Preparing for an interview at Relace requires a dual focus on rigorous computer science fundamentals and practical, hardware-level machine learning experience. You should approach your preparation with a first-principles mindset, ready to explain not just what tool you would use, but how that tool works under the hood.

Relace evaluates candidates across several core criteria to ensure alignment with their highly technical, high-leverage engineering culture:

Low-Level & Hardware-Aware Optimization – You must demonstrate a deep understanding of how code executes on hardware. This includes knowledge of GPU memory hierarchies, instruction pipelining, cache locality, and parallel computing paradigms.

Mathematical & Algorithmic Rigor – Whether designing a new training loss or optimizing a kernel, you should be comfortable with the underlying mathematics of machine learning, including linear algebra, calculus, and optimization theory.

Systems Architecture & Scalability – You will be assessed on your ability to design robust, fault-tolerant, and highly performant systems that can scale to hundreds of millions of users.

Execution Speed & Adaptability – As a fast-growing, Series A startup, Relace values engineers who can quickly turn theoretical breakthroughs into production-ready code without sacrificing quality.

The interview process at Relace is designed to be highly technical, transparent, and reflective of the actual day-to-day engineering challenges you will face on the job. The team values your time and aims to move candidates through the pipeline efficiently, maintaining open communication throughout.

The journey typically begins with an initial technical screening, followed by deep-dive technical rounds, and culminates in a comprehensive onsite interview. Throughout the process, the focus is on assessing your problem-solving process, your coding fluency in Python and systems languages like C++ or Rust, and your understanding of deep learning systems.

The timeline above details the typical stages a candidate will navigate during the hiring process. This structured progression ensures that both your systems-level engineering capabilities and your alignment with the team's collaborative culture are thoroughly evaluated. Candidates should use this timeline to pace their preparation, ensuring they dedicate ample time to both coding practice and system design review.

To excel in the Relace interview loops, you must be prepared to demonstrate deep expertise in several specialized areas of machine learning systems.

GPU Programming and CUDA Kernel Optimization

This area is critical for candidates applying for the Machine Learning Engineer role. You must understand how to write and optimize code that runs directly on GPU hardware to achieve maximum compute utilization.

Be ready to go over:

• Thread Hierarchy – How grids, blocks, and threads map to streaming multiprocessors (SMs).
• Memory Coalescing – Ensuring global memory accesses by threads in a warp are coalesced into single memory transactions.
• Shared Memory Scratches – Utilizing on-chip shared memory to minimize high-latency global memory roundtrips.
• Advanced concepts (less common) – Triton programming language, writing custom fused operators, and managing bank conflicts in shared memory.

Example scenarios:

• "Walk us through how you would optimize a softmax kernel to avoid memory bandwidth bottlenecks."
• "Explain how you would write a custom element-wise addition kernel in CUDA and how you would profile its execution."

Distributed Training and Scale

For both engineering and science roles, understanding how to train models efficiently across hundreds or thousands of GPUs is essential.

Be ready to go over:

• Parallelism Strategies – Deep understanding of Megatron-style Tensor Parallelism and DeepSpeed-style Pipeline Parallelism.
• Communication Overheads – Understanding collective communication primitives like All-Reduce, All-Gather, and Reduce-Scatter.
• Mixed-Precision Training – Implementing FP16, BF16, and FP8 training pipelines while managing gradient scaling.
• Advanced concepts (less common) – Overlapping computation with communication using CUDA streams, and optimizing gradient accumulation steps.

Example scenarios:

• "How would you configure a training run for a 7B parameter model on a cluster of 8x H100 GPUs to maximize MFU (Model Flops Utilization)?"
• "Describe how you would diagnose a bottleneck where GPUs are constantly waiting on network communication during backward passes."

Small Language Models and Code Generation

This area is highly relevant for the Machine Learning Scientist track, focusing on model capabilities, training data curation, and evaluation.

Be ready to go over:

• Data Pipeline Curation – Deduplication, filtering, and synthetic data generation for code training datasets.
• Retrieval-Augmented Generation (RAG) – Designing latency-sensitive retrieval systems for IDE-integrated code agents.
• Model Quantization – Techniques like AWQ, GPTQ, or bitsandbytes for deploying models on resource-constrained hardware.
• Advanced concepts (less common) – Direct Preference Optimization (DPO) on code outputs and designing custom tokenizers optimized for programming syntax.

Example scenarios:

• "How would you design a training and evaluation pipeline to teach an SLM to use specific API tools reliably?"
• "What are the trade-offs between dense retrieval and sparse retrieval when building a code search index for a large repository?"

As a Machine Learning Engineer or Machine Learning Scientist at Relace, your day-to-day responsibilities will directly shape the core product offering. You will not be siloed; instead, you will own features from conceptual design to high-scale production deployment.

Your primary responsibilities will include:

• Performance Engineering – Writing high-performance CUDA kernels and optimizing memory layouts to push inference and training speeds to their theoretical limits.
• Model Training & Scaling – Designing and executing training runs for state-of-the-art small language models optimized for code generation, retrieval, and agentic tasks.
• Infrastructure Development – Building robust distributed systems to support low-latency inference pipelines capable of serving hundreds of millions of users.
• Cross-Functional Collaboration – Partnering directly with product and research teams to productionize novel architectures and rapidly deploy them to key partners like Lovable, Figma, and Vercel.
• System Profiling – Continuously profiling memory management, parallelization, and hardware utilization to identify and resolve performance regressions.

Relace looks for exceptional individuals who possess a blend of strong software engineering foundations and deep machine learning expertise.

Technical Skills

• Systems Languages – Fluency in Python and at least one systems-level language, with a strong preference for C++ or Rust.
• ML Frameworks – Mastery of deep learning frameworks such as PyTorch or JAX.
• Optimization Tools – Hands-on experience with CUDA, Triton, TensorRT, or other low-level GPU programming and profiling tools.
• Distributed Compute – Experience with distributed training frameworks like DeepSpeed, Megatron-LM, FSDP, or Ray.

Experience & Soft Skills

• Industry Experience – 2+ years of working in high-performance machine learning infrastructure, performance-critical systems, or cutting-edge ML research environments.
• Education – A strong quantitative background (BS, MS, or PhD) in Computer Science, Mathematics, Physics, or a related quantitative field, or equivalent deep industry experience.
• Collaborative Drive – A passion for elegant systems design, mathematical beauty, and a desire to work in-person in a fast-moving startup environment in San Francisco.