# Reka AI Interview Guide (2026): Multimodal AI Models

Source: https://www.techinterview.org/companies/reka-ai-interview-guide/
Updated: 2026-07-12 · techinterview.org

**TL;DR —** Reka AI interviews focus on your ability to build and reason about multimodal models that process text, images, audio, and video together, with heavy weight on transformer architectures, large-scale training, and real model behavior rather than memorized theory. Expect role-specific rounds — coding or research depth depending on whether you're targeting ML engineering, research science, or infrastructure — alongside a deep walk-through of your past projects and an open-ended multimodal problem to reason through. You'll stand out by explaining the trade-offs behind your own work and thinking out loud through practical modeling decisions instead of reciting textbook answers.

Reka AI builds frontier multimodal models — combining text, image, audio, and video reasoning. Founded by ex-Google DeepMind, Meta, and Baidu researchers (Yi Tay and team). Series B in 2024. The interview emphasizes deep ML research engineering, multimodal architectures, and the unique tradeoffs of training cross-modal frontier systems.

## Process

Recruiter screen → 60-minute [coding](/algorithm-patterns-cheat-sheet/) (Python with PyTorch fluency) → onsite virtual: 2 coding/ML, 1 [ML system design](/category/system-design/), 1 research deep-dive, 1 [behavioral](/post/3233460379/behavioral-interview-questions-2026-star-method-amazon-leadership-principles-and-winning-answers/). Research-track candidates get a paper-discussion round. Cycle: 4–6 weeks.

## What they actually ask

- Design a multimodal training pipeline (data ingestion, modality alignment). Focus on how raw image, audio, and video get encoded or tokenized, how you align modalities into a shared embedding space, and how you handle imbalanced or noisy cross-modal data. Interviewers probe data throughput, deduplication, and keeping long runs reproducible at scale.

- Design an inference platform serving multimodal queries. Expect questions on batching mixed-modality requests, KV-cache management, and serving large vision encoders under a latency budget. Be ready to reason about GPU memory limits and how one query touching both image and text changes your throughput math.

- Discuss tradeoffs between encoder-only vs decoder-only multimodal architectures. Know why decoder-only stacks that project vision tokens into the LLM dominate recent frontier models, and where separate encoders with cross-attention still win. Be concrete about training cost, the ease of adding new modalities, and inference latency.

- Coding: [medium-hard DSA](/problems-by-difficulty/), often ML-flavored. Expect array, string, and graph problems, sometimes dressed up as tensor reshaping, tokenization, or sampling logic. State your [time and space complexity](/big-o-cheat-sheet/) as you go and write clean, PyTorch-adjacent Python.

- Behavioral: ownership, taste, fast-moving research-engineering culture. They want examples where you shipped something end-to-end with little direction and made a sharp call under ambiguity. Prepare stories that show research judgment — why you dropped one approach for another — not just raw execution.

## Levels and comp (2026)

- SE: $200K–$280K total (Singapore HQ; US offers higher)

- Senior SE / Research Eng: $290K–$400K total

- Staff / Senior Researcher: $410K–$600K+ total at top of band

## Prep priorities

- Be fluent in Python and PyTorch deeply. You should write idiomatic tensor code without an IDE, debug shape mismatches fast, and know autograd, custom modules, and mixed-precision training cold.

- Understand multimodal architectures (vision encoders, cross-attention, late fusion). Be able to sketch how a CLIP-style encoder, a projection layer, and cross-attention wire a vision tower into an LLM, and when early versus late fusion matters.

- Brush up on distributed training, sequence parallelism, and inference optimization. Know the difference between data, tensor, and pipeline parallelism, what sequence parallelism buys you on long contexts, and inference tricks like KV-cache reuse, quantization, and continuous batching.

## Frequently Asked Questions

### Is Reka remote-friendly?

Hubs in Singapore (HQ) and remote across US/EU/APAC. Many roles distributed.

### How does Reka compare to OpenAI or Google DeepMind on multimodal?

Reka punches above its weight on multimodal, particularly for an independent lab. [Comp](/total-comp-calculator/) below US-frontier-lab top of band but competitive for Singapore-based ML roles.

### What is the engineering culture?

Research-engineering blended; international team across multiple time zones. Strong async culture and high research bar.