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 (Python with PyTorch fluency) → onsite virtual: 2 coding/ML, 1 ML system design, 1 research deep-dive, 1 behavioral. 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)
- Design an inference platform serving multimodal queries
- Discuss tradeoffs between encoder-only vs decoder-only multimodal architectures
- Coding: medium-hard DSA, often ML-flavored
- Behavioral: ownership, taste, fast-moving research-engineering culture
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
- Understand multimodal architectures (vision encoders, cross-attention, late fusion)
- Brush up on distributed training, sequence parallelism, and inference optimization
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 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.