Infrastructure Team Manager Jobs

We are seeking an experienced Quantitative Developer to join our Markets Quantitative Analytics team, partnering closely with Quantitative Analysts, Traders, and Technology professionals to build the next generation of pricing, risk, and analytics platforms. This is a hands-on technical role for a highly skilled software engineer with a passion for quantitative finance. You will be responsible for designing and delivering high-performance, scalable solutions that support front office trading businesses across asset classes. The role offers the opportunity to work on complex quantitative challenges, modern engineering practices, and large-scale distributed systems while helping shape the strategic direction of Citi's quantitative technology platform. Successful candidates will combine strong software engineering expertise with an understanding of quantitative methodologies and financial markets, translating sophisticated mathematical models into robust, production-grade solutions. Key Responsibilities Design, develop, and maintain high-performance pricing, risk, and analytics libraries used across Global Markets. Partner with Quantitative Analysts to transform research models and prototypes into scalable, production-quality software. Build and optimize quantitative applications using modern C++ and Python, applying strong software architecture and engineering principles. Own the full software development lifecycle, including requirements gathering, design, implementation, testing, deployment, and ongoing support. Drive engineering excellence through CI/CD adoption, automated testing, code reviews, and software quality best practices. Develop and maintain market data platforms and data pipelines supporting analytics, pricing, and risk workflows. Work with infrastructure teams to leverage distributed computing, cloud technologies, and scalable arc

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. As a Datacenter Liquid Cooling Architect, you will define, design, and architect next-generation liquid cooling infrastructure for Tenstorrent’s large-scale AI training and inference clusters. You will partner with systems engineering, mechanical engineering, software, and cross-functional design teams to develop chassis-, rack-, and cluster-scale cooling solutions, including CDU integration, telemetry and control, leak detection, and resilient operating strategies. This role will help shape reliable AI datacenter architectures and deployments for both internal and external customers. This role is on-site, based out of Toronto, Canada, Austin, Texas or Santa Clara, California. 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 datacenter and system thermal design professional with 10+ years of experience architecting cooling infrastructure for complex computing environments. An experienced liquid cooling architect who can design chassis- and rack-scale solutions for large AI training and inference clusters. A systems thinker who understands how mechanical, electrical, software, facility, and systems engineering decisions come toge

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Strategic Supportability Operations (SSO) sits within Risk Operations and User Policy Operations. Our team is focused on protecting and advancing Stripe's business, brand, and mission by deploying industry-leading supportability solutions that instill user and partner trust in Stripe, while enabling progressive and sustainable business growth. Unlike other Risk Operations teams, SSO handles the most complex, high-stakes, and ambiguous user business cases—ones that require deep investigation, custom risk analysis, and creative problem-solving at the intersection of user experience, compliance, financial partner requirements, and Stripe's growth ambitions. What you’ll do As a Risk Operations Analyst on the Strategic Supportability Operations team, you will investigate and assess complex, gray-area user business cases that require deep risk judgment and tailored solutions. You will go beyond standard procedures to understand the full risk profile of users, develop custom recommendations, and collaborate across internal teams to drive cohesive, well-reasoned decisions. You will also play a key role in identifying patterns across individual cases to improve our processes, policies, and detection systems at scale—bridging tactical case work with strategic, systemic impact across Risk Operations. Responsibilities Investigate complex, high-risk, and gray-area user business cases, applying deep risk knowledge to make sound, non-binary ju

About the Role: We're hiring Senior and Staff Data Platform Engineers to join the Data Infrastructure teams in Toronto. Together these teams own the infrastructure that processes billions of events per day: Spark-on-Kubernetes, Flink and Kinesis pipelines, a multi-petabyte Delta Lake, a large-scale MemoryDB feature store, Databricks multi-environment operations, and the catalog and lifecycle systems that govern it. The team is small and senior. Each engineer owns major platform components: you design it, build it, and support it in production. This is a hybrid-role based out of our Toronto office. You must be willing to travel to our Toronto office two days/week. What You'll Do: Spark-on-Kubernetes — EKS-based compute platform for Spark workloads: cluster configuration, Pod Identity IAM, job environment setup, Kustomize overlays, and shadow canary validation Event ingestion — Rust services and Flink jobs processing billions of events per day over Kinesis; throughput, reliability, on-call response, and AI-assisted operational tooling to reduce toil Platform infrastructure — Terraform modules for environment provisioning, cross-account AWS IAM, ARC runner infrastructure, and CI/CD for data platform changes Feature store and ML compute — Flink-based real-time feature pipelines feeding a large-scale MemoryDB cluster; GPU capacity governance and Databricks multi-environment operations for ML training workloads Workflow orchestration and CDC — Airflow-based DAG deployment, change data capture pipeline operations, and data quality monitoring Your Background: 3+ years building and operating production data platform infrastructure at the cluster or platform level, across Spark, Flink, Kinesis, Kubernetes, or equivalent Deep experience in at least one of: Spark-on-K8s cluster operations, Rust-based data or systems engineering, Kubernetes platform engineering and IaC, or data catalog and governance tooling Production AWS experience or equivalent: EKS, S3, Kinesis, and mu

We're looking for an ML Data & Platform Engineer to own the infrastructure that powers our speech AI models: the pipelines that source and prepare training data, and the platform that trains, evaluates, and serves them in production. Speech AI has a data problem most ML teams don't, and you'll be at the centre of solving it, working as part of our ML team to remove friction across the entire lifecycle and get better models into production faster. This is a broad, cross-functional role suited to someone who enjoys working across the full stack: data infrastructure, distributed systems, and production ML, and who takes ownership of problems end to end rather than waiting to be told what to fix. What you'll do Designing, building, and maintaining scalable data pipelines for ingesting, transforming, validating, and storing large datasets used to train our models Developing and maintaining web scraping and data acquisition solutions to keep training datasets fresh, high-quality, and available at scale Building and operating the infrastructure that lets the ML team deploy and evaluate new models quickly, and that serves models efficiently and reliably in production Optimising infrastructure for both iteration speed and production reliability, including GPU utilisation, job scheduling, and training efficiency Implementing observability (monitoring, logging, alerting) across data pipelines and ML systems to catch issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with a solid backend or data engineering foundation Hands-on experience with containerisation and orchestration (Docker, Kubernetes), and working with a major cloud provider Experience building data pipelines and ETL/ELT processe

About the Role Together AI runs one of the largest GPU fleets in the world. The Infra Agent Systems team builds the software systems that power and automate that infrastructure. We develop production AI agents that diagnose hardware failures, investigate incidents, correlate signals across the fleet, and automate operational workflows. Alongside these agents, we build the platform they run on, including knowledge graphs, retrieval systems, orchestration frameworks, and developer tooling. You’ll work across two areas: Infrastructure Agent Systems — Build production AI agents that help operate our GPU fleet by diagnosing failures, investigating incidents, gathering evidence from live systems, and assisting with remediation. These agents are used every day by our infrastructure and datacenter teams through APIs, CLI, dashboards, and Slack. Core Agent Platform — Build the platform that powers these agents, including knowledge graphs, search and retrieval, orchestration, evaluation, and the tooling that enables agents to reason, act, and continuously improve. We’re working on something that hasn’t really been done before: building knowledge graphs and self-improving AI agents that understand, operate, and continuously improve large-scale AI infrastructure. This is an opportunity to work at the intersection of AI agents, distributed systems, infrastructure, and automation , solving challenging engineering problems with real production impact. There’s an enormous amount to build, learn, and shape as we define the future of autonomous infrastructure. responsible for delivering the software but also for operating and supporting it in production. Why this Role You’ll work on two hard problems at the same time: making AI agents trustworthy enough to operate production infrastructure, and building the knowledge, retrieval, and distributed systems that make those agents effective. You’ll have the opportunity to build foundational systems from the ground up, work on infrastructur

JOB TITLE IT Operations Engineer, Application Support A CAREER WITH POINT72’S TECHNOLOGY TEAM As Point72 reimagines the future of investing, our Technology group is constantly improving our company’s IT infrastructure, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts experimenting, discovering new ways to harness the power of open-source solutions, and embracing enterprise agile methodology. We encourage professional development to ensure you bring innovative ideas to our products while satisfying your own intellectual curiosity. WHAT YOU’LL DO • Provide technical support for software applications and investigate, diagnose, and resolve application issues • Automate start-of-day and end-of-day checks for key applications. • Log and track incidents across applications in the production environment. • Implement monitoring and automation initiatives and develop custom solutions using Python, Shell, and/or Powershell scripts. • Create tactical support tools and scripts to improve the incident investigation process and enable • transparency into potential business impacts. • Prioritize and categorize incidents based on severity and impact. • Collaborate with the development team to improve applications based on user feedback. • Create and maintain documentation for responding to common errors and application incidents. • Assist with software applications deployment and configuration . • Provide training and assistance to users to ensure effective use of applications and systems. • Develop knowledge base resources to empower users to independently resolve common problems. WHAT’S REQUIRED • Bachelor's degree in computer science, information technology, or a related field. • Experience supporting middle- and back-office applications created in .Net, Java, C# etc. Ability to debug apps using of code, logs, alerts etc. • Literacy in complex SQL procedures/queries. • Ability to diagnose and troubleshoot technical issues.

Job Summary Reporting to the Memory Validation leadership team, the Senior Silicon DDR/HBM Validation Engineer will be responsible for the bring-up, validation, characterization and debug of advanced memory subsystems used in next-generation AI compute platforms. The role will focus on DDR and HBM technologies, working closely with silicon design, firmware, characterization, platform and systems teams to ensure robust memory subsystem functionality, performance and reliability. The successful candidate will take ownership of significant validation activities, contribute to debug and root-cause analysis efforts, and help improve validation methodologies, automation and infrastructure. The Team The Memory Validation team sits within the Validation organisation and is responsible for the bring-up, validation, characterization and debug of memory subsystems across Graphcore silicon and platform products. The team supports DDR and HBM validation activities throughout the product lifecycle, from first silicon through production readiness. Engineers work closely with architecture, RTL, firmware, characterization, systems and platform teams to ensure memory technologies meet functionality, performance, reliability and performance objectives. Responsibilities and Duties Execute validation and bring-up activities for DDR and HBM memory subsystems Verify memory bring-up software, firmware and scripts against defined project requirements Debug firmware, hardware and system-level issues and contribute to root-cause analysis activities Analyse system logs, validation data and characterization results to identify failures and performance issues Perform PHY characterization and analog-level analysis during stress testing and validation activities Develop and execute functional, stress, performance and corner-case validation tests Perform signal integrity, voltage, frequency and timing measurements using laboratory instrumentation Char

Opportunity Overview: We are seeking a Lead Software Engineer to join our Integrations team. In this role, you will be designing, developing, and scaling highly available healthcare integration systems supporting prior authorization workflows across providers, payers, and delegated entities. You'll direct a fast-paced, autonomous,agile team of software engineers in the design, development, and operational support of a growing enterprise integration platform. This is an opportunity to drive technical excellence at the intersection of healthcare interoperability and modern distributed systems. What you’ll do: Technical Leadership: Provide technical leadership across architecture, system design, platform scalability, reliability, and operational excellence. Platform Engineering: Design and build scalable, resilient, and high-performing systems that support critical business workflows and enterprise integrations. Integration Solutions: Lead the development and maintenance of secure integrations with internal and external platforms, partners, and third-party systems. Cloud & Automation: Drive cloud infrastructure, deployment automation, and software delivery practices that enable reliable and efficient releases. Distributed Systems: Design and support event-driven and distributed architectures that enable scalable and fault-tolerant processing. Operational Excellence: Establish monitoring, observability, and incident response practices to ensure system reliability, performance, and availability. Quality Engineering: Champion automated testing, quality assurance, and engineering best practices throughout the software development lifecycle. Production Support: Lead the resolution of complex production issues and drive continuous improvement in platform stability and operational efficiency. Cross-Functional Collaboration: Partner with product, operations, data, security, and business stakeholders to deliver solutions aligned with organizational goals. Agile Delive

Opportunity Overview: We are seeking a Sr. Data Engineer to help drive the technical vision of our Data Engineering team as we scale our infrastructure, services, and architecture. This role is ideal for someone who blends deep technical expertise with strategic thinking and cross-functional influence. As a Sr. Data Engineer, you will partner closely with other data and analytics engineers, architects, product leaders, and business stakeholders to evolve our data infrastructure in support of high-trust, scalable, and business-aligned outcomes. You’ll also serve as a force multiplier across teams; raising the bar for data engineering excellence and accelerating the maturity of our data platform capabilities. What you’ll do: Develop, and maintain robust ETL/ELT pipelines to ingest, transform, and load data from multiple source systems into enterprise data platforms. Ensure pipelines are scalable, reusable, fault-tolerant, and optimized for batch and near real-time processing requirements. Develop interactive dashboards, scorecards, and KPI reporting solutions using BI tools. Deliver executive, operational, and analytical reporting aligned with business goals. Identify repetitive manual reporting processes and automate them using scripting, workflow orchestration, scheduling tools, and dashboard refresh mechanisms to improve efficiency, accuracy, and turnaround time. Collaborate with internal stakeholders and leadership teams to understand data integration and reporting needs, define KPIs, document business rules, and translate requirements into scalable technical solutions. Implement data validation checks, reconciliation controls, lineage documentation, metadata standards, and governance policies to maintain trusted, secure, and compliant data assets across the organization. Provide technical guidance, code reviews, best practices, and mentor junior team members. Investigate and resolve production incidents related to pipelines, delayed reports, data mismatches

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

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Senior Software Engineer — Cortex Training The Snowflake ML Platform team's mission is to let customers run their most demanding ML/AI workloads inside Snowflake. Cortex Training is our LLM post-training platform: it turns scarce, expensive GPU capacity into a simple, composable service, so customers can adapt open-weight foundation models to their own business problems while we handle the hard distributed-systems parts, including scheduling, orchestration, multi-node training and inference, fault tolerance, and throughput. The platform already runs post-training at scale. Under the hood, it decouples GPU computation from the training loop and exposes it as primitive APIs that compose into everything from SFT to full RL workflows. You'll work alongside a team that ships fast & sweats reliability and the researchers behind DeepSpeed. We're looking for an engineer who thrives in the ML infrastructure layer and brings a solid understanding of LLMs and post-training to help us scale and grow it. YOU WILL: Design and build across the full stack — from the public training APIs and SDK through the control plane to the GPU data plane. Scale the distributed systems that make GPU compute serverless — multi-tenant scheduling, placement, and capacity-aware routing across regional G

Location Details: India, Remote At GoDaddy the future of work looks different for each team. Some teams work in the office full-time; others have a hybrid arrangement (they work remotely some days and in the office some days) and some work entirely remotely.​ This is a remote position, so you’ll be working remotely from your home. You may occasionally visit a GoDaddy office to meet with your team for events or meetings. Join Our Team The Network Security team is responsible for securing GoDaddy's global hybrid infrastructure across data centres, cloud, edge, and remote-access environments. We partner across Security, Infrastructure, Cloud, and Engineering teams to build scalable, resilient, and secure solutions that support the business. As a Principal Network Security Engineer, you'll act as a senior technical leader, helping define network security strategy, influence architecture across teams, and drive security outcomes at enterprise scale through technical expertise, systems thinking, and cross-functional leadership. What you'll get to do... Define and drive network security architecture across hybrid environments, including data centres, cloud, edge, and remote-access technologies Design trust boundaries, segmentation strategies, secure connectivity patterns, and network controls that reduce risk and improve security posture Lead complex technical initiatives, migrations, and architectural decisions while balancing security, reliability, performance, and operational requirements Establish scalable approaches for policy governance, automation, monitoring, telemetry, and security control validation Partner across engineering organizations to drive large-scale initiatives, mentor engineers, and influence technical direction through architecture reviews and technical leadership Your experience should include... 10+ years of experience in Network Security Engineering, Network Architecture, or Security Engineering, including ownership of enterprise-scale secu

Location Details: At GoDaddy the future of work looks different for each team. Some teams work in the office full-time; others have a hybrid arrangement (they work remotely some days and in the office some days) and some work entirely remotely.​ Remote: This is a remote position, so you’ll be working remotely from your home. You may occasionally visit a GoDaddy office to meet with your team for events or meetings. About The Team The Commerce Site Reliability Engineering team is responsible for the reliability, scalability, and day-to-day operation of the platforms that power GoDaddy's Commerce ecosystem. We build and operate shared infrastructure, support critical production systems, and partner closely with engineering teams to ensure services remain secure, resilient, and highly available. As a Senior Site Reliability Engineer, you'll join a team that values ownership, operational excellence, and continuous improvement. Engineers are empowered to identify problems, drive meaningful change, and influence how reliability is delivered across the broader Commerce organisation. From improving operational maturity and reducing toil to modernising delivery platforms and strengthening incident response practices, this team plays a key role in enabling engineering teams to move quickly and safely. You'll work closely with engineers across infrastructure, cloud, security, networking, and application teams while helping shape the future of reliability engineering at GoDaddy. This role offers significant opportunity to broaden your impact, develop technical leadership skills, and grow toward Staff and Principal engineering positions over time. What you'll get to do... Lead reliability and operational improvement initiatives across GoDaddy's Commerce platform, helping engineering teams build and operate services safely and at scale. Own critical production systems, drive incident response and post-incident improvements, and continuously raise the bar