Magic.dev Interview Guide (2026): Frontier Long-Context Code Models

Magic was founded in 2022 by Eric Steinberger, the CEO and a former Meta AI researcher, and Sebastian De Ro, the CTO. The company builds AI models for software engineering and frames its goal as automating parts of the development process rather than shipping a chat product. Its best-known result is LTM-2-mini, a model with a 100-million-token context window (roughly 10 million lines of code) that it trained in 2024, paired with a sequence-dimension algorithm the company says is far cheaper than standard attention at that length. Magic trains on G4 and planned G5 supercomputers built with Google Cloud on NVIDIA H100 and Grace Blackwell hardware. It has raised over $450 million and was reported near a $1.5 billion valuation, with backers including Nat Friedman, Daniel Gross, Eric Schmidt, CapitalG, Atlassian, Jane Street and Sequoia.

Process

Very little about Magic’s loop is documented publicly, and the one detailed candidate account online describes a short pipeline rather than a big-tech gauntlet. Based on its stage and that account, expect a recruiter or founder screen, then a technical exercise: a live coding problem or a take-home you push to a repo with a short design write-up, followed by team-specific interviews. For engineering roles, plan for system design rounds on distributed training and inference at scale, while research roles lean toward your past work and knowledge of the literature. A behavioral conversation, often with a co-founder given the team’s size, tends to sit near the end. Expect the whole cycle to be fast, maybe one to three weeks, since a company this small can move quickly and skip the layers larger firms add. One public review flagged a five-hour take-home with limited chance to explain the result, so clarify scope and evaluation criteria before you start.

What they actually ask

• Ultra-long-context architecture: why standard attention becomes impractical at millions of tokens, how memory and compute scale with sequence length, and what alternatives (sparse, linear, or recurrent-style mechanisms, KV-cache tradeoffs) you would weigh.
• Training and inference infrastructure: designing data-parallel, tensor-parallel, and pipeline-parallel training across thousands of GPUs, handling failures and checkpointing on long runs, and optimizing inference throughput for a non-standard architecture.
• Evaluation of code models: how you would measure whether a model actually helps on real software tasks, what popular benchmarks miss, and how to build evals that resist contamination and overfitting.
• Distributed systems fundamentals: storage, networking, and scheduling for large GPU clusters, and reasoning about where bottlenecks appear under load.
• Hard coding problems: efficient data structures and algorithms, sometimes framed around data pipelines, streaming, or manipulating very large inputs rather than textbook puzzles.
• For research roles: familiarity with the deep learning literature and the ability to defend design choices from your own past projects.

Levels and comp (2026)

Magic is small and does not publish a leveling ladder like larger firms, so titles stay loose, with variants such as member of technical staff, research engineer, and software engineer. One posted research engineering role listed a base salary range of roughly $225K to $550K depending on experience, plus equity, 401(k) matching, and benefits, which sets a rough anchor for senior technical pay. Total compensation at a well-funded frontier lab leans heavily on equity, and early-stage equity is a bet: the upside can be large if the company succeeds, but it is illiquid, and Magic’s reported revenue is small relative to its valuation, so weigh the paper number against real risk. For current figures, check levels.fyi and recent Blind threads, and ask the recruiter directly about base, equity value at the current preferred price, vesting, and refresh policy. Do not anchor on a single headline number.

Prep priorities

Read Magic’s own blog posts on 100-million-token context and its cost claims, and be ready to reason about why long context is hard and where it breaks. Refresh transformer internals, attention complexity, KV caching, and the common long-context and efficient-attention approaches. For infrastructure roles, review distributed training (data, tensor, and pipeline parallelism), GPU memory and networking, checkpointing, and recovery on long jobs. Sharpen algorithmic coding on realistic tasks involving large data and streaming. Have two or three projects you can explain end to end, including the tradeoffs you made and what you would change now. Because the process is thin and one review called it uneven, confirm the format, timeline, and how each stage is judged before you invest hours in a take-home.

Frequently Asked Questions

Is Magic hard to interview at? The bar for ML research and systems knowledge is high, but the pipeline itself appears short and is lightly documented, so preparation matters more than surviving many rounds. Treat the take-home as the main filter and confirm how it will be judged.

What does Magic actually build? Frontier code models with very long context windows, aimed at automating software work, plus the training and inference infrastructure behind them. It is a research lab first, not a polished consumer product, and it stays secretive about details.

How much does Magic pay? A posted research engineering role showed roughly $225K to $550K base plus equity, so senior pay is competitive with other frontier labs. Confirm live numbers on levels.fyi and with the recruiter, and read the equity terms closely.

Do I need a PhD to work at Magic? No. Research roles want strong knowledge of the deep learning literature and hands-on LLM experience, and engineering roles want strong distributed systems skills. Demonstrated ability tends to matter more than the specific credential.