Junior AI Researcher

Member of Technical Staff: AI Research & Engineering in Media Integrity

About Synhawk

Synhawk builds omnimodal foundation models for communication integrity, aimed at infrastructure-side deployment in telco and banking sectors. Our platform analyzes the integrity of audio and video, and protects platforms against AI threats. This currently includes detecting synthetic speech & voice cloning, video & image manipulation, social engineering, and identity impersonation. We’re building our models in a way that generalizes to future AI threats yet to come, especially as AI agents get integrated into the workforce and society.

We're past the initial research phase. We're actively deploying with major telco customers, working with and building our own GPU clusters, air-gapped environments, and strict production SLAs. Your work with us will have an immediate, measurable impact on systems that defend real communications infrastructure.

We're a small, highly technical, founder-led team. You'll play an integral part in shaping our research agenda while building foundation models to help real customers from day one.

The Role

You'll own and advance Synhawk's core engine - spanning the development of foundation model and media integrity methods in a full research-to-production pipeline. This isn't a pure research role, as the models and methods you build will directly feed into production with our telco partners. That is to say, you’ll be working under real latency and reliability constraints. But no pressure.

While practical, real-world applicability grounds how we build, we also invest deeply in forward-looking research into emerging AI communication threats. In practice, we balance both: staying focused on protecting customers today while preparing for threats that don’t yet exist.

You'll have direct influence over our research agenda from day one. As we scale beyond early deployments, this role also provides opportunities to publish novel research and engage with the broader community through conferences and events.

What You'll Work On

Foundation Models

• Running and scaling training experiments on multi-GPU setups

• Data curation: filtering, deduplication, and building training data mixtures

• Fine-tuning and post-training (SFT, preference optimization), and evaluating how models behave under distribution shift

• Multimodal data augmentation strategies

Media Integrity & Deepfake Detection

• Implementing and testing detection architectures for audio, video, and images

• Signal processing: codecs, compression artifacts, forensic fingerprints

• Adversarial robustness: testing models against attacks, re-compression, and downsampling

• Hands-on red teaming with open-source and proprietary deepfake generators, integrated into the R&D cycle

Who We're Looking For

You're finishing a Master's or PhD, or recently graduated, with strong foundations in machine learning and a real interest in audio, video, or media forensics. You don't need years of industry experience, but we'd love to see something that shows how you work: a thesis, a paper, open-source contributions, a research internship, or side projects you're proud of.

You're comfortable in Python and PyTorch, you've trained models yourself, and you enjoy the messy parts too - data, debugging, and figuring out why an experiment didn't work.

Most importantly, though, we're looking for folks who are curious and purpose-driven. Even if you don't meet all of the criteria above, but are eager to become an expert in them, we want to hear from you!

What We Offer

• Hands-on work on core AI research at an early-stage company with real traction

• Mentorship from experienced researchers and room to grow fast

• H100 GPU clusters and serious compute

• Competitive comp

• Small, founder-led team - minimal process, maximum trust