To stand out in the Together AI interview process, your preparation must go beyond standard algorithm practice. You need to demonstrate a deep, practical understanding of how hardware and software interact at scale.

Distributed Systems Design – You must show that you can design robust, fault-tolerant microservices and storage engines. Focus on demonstrating your grasp of consensus protocols, data replication, caching strategies, and how to handle network failures gracefully.

Low-Level Systems Knowledge – Interviewers will probe your understanding of operating systems, memory footprints, and resource virtualization. Be prepared to explain how hypervisors, containers, and hardware accelerators interact to deliver bare-metal-like performance.

Practical Coding Proficiency – You will need to write clean, idiomatic, and highly performant code (preferably in Golang). Expect to be evaluated on your ability to handle concurrency, write robust unit tests, and structure your code for long-term maintainability.

Systems-Level Troubleshooting – You must demonstrate a systematic approach to debugging complex, distributed systems. Interviewers want to see how you isolate variables, analyze metrics, and trace requests across multi-tiered architectures under stress.

The interview loop at Together AI is designed to evaluate both your deep technical competence and your ability to execute in a fast-paced, research-driven environment. The process is rigorous but highly structured, focusing on real-world engineering challenges rather than abstract academic puzzles.

The journey typically begins with an initial recruiter screen to align on your background, career goals, and compensation expectations. This is followed by one or more technical phone screens focusing on systems programming, concurrent coding, or high-level architecture design. Once you pass these initial hurdles, you will move to the virtual or on-site interview loop, which deep dives into distributed systems, networking, infrastructure automation, and behavioral alignment.

The visual timeline above outlines the typical progression from your initial application to the final offer stage. Candidates should expect the entire process to move quickly, though depth of technical evaluation remains consistently high across all stages. Use this timeline to pace your preparation, ensuring you allocate ample time for both coding practice and deep architectural reviews.

Distributed Systems & Scheduling

Managing thousands of GPUs across multiple geographic zones requires highly sophisticated scheduling and orchestration. Together AI evaluates your capacity to design and maintain these complex systems.

Be ready to go over:

• GPU Scheduling Algorithms – How to schedule workloads to minimize fragmentation, optimize network topology alignment, and handle preemption.
• State and Consensus – Managing distributed state across unstable networks, using tools like etcd or custom consensus implementations.
• Fault Tolerance – Designing systems that gracefully handle node failures, network splits, and hardware degradation without interrupting long-running ML training jobs.
• Advanced concepts (less common) – Decentralized AI workloads, peer-to-peer data distribution, and dynamic resource re-allocation.

Example scenarios:

• "Design a scheduler that handles both short-lived, high-priority inference requests and long-running, low-priority fine-tuning jobs on a shared pool of GPUs."
• "Explain how you would coordinate checkpointing for an LLM training run across 512 nodes so that a single node failure doesn't ruin hours of progress."

Virtualization & Containerization Internals

To build a highly efficient cloud, Together AI virtualizes bare-metal hardware. You must demonstrate a strong grasp of how physical compute, storage, and networking are partitioned and exposed to users.

Be ready to go over:

• Kubernetes Internals – Writing custom resource definitions (CRDs), implementing custom controllers, and managing device plugins.
• Hypervisors and VMs – Understanding technologies like QEMU/KVM, KubeVirt, and VFIO for high-performance virtual machine provisioning.
• Hardware Passthrough – The mechanics of exposing GPUs, SmartNICs, and storage controllers directly to virtualized workloads.
• Advanced concepts (less common) – Single Root I/O Virtualization (SR-IOV), DPU offloading, and custom Linux kernel patches.

Example scenarios:

• "Walk me through how a packet travels from a virtual machine out to a physical Infiniband switch, highlighting potential performance bottlenecks."
• "How would you design a Kubernetes device plugin to expose custom hardware accelerators to containerized workloads?"

High-Performance Networking & Storage

AI workloads require massive amounts of data delivered at extreme speeds. Your understanding of network topologies and storage architectures is highly scrutinized.

Be ready to go over:

• Data Center Networking – Deep familiarity with VXLAN, VPC setups, routing protocols, and software-defined networking (SDN).
• High-Throughput Storage – Designing parallel file systems, distributed object stores, and optimizing read paths for large datasets.
• Network Performance – Understanding RDMA, Infiniband, and how to eliminate packet loss in high-concurrency environments.

Example scenarios:

• "Design a storage backend that can feed training data to thousands of GPUs concurrently without hitting IOPS bottlenecks."
• "How would you diagnose and resolve a network congestion issue causing latency spikes in a distributed training cluster?"

As a Software Engineer on the Together Cloud Platform or Together Cloud Infrastructure team, your day-to-day work will directly impact the performance and reliability of the entire AI Acceleration Cloud.

You will identify, design, and develop the foundational backend services and API microservices that power Together AI's customer-facing products. This includes writing clean, well-tested, and maintainable software (primarily in Golang) and implementing Infrastructure as Code (IaC) to manage physical and virtual resources. You will work closely with product and research teams to translate complex functional requirements into robust, scalable engineering solutions.

Beyond writing code, you will play an active role in maintaining system health. This involves conducting thorough design and code reviews, creating comprehensive developer documentation, and formulating testing strategies to guarantee fault tolerance. You will also participate in an on-call rotation to quickly address and resolve critical infrastructure incidents, ensuring the cloud remains highly available around the clock.