Staff Software Engineer: Compute

About the Role Together AI is building the AI Native Cloud, an end-to-end platform for the full generative AI lifecycle, combining the fastest LLM inference engine with state-of-the-art GPU cloud infrastructure. The Together Cloud team builds the [Together GPU Clusters](https://www.together.ai/gpu-clusters) flagship IaaS product that provides high-performance, AI-ready GPU clusters through a self-serve cloud console, along with the virtualized infrastructure layer powering Together's inference, RL, and fine-tuning products. As a Staff Software Engineer focusing on AI Compute in the Together Cloud org, you will set technical direction for and build major components of the next generation AI cloud platform – a highly available, global cloud infrastructure with cutting-edge virtualization of the latest ML hardware: GB300s/VRs, BlueField DPUs, InfiniBand and dual/quad-plane RoCEv2 fabrics. That virtualized computing platform powers our own SaaS products – inference, RL, and fine-tuning – and serves external cloud customers through self-serve offerings such as on-demand/reserved Kubernetes/Slurm clusters, across dozens of data centers and hundreds of thousands of GPUs. This is an architect-and-build role. Fully automated bootstrapping of GPU data centers, high-performance virtualization of GPU compute and DC networking without compromising isolation or portability, and fault-tolerant decentralized control planes — you'll set the architecture for these across our global and in-DC services, and be a key owner of the hardest parts, in the code as well as the design. Your designs will span the IaaS layer of a greenfield Vera Rubin data center up to the global management plane that schedules capacity across all of them. At this level the job is as much leverage as code: the standards you set and the engineers you grow decide how fast the rest of Together Cloud ships. Responsibilities Own the GPU and network virtualization stack: the hypervisor, kernel, and SDN work that keeps

Senior: PLN 260,400 - 352,200 Staff: PLN 350,700 - 474,400 Subject to alignment to the responsibilities and duties of the role - we currently have multiple positions available at both Senior and Staff level. Location: Gdańsk. This role is not available as fully remote. About the job Build the AI compute libraries that turn next-generation hardware into real performance. As a Staff Software Engineer, you will design and optimise high-performance compute kernels for AI workloads. You will work across linear algebra, tensor operations, numerical correctness and low-level performance. Your work will help customers get more from advanced AI hardware. That means shaping the libraries behind faster, more reliable AI systems at scale. You will write C++ and Python, profile on Linux and make hard trade-offs across memory, threading and numerical behaviour. You will also guide technical direction, review designs and help engineers make better decisions. This role suits someone who wants deep technical ownership without moving away from hands-on engineering. The team and culture You will join the ML Kernels & Runtime team, which builds the compute libraries that expose hardware performance to customers. The work is technical, detailed and close to the machine. Engineers own problems from design through validation, benchmarking and production quality. Decisions are made through evidence, clear technical reasoning and open debate. You will move quickly, speak up when something can be better and help others raise the quality of their work. Leadership here means taking responsibility for outcomes, not waiting for permission. What we’re looking for Excellent C++ and Python skills, with experience building performance-sensitive software Strong understanding of processor architectures, Linux profiling and low-level optimisation Proven technical leadership, including guiding engineering direction, reviewing designs and mentoring others Ability to balance hands-on implem

Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase . As a Staff Software Engineer on the Compute Platform team within the Platform Product Group, you'll own the routing layer that connects every service at Coinbase to its customers. The Compute Platform team builds and operates core compute orchestration infrastructure based on CNCF technologies including Istio, Envoy, and Kubernetes. You'll design and deliver net-new routing capabilities that improve the scalability, reliability, and performance of application traffic across our entire fleet. What you'll do: Own the design, development, and automation of routing infrastructure that directs application traffic to Kubernetes backends at scale Build capabilities on top of Envoy-based reverse proxies, Istio gateways, Ambient mesh, and SPIFFE service authentication Drive measurable improvements to latency, reliability, and performance of application routing systems across Coinbase's production environment Shape the paved-road developer experience for operating applications at Coinbase, with emphasis on AI-driven tooling, processes, and support Partner with Security, Reliability, and Observability teams to ensure routing infrastructure meets cross-cutting platform requirements Required Skills and Experience: 8+ years of experience in software engineering and infrastructure roles 5+ years of hands-on engineering experience with application routing technologies including reve

About the Role As a Staff Infrastructure Engineer on the Compute & Storage Platform team in Toast, you will design, build, and operate the foundational distributed systems powering the Toast platform as the company expands into further international markets and broadens its product offerings to encompass retail shopping. You will bridge the gap between core infrastructure and cutting-edge product development—building low-latency state and storage systems, scalable compute clusters, and high-throughput streaming pipelines. Your work will directly enable Toast product delivery across our Product Development Life Cycle (PDLC). For this role it is a requirement that you have at least 8+ years of industry experience. Key Responsibilities Online Databases and Storage: Architect, deploy, and manage low-latency, highly available storage solutions (RDS Postgres and Aurora Postgres; DynamoDB; OpenSearch; S3) to power applications across the Toast product ecosystem. High Availability & Realtime Performance: Ensure sub-second end-user latency, near-zero-downtime reliability, and high throughput across storage engines to support Toast's products across POS, phone, computer, drive-thru, and kiosk interfaces. Infrastructure for AI-Driven PDLC: Automate infrastructure provisioning and compute pipelines. Cost Optimization & Performance Engineering: Right-size compute/storage resources, implement auto-scaling policies, and manage AWS infrastructure costs without compromising platform reliability or latency SLAs. Required Qualifications 1. Core Infrastructure & Automation Cloud & Infrastructure as Code: Hands-on experience building and managing scalable cloud infrastructure on AWS using Infrastructure as Code (Terraform, CloudFormation, etc.). Software Development: Fluency in Python , Go , or a similar language for infrastructure automation, tooling, or backend development. 2. Storage & Compute Operations Databases: Deep experience operating and tuning en

About the Role REMOTE IN INDIA We're looking for a software engineer to build the Kubernetes-native control plane that provisions and runs our GPU inference fleet. You'll design a manifest-driven API where the inference team declares what they need, whether that's a cluster, a model deployment, or a capacity change, and our controllers handle the reconciliation, provider/runtime selection, and lifecycle management underneath, so the inference team never has to know or care which specific serving stack, scheduler, or hardware pool is doing the work. You'll also build the systems that keep the fleet efficient, not just running, including defragmentation and rebalancing logic that consolidates scattered workloads back into contiguous capacity, and scheduling/bin-packing improvements that push GPU utilization up without hurting latency. The core value we're after is decoupling the people building on top of the platform from the operational and runtime complexity underneath, while squeezing more usable capacity out of the same hardware. You'll build the controllers, reconciliation loops, and self-service surface (API/CLI, not tickets) that make that decoupling real, plus the event-driven health, remediation, and utilization systems that keep it running and efficient without a human in the loop. Strong candidates have hands-on experience with Kubernetes controller/CRD patterns, have built or operated a platform API that abstracts multiple backends behind one interface, understand GPU scheduling and capacity efficiency (fragmentation, bin-packing, right-sizing), and think about GPU infrastructure as software to be engineered. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities Build the provisioning state machine

About the Role We're looking for a Software Engineer to build the systems that treat infrastructure as software. This role owns the software state machines that provision hardware, bring it into service, and manage its full lifecycle — turning racks of GPUs into running inference clusters without a human touching a runbook. The Research and Inference team is your customer: today they file tickets and wait; the target state is that they issue a single API call to stand up, scale, or tear down a cluster, and the system takes care of the rest. The platform is manifest-driven such that teams declare the desired state of a cluster or host — shape, topology, software stack — and the system is responsible for reconciling reality to that manifest, continuously, through every stage of its lifecycle. You will design the engines that manifest the schema, the engines that execute against it, and the workflows that carry a piece of hardware or a cluster from one state to the next—taking it from bare metal to a fully functioning AI cluster for training or inference. You'll write production code which is typed, tested, versioned, and deployed through CI/CD that models infrastructure state and reconciles it, the same way a Kubernetes controller reconciles a cluster's desired state. Success looks like eliminating manual provisioning work, not documenting it better. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities Build the provisioning state machine: design and implement the software that models the full lifecycle of a physical host from discovery, inference bring-up to GPU driver/CUDA stack, health validation, and decommission/RMA — as explicit, versioned states and transitions. Build the self-service API: design declarative