Io Module Engineer Jobs

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. We are seeking a Software Driver Developer to create reference drivers for our AI-IP in a reference system. You will also support our AI-IP customer optimizing our reference driver into our customer specific systems. This role is hybrid, based out of Belgrade, Serbia. 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 Deep systems engineer with 7+ years of experience in device drivers or low-level systems programming. Strong proficiency in C and C++ with a solid understanding of hardware-software interfaces. Hands-on experience developing Linux kernel drivers, including kernel architecture, debugging, and kernel-space/user-space interactions. Experience developing drivers for high-speed I/O or interconnect technologies such as PCIe; PCIe driver development is strongly preferred. Familiar with user-mode and kernel-mode driver models and understands that Linux kernel driver development differs meaningfully from bare-metal or RTOS-based driver development. What We Need Design and evolve the driver layer that provides high-performance APIs for Tenstorrent devices. Develop and maintain user-mode and kernel-mode drivers for Te

Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange™️ platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 160+ public exchanges globally and thousands of private exchanges at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform. We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler. Role We are looking for a Sr. Staff Software Development Engineer-AI Security to join our team. This is a Hybrid (based in San Jose, CA or Bellevue, WA with a 3 days in office requirement) role, reporting to the Director of Software Engineering in the Emerging Tech department. You will be responsible for designing and implementing core infrastructure components and distributed systems, serving as a foundational architect for our AI security solution. This high-impact role focuses on scaling security infrastructure to support hundreds of millions of users, collaborating with stakeholders across the development lifecycle to drive innovation and technical excellence. What you’ll do (Role Expectations) Architect, develop, and optimize a low-latency, high-throughput AI Security plane utilizing Rust, specifically leveraging its async/await model for highly efficient I/O and service-oriented architecture Build resi

About Anyscale: At Anyscale , we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray , a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI , Uber , Spotify , Instacart , Cruise , and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date. About the role Ray aims to provide a universal API for building distributed applications. To achieve this goal requires a distributed system with high levels of performance and reliability. We're looking for engineers with systems software experience that are interested in contributing to the Ray backend. About the Ray Core Team The Ray Core team develops and maintains the Ray C++ backend (e.g., distributed scheduler, language runtime integration, I/O and memory subsystems). We are responsible for the reliability, scalability, and performance of Ray as well as ensuring that Ray provides the right feature set to support higher level libraries and use cases. The team works on a balance of new features / distributed libraries, test infra improvements, debugging, and longer-term architectural improvements to Ray. A snapshot of projects you can work on: Optimizing performance of large-scale workloads on Ray Stability and stress testing infrastructure Improving fault tolerance (HA) As part of this role, you will: Leading cross-team projects while mentoring junior team members Develop high quality open source software to simplify distributed programming (Ray) Identify, implement, and evaluate architectural improvements

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? The Data Infrastructure team at Cohere is responsible for the storage and data movement layer underlying every model training run. We're building the unified storage layer that feeds our training workloads. It needs to serve petabytes of training data and model checkpoints fast enough to keep thousands of GPUs busy across several training clusters. In this role, you’d have an opportunity to build this system from the ground up. You’d be a key contributor, working on a problem few teams have had to solve at this scale. In this role, you will: Design, build, and operate the distributed storage system that feeds model training and evaluation. Run this system multiple on Kubernetes clusters at petabyte scale. Work with researchers and training-infra teams on how jobs actually read and write data, and turn that into throughput, latency, and durability requirements Work through the networking, I/O, and consistency problems of moving large datasets and checkpoints across regions and backends, with GPU idle time and time-to-insight as the measures of success You may be a good fit if you have: Strong storage fundamentals,

Title: Staff Site Reliability Engineer, Cloud Cost & Engineering Efficiency Location: Noida / Bangalore (Hybrid) Summary of role Sumo Logic's microservices architecture, hosted on AWS, ingests petabytes of data daily across many geographic regions in support of our planet-scale observability and security products, serving hundreds of millions of queries a day against thousands of petabytes of data. At that scale, every inefficiency — in code, architecture, or infrastructure — compounds into real cost. This role sits within the Product SRE organization, working alongside your global SRE team on your product area's reliability roadmap — with a mandate that leads with code as much as operations. You'll find where Sumo's systems are spending more compute, storage, or engineering time than necessary, and fix it in the code and architecture, not just the infra config, while also carrying the fuller SRE mandate — reliability, security posture, and improving the day-to-day experience of the engineers within your product area. You'll be part of a team that blends SRE and backend software engineering skillsets, partnering closely with product engineering teams across your product area. This is an engineering role — the work is about shipping code, architecture, and system-level changes that improve unit economics, not managing cost dashboards, tagging, or reserved-instance/savings-plan purchasing. What you’ll do Continuously discover cost and efficiency opportunities through production profiling, telemetry, cost data, capacity trends, and system-level analysis — across algorithmic inefficiencies, resource-heavy code paths, and architectural decisions — and turn ambiguous problems into prioritized engineering initiatives. Apply performance and capacity engineering techniques to understand CPU, memory, storage, network, and I/O behavior under real production workloads, and optimize the resulting resource footprint. Write production-grade code to implement the optimizations

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Voice is becoming the internet’s next interface, but a production-grade Voice AI system is "hard to build" . You’ll join a small founding team of Baseten Voice AI, focused on bringing state-of-the-art open source models into production for Voice AI customers across productivity, customer service, clinical conversation, creator tools, education, and more. You’ll make a meaningful impact on people’s daily lives and help reshape these industries. This is a high-impact, high-ownership role. You will be the primary owner of Baseten Voice AI - our in-house inference stack to power Voice AI models - from product roadmap through engineering implementation. You’ll partner closely with Forward Deployed Engineers, Model Performance Engineers, and sister engineering teams to push the boundaries of Voice AI. EXAMPLE INITIATIVES Develop world-class model serving stack for state-of-the-art open-source voice models - reduce end-to-end and tail latency (p95/p99), increase throughput, and improve GPU efficiency via profiling, runtime tuning, and server-level optimizations. Build large-scale, real-time infrastructure for multi-model voice agents - orchestrate STT, TTS, and agent components with streaming I/O to meet customer SLOs. Design tight training and inference iteration loops for voice model customization - enable fast evaluation, safe rollout, and rapid experimentation for custom voice model development. Past projects: Base

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. The Architecture team at Tenstorrent is designing the central compute chiplet that powers our next generation automotive SoC. This role is about turning real world automotive and safety requirements into architecture that performs, scales, and ships fast and reliably. If you think silicon design should start with clarity and end with confidence, this is where your experience makes the biggest impact. This role is remote, based in Munich, Germany. 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 Approach design with a systems mindset, always thinking in trade offs, not just specs and block diagrams. Bring deep knowledge of SoC architecture, from interconnects and I/O to memory and CPUs. Confident working within performance, power, and reliability constraints. Familiar with, or eager to dive into, functional safety, ISO 26262, and automotive grade design challenges. What We Need Someone to architect and specify one or more chiplets in the robotics and automotive family. 5+ years of experience translating high level requirements into detailed architectural direction. Ability to identify impactful features that boost PPA, safety, and reliab

Title: Staff Site Reliability Engineer, Cloud Cost & Engineering Efficiency Location: Noida/ Bangalore (Hybrid) Summary of role Sumo Logic's microservices architecture, hosted on AWS, ingests petabytes of data daily across many geographic regions in support of our planet-scale observability and security products, serving hundreds of millions of queries a day against thousands of petabytes of data. At that scale, every inefficiency — in code, architecture, or infrastructure — compounds into real cost. This role sits within the Product SRE organization, working alongside your global SRE team on your product area's reliability roadmap — with a mandate that leads with code as much as operations. You'll find where Sumo's systems are spending more compute, storage, or engineering time than necessary, and fix it in the code and architecture, not just the infra config, while also carrying the fuller SRE mandate — reliability, security posture, and improving the day-to-day experience of the engineers within your product area. You'll be part of a team that blends SRE and backend software engineering skillsets, partnering closely with product engineering teams across your product area. This is an engineering role — the work is about shipping code, architecture, and system-level changes that improve unit economics, not managing cost dashboards, tagging, or reserved-instance/savings-plan purchasing. What you’ll do Continuously discover cost and efficiency opportunities through production profiling, telemetry, cost data, capacity trends, and system-level analysis — across algorithmic inefficiencies, resource-heavy code paths, and architectural decisions — and turn ambiguous problems into prioritized engineering initiatives. Apply performance and capacity engineering techniques to understand CPU, memory, storage, network, and I/O behavior under real production workloads, and optimize the resulting resource footprint. Write production-grade code to implement the optimizations y

Location: San Francisco, CA (Hybrid: 4 days onsite/week). Relocation assistance available. About the Team: We build foundational platform software that enables reliable, secure, and performant products. The team works across system layers and partners closely with adjacent engineering groups to deliver robust capabilities from concept through launch. About the Role: We’re seeking a System Software Engineer to design, implement, and debug core platform components and the pipelines that build and update system images. You’ll work across operating system layers, focusing on performance, security, and deep system debugging to ship production‑grade systems. In this role, you will: Design, implement, and debug system‑level components and services across kernel and user space. Configure and maintain OS platform services (init, services, networking, security policies) and related tooling. Build and operate image and update pipelines, ensuring reliability, reproducibility, and rollback safety. Instrument and analyze performance using profiling and tracing; optimize CPU, memory, I/O, and power usage. Own platform observability and reliability: logging, crash capture, watchdogs, and diagnostics. Collaborate with cross‑functional teams to define interfaces and deliver end‑to‑end features. Establish strong engineering practices: code review, CI, reproducible builds, and release management. Partner with external suppliers to support builds and deployments. You might thrive in this role if you: Have shipped production systems software on modern operating systems. Are proficient in C/C++ and a scripting language, and comfortable with OS internals (concurrency, memory management, filesystems, networking, power management). Bring strong systems debugging skills using debuggers, tracers, profilers, and logs across kernel/user‑space boundaries. Understand configuration of platform services and interfaces, and can translate requirements into stable, well‑documented APIs. Are fluent in u

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 As a software engineer on the Scaling team, you’ll help build and optimize the low-level stack that orchestrates computation and data movement across OpenAI’s supercomputing clusters. Your work will involve designing high-performance runtimes, building custom kernels, contributing to compiler infrastructure, and developing scalable simulation systems to validate and optimize distributed training workloads. You will work at the intersection of systems programming, ML infrastructure, and high-performance computing, helping to create both ergonomic developer APIs and highly efficient runtime systems. This means balancing ease of use and introspection with the need for stability and performance on our evolving hardware fleet. This role is based in San Francisco, CA, with a hybrid work model (3 days/week in-office). Relocation assistance is available. In this role, you will: Design and build APIs and runtime components to orchestrate computation and data movement across heterogeneous ML workloads. Contribute to compiler infrastructure, including the development of optimizations and compiler passes to support evolving hardware. Engineer and optimize compute and data kernels, ensuring correctness, high performance, and portability across simulation and production environments. Profile and optimize system bottlenecks, especially around I/O, memory hierarchy, and interconnects, at both local and distributed scales. Develop simulation infrastructure to validate runtime b

About the Team Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve. Our work focuses on three pillars: high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long-lived, job-specific and user-provided processes. We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products). About the Role As a Training Performance Engineer, you’ll drive efficiency improvements across our distributed training stack. You’ll analyze large-scale training runs, identify utilization gaps, and design optimizations that push the boundaries of throughput and uptime. This role blends deep systems understanding with practical performance engineering — analyzing GPU kernel performance, collective communication throughput, investigating I/O bottlenecks, and sharding our models so we can train them at massive scale. You’ll help ensure that our clusters are running at peak performance, enabling OpenAI to train larger, more capable models with the same compute budget. This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees. In this role, you will: Profil

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 on a quest to find a strong Technical Program Manager to lead the end-to-end software development of our next-generation Chiplet based SiP. The scope spans from architecture to tape-out, silicon debug and software productization - across AI, CPU, Memory and I/O Chiplet domains. If you bring strong expertise in silicon development and exceptional Program Management skills - you will thrive in this role. You’ll drive end-to-end software across pre-silicon and post silicon stages - building complex programs, defining program metrics, pushing boundaries and innovating in the AI Supercomputers space This role is Hybrid, based out of Santa Clara, CA, Austin, TX, or Toronto, ON. 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 10+ years of experience in technical program management or chip-level engineering, with 2+ years in multi-chip/chiplet systems. Familiar with heterogeneous compute architectures (AI, CPU, memory, networking) and early software phases (architecture, pre-/post-silicon). Proven ability to manage 40+ engineer programs, external vendors (PCIe, D2D, Ethernet), and full software stacks from bare-metal to ML frameworks.

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 seeking a skilled Software Engineer with a passion for building high-performance, low-level systems software. In this role, you’ll contribute to the development and optimization of the infrastructure that powers our cutting-edge processors, with a primary focus on C/C++ development and low-level programming. You'll work closely with large inference and training model development to further drive Scale Out software and hardware performance. This role is hybrid, based out of Toronto, ON. Who You Are Strong C or C++ systems engineer with a deep understanding of memory, threading, I/O, and low-level execution models. Experienced building low-level software, drivers, embedded systems, or performance-critical infrastructure. Comfortable working close to hardware and curious about how systems behave under the hood. Proficient with Linux systems programming and debugging tools such as gdb, strace, and perf. Structured problem solver who thrives in fast-paced, highly technical environments. What We Need Design, develop, and maintain core infrastructure software that interfaces directly with Tenstorrent hardware. Build low-level libraries and APIs for communication and synchronization across compute nodes. Optimize system-level software for performance, scalability, and reliability in distributed environments. Support hardware

Job Details: Job Description: About the Role Join Intel's Client Computing and Physical AI Group and help enable the next generation of mobile and desktop client platforms. We are seeking a Senior Customer Solutions Engineer to serve as a key technical leader responsible for customer platform enablement, architecture collaboration, and execution across Intel's client platform ecosystem. In this role, you will work closely with OEM customers, engineering teams, and program stakeholders to drive the successful delivery of innovative platform technologies. You will play a critical role in platform planning, issue resolution, customer engagement, and cross-functional execution from product definition through product launch and production. What You’ll Do Key responsibilities will include but not limited to: Serve as the primary technical interface between customers and Intel engineering teams, driving platform enablement and customer success. Lead cross-functional execution across platform technologies including hardware, firmware, graphics, connectivity, and I/O technologies. Drive resolution of critical platform issues, customer escalations, and technical challenges throughout the product lifecycle. Represent customer readiness, platform health, and execution status in program reviews and leadership forums. Develop technical assessments, customer enablement plans, and supporting documentation to accelerate design wins and product launches. Drive technical program execution across customer and engineering teams by aligning milestones, dependencies, risks, priorities, and deliverables. Behavioral traits that we are looking for: Strong customer focus and the ability to build trusted partners

About Anyscale: At Anyscale , we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray , a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI , Uber , Spotify , Instacart , Cruise , and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date. About the role Ray aims to provide a universal API for building distributed applications. To achieve this goal requires a distributed system with high levels of performance and reliability. We're looking for engineers with systems software experience that are interested in contributing to the Ray backend. About the Ray Core Team The Ray Core team develops and maintains the Ray C++ backend (e.g., distributed scheduler, language runtime integration, I/O and memory subsystems). We are responsible for the reliability, scalability, and performance of Ray as well as ensuring that Ray provides the right feature set to support higher level libraries and use cases. The team works on a balance of new features / distributed libraries, test infra improvements, debugging, and longer-term architectural improvements to Ray. A snapshot of projects you can work on: - Optimizing performance of large-scale workloads on Ray - Stability and stress testing infrastructure - Improving fault tolerance (HA) As part of this role, you will: Develop high quality open source software to simplify distributed programming (Ray) Identify, implement, and evaluate architectural improvements to Ray core Improve the testing process for Ray to make re