In this role, you will: Own the integrated manufacturing and quality readiness for a product across L6, L10, and L11, with clear gates, milestones, deliverables, owners, and closure criteria. Lead readiness of process flows, tooling, fixtures, assembly operations, test interfaces, and production controls. Review and contribute to work instructions. Translate product requirements into qualification plans, process controls, test requirements and acceptance criteria with design engineering and Area SMEs Coordinate and drive execution of product and process qualification, reliability testing, and validation with the relevant SMEs. Maintain traceable evidence that assigned products and processes meet agreed performance, reliability,

About the Team The Safety Training research team aims to fundamentally advance our capabilities for precisely implementing safe behavior in AI models, and to leverage these advances to make OpenAI’s deployed models safe and beneficial. This requires a breadth of new ML research to address the growing set of safety challenges as AI becomes more powerful and used in more settings. Key focus areas include how to train nuanced safety behaviors, how to make the model robust to bad actors, how to address privacy and security risks, and how to make the model trustworthy in safety-critical situations. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. About the Role We’re seeking a researcher to train and evaluate models for U.S. government use, with a focus on national security applications. You’ll advance safety post-training and robustness, helping models follow nuanced policies while preserving their usefulness and capabilities. In this role, you will: Research and implement methods for safety training, reinforcement learning, and adversarial robustness. Develop evaluations, identify model failure modes, and use findings to improve training. Work with research, engineering, security, and policy partners to support safe, reliable deployment. You might thrive in this role if you: Bring 4+ years of relevant AI safety research experience, including RLHF, adversarial training, or robustness. Have a degree in computer science, machine learning, or a related field, and strong deep learning research or engineering skills. Have experience improving model safety for deployment and enjoy collaborative research. Are motivated by OpenAI’s mission and the responsible use of AI in safety-critical settings. Security Requirements Active TS/SCI clearance or equivalent. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefi

About the Team OpenAI develops models that can reason through complex problems and hardware designed for the demands of advanced AI. AI for Chips connects these efforts: applying increasingly capable AI systems to the work of semiconductor engineering. Our goal is to help engineers develop better chips and shorten design cycles. This work brings research, model training, and hardware expertise together to build tools that engineers can use on real designs, with correctness and measurable performance at the center. About the Role We’re hiring a Research Engineer to help OpenAI models solve chip-design problems through reinforcement learning, tool use, and evaluation. You’ll own experiments from the initial idea through implementation and analysis. That means building environments and evaluations, running training, investigating failures, and using the results to decide what to try next. You’ll also build the software needed to make those experiments reliable and reproducible. We value strong coding fundamentals, careful experimental judgment, and the ability to make progress independently. Prior chip-design experience is helpful, but you can learn the domain alongside the team’s hardware specialists. In this role, you will: Build RL environments and evaluations for tasks such as RTL generation, design verification, and physical design optimization. Develop and test approaches that help models use chip-design tools and improve power, performance, and area while preserving correctness. Design experiments, establish baselines, and measure whether improvements hold up on new tasks and designs. Investigate failures across model behavior, rewards, evaluation tools, and experiment infrastructure. Improve iteration speed through better tooling, faster evaluations, and proxy rewards that reflect the outcomes we care about. Turn successful experiments into reusable research code and training workflows, working closely with researchers and engineers. You might thrive in this ro

About the Team The Growth Platforms team builds the systems and operating foundations that help OpenAI grow responsibly. We partner across the product portfolio to connect customer signals, identity and consent, campaign workflows, measurement, and product experiences into an AI-enabled growth engine. Our work helps teams launch, learn, and scale with a high bar for data quality, privacy, reliability, and customer trust. About the Role We’re looking for an experienced marketing technology and operations leader to drive cross-functional work at the intersection of growth, measurement, data, and automation. Your mission will be to turn fragmented tools, signals, and workflows into reliable, measurable, AI-enabled capabilities that teams can use safely at scale. You’ll work across Growth, Marketing Operations, Product, Engineering, Data Engineering, Data Science, Security, Privacy, Legal, and Revenue Operations, as well as external advertising platforms, measurement providers, and implementation partners. You’ll translate business requirements and privacy constraints into data contracts, integration designs, rollout plans, and reliable first-party data systems. This is a hands-on, high-impact role for someone who brings structure to ambiguity and moves from event schemas, APIs, and data quality assurance to operating cadences, partner enablement, and executive updates. This role is based in San Francisco or New York City with a hybrid office expectation. In this role, you will: Own the operating model for Growth’s marketing technology stack across identity, consent, audiences, activation, measurement, and experimentation. Own and operate the complete paid-media tracking and measurement system, including website pixels, server-to-server conversion events, mobile measurement integrations, identity and consent controls, attribution methods, and timely signal delivery to advertising platforms. Design and implement event schemas, data mappings, APIs, and integrations; valid

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. We are looking for experienced backend software engineers to join our claims tech engineering team. Our vision is to tangibly reduce risk on the Lyft platform, and by extension reduce insurance cost. Our team is dedicated to centralizing the entire claims operation onto a unified risk platform. This consolidation of data, workflows, and communications aims to foster proactive measures, enhance efficiency, ensure consistency, and provide valuable insights. These efforts are designed to effectively reduce claims costs as Lyft's operations expand. Additionally, our team is responsible for maintaining robust relationships with our third-party insurance partners, guaranteeing timely, proactive, and precise sharing of claim data. Responsibilities: Write well-crafted, well-tested, readable, maintainable code Own feature from product spec to successful high quality development, deployment and maintenance Participate in code reviews to ensure code quality and distribute knowledge Respond to external questions and requests. Unblock, support and communicate with stakeholders to achieve results Experience: 3+ years of relevant professional experience Experience with object-oriented programming Experience in distributed systems Experience working with databases, relational or NoSQL Write clear, scalable and clear design documentation Design, build and improve a set of team owned components Benefits: Extended health and dental coverage options, along with life insurance and disability benefits Mental health benefits Family building benefits Child care and pet benefits Access to a Lyft funded Health Care Savings Account RRSP plan with company match to help save for your future In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows