Python Developer in Toronto

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

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. Tenstorrent is building next-generation CPU and AI silicon. 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 Experienced in hardware debug and post-silicon bring-up for CPU, SoC, or ASIC systems. Strong understanding of processor architecture and microarchitecture (RISC-V, x86, or ARM) with familiarity in debug and trace methodologies (e.g., iJTAG). Hands-on engineer who excels at diagnosing complex hardware, firmware, and software issues through root-cause analysis. Comfortable working in the lab with a passion for building debug tools, automation, and scalable methodologies. Collaborative team player with experience partnering across ASIC, firmware, software, and validation teams. What We Need

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

About the Role: As a Staff Software Engineer on the ML Infrastructure team, you will collaborate closely with the Machine Learning and Product teams to build world-class machine learning inference platforms. These platforms power essential services like personalized recommendations, search, and content understanding across Tubi. A core responsibility of this team is developing and maintaining low-latency ML model serving systems that support Deep Learning, LLM, and Search models. This involves building self-service infrastructure and critical components such as the inference engine, feature store, vector store, and experimentation engine. You will improve the way we deploy and operate our services and even contribute to open-source projects. This role grants the architectural freedom to explore new frameworks, lead critical cross-functional projects, and transform the capabilities of our ML and Product teams. Responsibilities: Design and build scalable, high throughput, and low latency distributed systems using Scala Build reusable components and services that serve various ML applications like Personalization, Search, Ads and Exploration Partner closely with ML engineers to understand their challenges and limitations and develop scalable solutions to address them. Proactively recommend solutions to keep our ML Inference stack state of the art. Take a data driven approach to identifying & optimizing latency, cost, and efficiency of our infra. Lead large scale cross functional refactorings if necessary Mentor other engineers on the team on system design, effective incident management, interviewing, leveraging LLMs for work, etc. Collaborate with ML, Product, and cross functional engineering teams to define the long term vision and architecture for ML Infrastructure at Tubi. Your Background: Experience designing and building scalable, distributed systems in any modern backend language (e.g., Scala, Java, Python, Go, C++); experience with Scala or JVM b

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

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. Our Infrastructure team is passionate about building software to solve problems at massive scale. We do this often, and when we believe our solution is worth sharing with the community, such as Envoy Proxy , we open source our ideas for the benefit of others. As an Observability team member, you are responsible for the operation and maintenance of our logging and metrics infrastructure. You ensure all teams at Lyft are aware of the operational health of their products by monitoring system availability and take a holistic view of our platform performance. You build software and platforms to automate infrastructure platform operations and management. By measuring and monitoring our operations you find opportunities to improve our systems in order to push our platform forward. You provide our partners with the support they need to help them build robust large scale distributed systems. We count on the reliability of our infrastructure to empower Lyft teams to provide our customers rich experiences that are highly available with rock solid performance to ensure our transportation platform continues to connect people and places. As we grow our team, we are seeking experienced Infrastructure Engineer to ensure that as our Infrastructure continues to scale, our platform continues to provide an essential and dependable service that transports millions of people every day. Specifically we are searching for someone who brings fresh perspectives, enjoys collaborating with cross-functional teams in order to continually improve our products and services for our customers. Responsibilities: Maintain, improve, and develop tooling and systems that enhance the reliability, scalability, and efficiency of our platform. Assist engineering teams in defining service-level objectives (SLOs) and provide the necessary toolin

About Forma.ai: Forma.ai is a Series B startup that's revolutionizing how sales compensation is designed, managed and optimized. We handle billions in annual managed commissions for market leaders like Edmentum, Stryker, and Autodesk. Our growth has been fuelled by our passion for fundamentally changing and shaping how companies use sales intelligence to drive business strategy. We’re welcoming equally driven individuals who are excited about creating something big! The Opportunity As a Staff Security Engineer, you will be a hands-on technical leader strengthening security across Forma's application, cloud infrastructure, development lifecycle, internal systems, and incident-response practices. Security today is shared across Engineering and DevOps. You'll work closely with both teams and have real room to shape how Forma approaches security as we grow. Depending on your interests and the needs of the business, the role could develop into a deeper individual-contributor position or help build a dedicated security team. You'll work directly with Engineering, DevOps, IT, Product, Legal, and Privacy to identify risks, design practical controls, automate security processes, and help teams ship secure and reliable software. What you'll do Cloud and infrastructure security Design and implement security controls across Forma's AWS environments, with a focus on IAM, least-privilege access, service identities, and account boundaries. Embed security requirements into Terraform and other Infrastructure as Code, and improve secrets, certificate, encryption-key, and credential management. Build automated checks for insecure configurations, excessive permissions, exposed resources, and configuration drift across Kubernetes, containers, serverless workloads, networking, and data services. Application, data, and AI security Run threat modelling and security architecture reviews for new products, services, APIs, data pipelines, and third-party integrations

Overview: Qsight is a high-growth division of Guidepoint focused on building data intelligence solutions for the healthcare sector. Qsight leverages proprietary datasets and rigorous analysis of alternative data sources to generate actionable insights for top-tier institutional investors, medical device manufacturers, and pharmaceutical companies. The Qsight team develops market intelligence products designed to be highly relevant, accurate, and scalable – delivering superior insights to a diverse, global client base. We are seeking an experienced, motivated Tehnical Operations Engineer to join our growing team. This is a multiple-hats role focused on SaaS/platform operations and tier-2 support for client-facing systems. You will own the administration and reliability of key tools, troubleshoot and resolve escalations with clear documentation, and build lightweight automation and reporting to reduce manual work as we scale. You will partner closely with Customer Success, Product, and Engineering to proactively monitor, support, and improve critical systems. Through practical, creative problem-solving, you will strengthen reliability, accelerate time to resolution, and increase operational visibility. Day to day, you will triage and resolve client technical questions, manage vendor license administration and renewals, and produce reporting that informs operational decisions. This role is a launchpad toward an SRE/Platform Engineering track as you grow into deeper automation, reliability engineering, and systems design work. This is a hybrid position based out of our Toronto office. What You’ll Do: Platform Support Own routine ops and configuration changes for critical SaaS platforms – Including Tableau, Freshdesk, Datadog, and our own client facing and internal portals Configure and maintain Freshdesk portals, routing, SLAs, permissions, integrations, etc. based on business requirements. Automate manual operations with Python, PowerAutomate, and shell scri

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 Trust & Safety function at Lyft is dedicated to keeping every Lyft ride safe for both riders and drivers. We build and maintain the tools, features, and systems that deter bad actors, protect vulnerable populations, and reinforce trust across the platform. As an Backend Engineer working on Trust & Safety, you'll work at the intersection of product, design, engineering, and policy to ship high-impact safety features to millions of users. Responsibilities: Drive high-impact projects and innovate new solutions to provide the best user experience. Work closely with cross-functional teams and partner teams to develop solutions based on technology and business needs, and advance team’s goals and priorities Independently lead features from idea to positive execution and launch Unblock, support and communicate with internal partners to achieve results Write well-crafted, well-tested, readable, maintainable code Utilize your expertise in Python, Golang, AWS to deliver robust and scalable solutions Participate in code reviews to ensure code quality and distribute knowledge, as well as on call rotations Share your knowledge by giving brown bags, tech talks, and promoting appropriate tech and engineering best practices Experience: BS/MS or equivalent in Computer Engineering, Computer Science, or related field or relevant work experience 3+ years of software engineering industry experience Extensive experience in object oriented programming, ideally in GoLang or Python Experience in backend software development of distributed systems and concurrency Strong oral and written communication skills, and ability to collaborate with and influence cross-functional partners Benefits: Extended health and dental coverage options, along with life insurance and disability benefits Mental health benefits Family bui

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. Data Science is at the heart of Lyft’s products and decision-making. Data Scientists at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges - from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products. Driver Incentives Science owns the algorithms and systems behind incentive design, influencing driver engagement and marketplace efficiency — from real-time supply positioning to longer-horizon earnings and engagement programs. The team is responsible for designing pay and incentive mechanisms that are efficient and good for driver experience over the long run. As a Data Scientist specializing in Algorithms, you'll partner closely with product, engineering, and operations leaders to build and scale incentive systems, shape long-term mechanism design strategy, and deliver on critical business goals tied to marketplace efficiency and driver earnings. Candidates with strong optimization backgrounds — think mathematical programming, control theory, or operations research — are a great fit, though we welcome strong candidates from machine learning or causal inference as well. The ideal candidate thrives in a fast-paced environment and brings a hands-on, entrepreneurial mindset to drive results. Responsibilities: Collaborate with engineering and product teams to design, implement, and iterate on new features and algorithmic improvements for driver incentives and pay mechanisms. Design, develop, and deploy optimization models, algorithms, and systems for problems such as budget allocation, multidimensional cost-curve development, and incentive targeting. Write production model code; collabor

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. Data Science is at the heart of Lyft’s products and decision-making. Data Scientists at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges - from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products. Driver Incentives Science owns the algorithms and systems behind incentive design, influencing driver engagement and marketplace efficiency — from real-time supply positioning to longer-horizon earnings and engagement programs. The team is responsible for designing pay and incentive mechanisms that are efficient and good for driver experience over the long run. As a Data Scientist specializing in Algorithms, you'll partner closely with product, engineering, and operations leaders to build and scale incentive systems, shape long-term mechanism design strategy, and deliver on critical business goals tied to marketplace efficiency and driver earnings. Candidates with strong optimization backgrounds — think mathematical programming, control theory, or operations research — are a great fit, though we welcome strong candidates from machine learning or causal inference as well. The ideal candidate thrives in a fast-paced environment and brings a hands-on, entrepreneurial mindset to drive results. Responsibilities: Collaborate with engineering and product teams to design, implement, and iterate on new features and algorithmic improvements for driver incentives and pay mechanisms. Design, develop, and deploy optimization models, algorithms, and systems for problems such as budget allocation, multidimensional cost-curve development, and incentive targeting. Write production model code; collabor

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. Lyft is seeking an Analyst to join our Business Forecast & Planning team within Customer Care Operations. This role owns the forecasting, capacity, and scheduling models that keep our global support workforce sized and staffed correctly. You'll work hands-on with our BPO partners to translate demand signals into staffing plans, and bring deep contact centre planning expertise to a team managing support at scale across multiple sites and geographies. Responsibilities: Support the development and upkeep of the models and frameworks that connect business drivers to staffing and resourcing decisions Translate large, complex, and sometimes ambiguous data into clear, actionable insights that support leadership recommendations Own day-to-day planning execution, from dashboards maintenance and reporting to business reviews, with a sharp focus on accuracy and the details that matter Leverage AI tools to build and maintain dashboards, reports, and analyses that support the team's recommendations and insights Identify and help drive opportunities to improve how the team plans and operates Move quickly on ambiguous, time-sensitive requests, using modern tools to work smarter and faster Experience: 2+ years in planning, forecasting, capacity/resource management, or a related analytical role; consulting, investment banking, FP&A, or ops strategy backgrounds also fit well Sharp analytical mindset with hands-on SQL and Python skills AI fluency, comfortable using AI tools to accelerate analysis and problem-solving Strong business acumen paired with genuine curiosity about how large, complex business systems work; fast learner with a bias towards action, comfortable building context quickly across new systems and stakeholders Self-directed, with a track record of turning data into real decisions; strong commun

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

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. As a Data Scientist in the Pay, Integrity & Identity org, you will collaborate with our world class team of engineers, product managers, analysts and other data scientists to help create best in class pay platforms, stop fraudulent actors from harming our riders & drivers fraud and build user trust on the Lyft platform. You will run experiments (A/B tests) and develop data driven solutions to launch new features and remove the bad actors from the Lyft platform while maintaining a positive experience for genuine users. We’re looking for an intellectually curious individual who has extraordinary attention to detail, a track record of analytical problem-solving and skilled communication. Prior experience in the fintech, fraud or identity space is preferred. Responsibilities: Design and analyze experiments in collaboration with other scientists, product & engineering; communicate findings to stakeholders and facilitate launch decisions Leverage advanced statistical techniques to generate quantitative insights and develop machine learning models Analyze the wide variety of signals available to identify patterns in large datasets and uncover root causes Partner with product managers, engineers, and operators to translate analytical insights into decisions and action Build data pipelines and develop analytical frameworks to monitor business and product performance Set business metrics that measure the health of our products, as well as passenger and driver experience Collaborate with product and engineering and communicate findings to stakeholders in a clear and concise manner Experience: Degree in a quantitative field such as statistics, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience 4-6+ years of industry experi