About the Role

Sanas is building a full Speech AI suite, all working together as one platform. As that surface area grows, so does the need for a single, rigorous owner of how we know everything is working.

As Evaluations Lead, you'll design the evaluation frameworks and benchmarking systems that answer that question — sitting at the intersection of research, product, and infrastructure to build the metrics, systems, and studies that hold our models accountable. This role suits someone who pairs scientific rigor with real technical execution. Your work will shape how Sanas builds and evaluates its models across every one of these products, making sure progress is measured not just by static benchmarks, but by the harder, more meaningful qualities — understanding, naturalness, and adaptability in real-world interaction.

Your Impact

• Identify and define the model capabilities and behaviors that actually matter for evaluation — not just what's easy to measure
• Build and ship evaluation pipelines with robust statistical analysis and clear, actionable reporting
• Partner directly with model training and research teams to embed evaluation into the development loop itself
• Prototype new user studies and behavioral experiments that ground evaluation in how these models actually get used

What You Bring

• Experience designing or implementing evaluation frameworks for generative models — audio, text, or multimodal
• Strong technical and analytical skills, with the ability to take an open-ended research idea and turn it into a production-ready system
• Creativity in defining novel, quantitative metrics for qualities that are inherently subjective
• Genuine excitement for building evaluation systems that bridge research and real-world use
• Equal parts curious and rigorous — driven by actually figuring out how to measure meaningful progress, not just reporting a number
• The ability to build it yourself. This is an engineering role — you'll be writing the pipelines and tooling, not just specifying them

Nice-to-Haves

• AI modeling experience — someone who has trained, fine-tuned, or shipped models themselves brings a level of judgment to evaluation design that's hard to substitute, and is highly valued for this role
• Background in audio modeling — Speech-to-Text, Text-to-Speech, or similar
• Multilingual — especially relevant for evaluating multilingual systems, where understanding the semantic nuance across languages, not just the literal accuracy, is core to getting evaluation right