Research Scientist (Model Evaluation)

Sanas is pioneering the future of human communication. Founded by a team of Stanford researchers and entrepreneurs with deep industry experience, Sanas has developed the world's first real-time speech AI platform capable of accent translation, noise cancellation, speech enhancement, cross-language communication, and more.

Sanas makes conversations clearer, more inclusive, and more effective, removing barriers that prevent people from being understood, regardless of accent, background noise, or native language.

Sanas is currently one of the fastest growing startups in Silicon Valley, growing from $16M to $50M ARR in 2025. The company's core business is profitable and is on track to end 2026 with >$120M ARR. Our team combines deep expertise in model innovation and systems engineering with a design-minded product engineering culture to build and ship cutting-edge AI models and experiences — entirely in-house.

Sanas is a 180-strong team, established in 2020. In this short span, we've successfully secured over $100 million in funding. Our innovation has been supported by the industry's leading investors, including Insight Partners, Google Ventures, Quadrille Capital, General Catalyst, Quiet Capital, and other influential investors. Our reputation is further solidified by collaborations with numerous Fortune 100 companies. With Sanas, you're not just adopting a product; you're investing in the future of communication.

If you’re looking to have a significant role in roadmapping and driving technical directions, if you’re looking to deploy challenging and big ideas without much overhead or slowness, if you're looking to leave your mark on an ambitious, generational mission to change how the worlds thinks about speech + AI, then Sanas is a well-suited place for you.

About the Role

Progress in speech AI is only as meaningful as our ability to measure it. At Sanas, model quality spans dimensions that automated metrics struggle to capture — accent naturalness, perceptual clarity, speaker identity preservation, noise suppression without speech distortion, translation fluency under real-world disfluency. We're looking for a Research Scientist who can define what "better" actually means across all of Sanas's model families, build the evaluation infrastructure to measure it rigorously, and close the loop between research progress and real-world impact. This role sits at the intersection of research, product, and infrastructure — and directly shapes how every model team at Sanas measures progress.

Job Description

Evaluation framework design

• Design and own evaluation frameworks across Sanas's full model portfolio — Accent Translation, Noise Cancellation, Speech Enhancement, and Language Translation, and more — ensuring each captures meaningful progress, not just benchmark performance.
• Develop novel quantitative metrics for subjective and perceptual qualities: accent similarity, naturalness, speaker identity preservation, intelligibility under noise, and translation fluency in spoken-language domains.
• Build evaluation systems that bridge automated metrics and human judgment — designing listening studies, MOS/MUSHRA protocols, and preference tests that are statistically rigorous and operationally scalable.
• Define evaluation splits, test sets, and benchmark suites that accurately reflect production conditions — diverse accents, languages, noise environments, recording devices, and telephony codecs.

Evaluation infrastructure & tooling

• Build and maintain automated evaluation pipelines that run continuously against model checkpoints — surfacing regressions early and tracking quality trends across training runs.
• Develop reference-based and reference-free metrics calibrated to Sanas's specific model tasks: SI-SDR, PESQ, STOI, DNSMOS, speaker similarity, WER delta, COMET, and task-specific custom metrics where off-the-shelf measures fall short.
• Instrument model quality monitoring in production — detecting degradation across language pairs, accent profiles, and acoustic conditions in live customer traffic.
• Build tooling that allows research scientists and ML engineers to run rigorous ablations, compare model versions, and understand quality tradeoffs without needing to design the evaluation from scratch each time.

Human evaluation & research

• Design and operate human evaluation programs — listener panels, crowdsourced annotation, and expert evaluator workflows — that produce reliable signal on dimensions automated metrics cannot capture.
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