Model Behavior Engineer Manager Manager Manager Manager in Canada

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. From the early days, simulated physics has been the foundation of the way users build in Roblox. Our physics engine enables dynamic environments with emergent behavior making games more immersive and exciting. The Physics team develops Roblox's proprietary distributed physics engine. Our team implements the mathematical models behind our dynamic simulation engine, and ships engine APIs and tools that developers use every day. These models incorporate rigid body dynamics arising from external forces, collisions, and constraints while optimizing for real-time performance. We work on integrating the low level physics solver into the rest of the engine for online gameplay across multiple platforms. You will report to the Physics Environment Engineering Manager. You Will: Innovate and implement powerful physics features that empower Roblox developers to create immersive and dynamic experiences. Drive innovation through research and rapid prototyping , translating new ideas into practical solutions that stand the test of time. Optimize and enhance the robustness and performance of the physics engine and its integrations, ensuring a seamless user experience. Collaborate with internal teams and ext

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. From the early days, simulated physics has been the foundation of the way users build in Roblox. Our physics engine enables dynamic environments with emergent behavior making games more immersive and exciting. The Physics team develops Roblox's proprietary distributed physics engine. Our team implements the mathematical models behind our dynamic simulation engine, and ships engine APIs and tools that developers use every day. These models incorporate rigid body dynamics arising from external forces, collisions, and constraints while optimizing for real-time performance. We work on integrating the low level physics solver into the rest of the engine for online gameplay across multiple platforms. You will report to the Physics Environment Engineering Manager. You Will: Innovate and implement powerful physics features that empower Roblox developers to create immersive and dynamic experiences. Drive innovation through research and rapid prototyping , translating new ideas into practical solutions that stand the test of time. Optimize and enhance the robustness and performance of the physics engine and its integrations, ensuring a seamless user experience. Collaborate with internal teams and ext

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. As a Data Engineer on the SCC team, you will have ownership over the data modeling and pipelines that power SCC’s Associate and AI Agent Platform . Your efforts will be critical to the reliability of our pipelines, execution of third party data integrations, accurate reporting of agents performance, and efficiency improvements that can save millions of dollars / year. You will work cross-functionally to bridge Lyft's business goals with data engineering. Your efforts will allow access to business and user behavior insights, using huge amounts of Lyft data to fuel several teams such as Analytics, Data Science, Engineering, and many others. Responsibilities: Owner of the core data pipeline, responsible for scaling up data processing flow to meet the rapid data growth at Lyft Evolve data model and data schema based on business and engineering needs Implement systems tracking data quality and consistency Develop tools supporting self-service data pipeline management (ETL) SQL and MapReduce job tuning to improve data processing performance Write well-crafted, well-tested, readable, maintainable code Participate in code reviews to ensure code quality and distribute knowledge Collaborate cross-functionally with product, engineering, data science, and marketing teams to understand business problems and align on prioritization and solutions Experience: Bachelor's degree in Compute

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. Machine Learning is at the heart of Lyft’s products and decision-making. Machine Learning Engineers at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges, from pricing and marketplace frameworks that ensure reliability and competitiveness, to agentic AI platforms that automate analytical workflows, to behavioral detection systems that protect the integrity of our network. We operate at the intersection of applied ML and real business impact, shipping models that directly influence revenue, rider experience, and partner trust. Lyft Business builds products that help organizations move the people who matter most—employees, customers, patients, and guests—easily and efficiently. Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated tools. These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and community programs. We're looking for a Machine Learning Engineer to design, build, and deploy ML systems across Lyft Business. This is a high-scope role: you won't be siloed into one problem area. Instead, you'll move across pricing algorithms, fraud and behavior detection, agentic AI systems, and emerging ML applications as the business evolves. You'll write production-quality code, own models end-to-end from prototyping through deployment, and collaborate closely with Data Scientists, Product Managers, and Software Engineers to translate complex business problems into scalable ML solutions. This role is ideal for someone who is technically versatile, energized by variety, and wants to see th

Scale works with the industry's leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling) and evaluation. This role is on the evaluation pod within the GenAI Research Organization and will focus on building benchmarks and diagnosing model failure modes in both text and multimodal modalities. In this role, you will develop rigorous evaluations and diagnostic methods that reveal where frontier models fail and why. You will collaborate with researchers and engineers to define best practices in evaluation-driven AI development. You will also partner with top foundation model labs to translate failure analysis into technical and strategic input on the next generation of generative AI models. You will: Analyze model behavior to identify, characterize, and diagnose failure modes in frontier LLMs and Agents. You’ll identify everything from capability gaps and reasoning errors to robustness and alignment issues, all focusing on RCA. Design and build benchmarks and evaluation methods that measure LLM capabilities in both text and multimodal modalities. Apply post-training expertise (SFT, RLHF, reward modeling) to connect observed failures to the data and training interventions that address them. Publish research findings in top-tier AI conferences. Ideally you’d have: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field. Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning. Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning, and with LLM evaluation or benchmark development. Excellent written and verbal communication skills. Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals. Previous experience in a customer facing r

Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities. In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models. You will: Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities. Design and experiment new approaches to preference optimization. Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness. Publish research findings in top-tier AI conferences. Ideally you’d have: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field. Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning. Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning. Excellent written and verbal communication skills Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals Previous experience in a customer facing role. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined du

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. Join Tenstorrent and help bring next-generation AI accelerator technology from silicon bring-up to production. You’ll work at the forefront of hardware innovation, diagnosing complex issues across chips, systems, firmware, and software while collaborating with some of the brightest engineers in the industry. This role offers the opportunity to solve challenging technical problems, build impactful debug solutions, and directly influence the reliability and performance of cutting-edge AI compute platforms. This role is hybrid, based out of Toronto, Canada. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are A hands-on hardware debug engineer who thrives on solving complex, cross-functional problems at the intersection of silicon, firmware, and software. A curious and analytical problem solver who enjoys digging into failures, identifying root causes, and driving issues from initial discovery through resolution. An engineer with strong post-silicon validation and bring-up experience who is comfortable working in the lab and getting deep into system-level behavior. Someone who enjoys building tools, improving debug methodologies, and creating

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? This role will focus on evaluating coding tasks, requiring you to review and debug code, navigate repository architecture, and analyze model trajectories. Your work will contribute to our model development efforts and the logic our models apply when completing task requests. Please note : This is a part-time independent contractor position available within Canada. We seek candidates who are able to commit to 16 hours per week minimum at a 40 CAD/hour contract rate. This role is BYOD 💻 - Bring Your Own Device (laptop). Remote work within Canada. 12 month contract. Performance incentives included! As a Data Annotation Specialist, you will: Evaluate the model's ability to respond to coding requests, workflows, and code base-related questions using available tools. Assess agent trajectories and model capabilities for code generation and debugging requests. Prompt models to complete complex coding tasks and review the accuracy of generated responses. Label, proofread, and improve machine-written and human-written software engineering-related outputs. Report quality and performance trends related to model/agent behavio

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. The End User Protection Team Auth0 is an easy-to-implement authentication and authorization platform designed by developers for developers. We ensure access to applications is safe, secure, and seamless for the more than 100 million daily logins worldwide. Our modern approach to identity enables this Tier-0 global service to deliver convenience, privacy, and security, allowing customers to focus on innovation. The End User Protection team is responsible for building and maintaining Auth0’s Attack Protection capabilities. Cyberattacks, such as credential stuffing and password spraying, often target end users to gain unauthorised access to applications. These attacks pose a significant threat, compromising user accounts and data. The Attack Protection features mitigate these risks by continuously monitoring user login behaviour and automatically detecting and blocking malicious activity. They protect users without adding unnecessary friction. The team works closely with our Machine Learning and AI teams to develop and deploy cutting-edge detection models. This collaboration allows application builders to provide a secure login experience for their users while isolating them from the complexities and ever-evolving tactics of these attacks. The Software Engineer II Opportunity We are looking for a Software Engineer II to join our End User Protection team. What you’ll be doing Be a part of a fast-paced, agile team. Design and buil

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Job Summary We are seeking a motivated engineer to join the DRAM Systems Engineering team, focusing on the development, evaluation, and optimization of next-generation memory systems for AI accelerators. This role emphasizes research and development across hardware architecture, operating systems, and performance analysis to support Agentic AI inference workloads. If you are ambitious and eager to make an impact in the exciting world of AI and memory systems, this is the perfect opportunity for you! Responsibilities Characterize AI inference workloads and examine memory behavior Build and evaluate tiered memory hierarchies for AI accelerators Study KV cache lifecycles, MoE models, and data placement strategies Compare and optimize explicit versus hardware-assisted data movement Develop, test, debug, and detail system-level and OS components Prototype and evaluate agentic AI systems by building agents and multi-agent workflows using modern frameworks and orchestration patterns (planning, tool use, memory, and context management). Apply these technologies both as workloads under study and as accelerators for internal engineering workflows <h2 style="color:!importan

Scale Labs, Research Scientist — AI Controls and Monitoring As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist focused on AI Controls and Monitoring, you will design methods, systems, and experiments to ensure that advanced AI models and agents remain aligned with intended goals, even in high-stakes or adversarial environments. For example, you might: Develop monitoring techniques and observability methods that track AI behavior in real time to identify and flag deviations, emergent capabilities, or anomalous outputs; Research mechanisms for layered control, including fail-safes, oversight protocols, and intervention methods that can halt or redirect AI systems when risks are detected; Design red-team simulations to probe weaknesses in oversight and control mechanisms, and build mitigations to close identified gaps; Collaborate with policymakers, engineers, and other researchers to establish standards and benchmarks for AI monitoring and escalation. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Practical experience conducting technical research collaboratively. You should be

Scale Labs, Research Scientist — Safety Post Training As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist working on Safety Post-Training you will develop and apply post-training methods and interpretability techniques to make frontier AI systems safer, and better understood by researchers and policymakers.. For example, you might: Design and run post-training pipelines to study how training choices affect model safety, robustness, and alignment properties; Develop interpretability-informed evaluations that reveal how and why models produce unsafe, deceptive, or otherwise undesirable behaviors, and use those insights to guide targeted mitigations; Collaborate with policymakers, engineers, and other researchers to translate post-training and interpretability findings into actionable safety standards, evaluation benchmarks, and best practices. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches. A track record of published research in machine learning, particularly in generati

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. The Opportunity Okta is building an AI-enabled demand engine that uses signals, automation, and intelligent engagement to identify, qualify, route, and progress the right buyers more effectively. The AI Journey Operations Manager will own the business strategy, qualification framework, routing logic, operating model, and performance optimization for AI-assisted xDR capabilities across the Global Demand Center. This role is not primarily a platform administration or workflow-build role. It is a senior, cross-functional capability-owner role responsible for determining how AI-assisted qualification and engagement should operate, how it connects to human xDR and Sales follow-up, and how it contributes to measurable funnel and pipeline outcomes. The ideal candidate combines AI-enabled GTM experience, xDR/Sales process knowledge, demand generation, revenue operations, and journey strategy. They can translate business priorities into qualification logic, routing rules, AI engagement workflows, handoff requirements, measurement frameworks, and ongoing optimization plans. What You’ll Do Own AI xDR qualification strategy Define qualification frameworks for AI-assisted engagement across priority inbound, account-based, product-led, customer, and signal-triggered motions. Establish the fit, behavior, intent, engagement, and business-context criteria used to determine whether a lead or account should be engaged, progressed, routed, recycled, or suppressed. Partn

Location - Hybrid This is a hybrid role based out of our San Francisco, Corporate Headquarter office, 3 days in the office, 2 days work from home. About Us Sauce Labs is the world’s largest full-lifecycle, test automation platform, and the company behind Selenium. Trusted by 80% of the world’s top ten largest financial institutions and over 300,000 enterprise users, Sauce Labs provides the only AI platform capable of turning business intent into autonomous testing and quality assurance. With a proprietary dataset of 8.7 billion test runs, Sauce Labs empowers the Fortune 2000 to bridge the gap between AI-driven code generation and enterprise-grade software quality. Learn more at http://saucelabs.com . Release Assurance at the Speed of AI | Meet the new Sauce Labs The Role The Senior GTM Strategy & Planning Analyst will play a key role in supporting go-to-market (GTM) strategy through sales forecasting, churn forecasting, revenue analytics, and capacity planning across the GTM teams. This role will partner closely with Sales, Marketing, Customer Success, and Finance to align GTM initiatives with company growth objectives and contribute to executive-level insights for board presentations. Responsibilities GTM Board Materials: Support preparation of sales forecasting, churn forecasting, and revenue insight materials for executive and board review, ensuring data accuracy and alignment with company strategy. Executive Reporting: Help develop executive-level reporting materials, including presentations that translate complex data into clear, actionable insights. Forecasting: Build and maintain sales forecasting models in partnership with Sales Leadership, modeling pipeline generation, deal velocity, close rates, and other key metrics. Build predictive models to forecast customer churn, leveraging historical data and customer behavior insights to inform retention and renewal strategies. GTM Capacity Planning: Build and maintain capacity models to help ensure GTM t

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.