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

Weʼre looking for an experienced and forward-thinking Principal Machine Learning Engineer to lead the design and implementation of our end-to-end Machine Learning infrastructure for industry leading Voice AI products. This is a high impact role where you will shape the technical vision, own strategic architecture decisions, and mentor a growing team of Machine Learning engineers focused on delivering reliable and scalable Machine Learning training and inference systems.

Youʼll work cross-functionally with AI research scientists, Infrastructure and product teams to ensure that Machine Learning infrastructure is designed and built for accelerating innovation through increased experimentation and deployment velocity. Youʼll help push the boundaries of real-time Voice AI

What you'll do

• Architect robust, modular ML pipelines for model experimentation, feature extraction, and production inference
• Collaborate with data engineering to improve audio dataset quality, labeling pipelines, and feature engineering
• Mentor and collaborate with other ML engineers and research scientists to ensure best practices in model development, evaluation, and deployment.
• Optimize models for latency, memory, and real-time performance on CPU/GPU/edge hardware.
• Introduce frameworks for continual learning, model versioning, and A/B testing in production.
• Stay current with advancements in Voice AI, Deep learning and multimodal model architectures

Qualifications

• 10+ years of experience in Machine Learning Systems, ML workflows with atleast 3+ years in a technical leadership capacity
• Advanced proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX
• Strong understanding of Deep learning architectures like RNNs, LSTMs, CNNs,Transformers, CTC and their application in Accent translation, Noise cancellation, Acoustic Modeling, Language Modeling and Language Translation
• Experience deploying ML models to production (e.g., via ONNX, TensorRT, TorchScript, or custom inference stacks)

Nice to Have:

• Familiarity with audio data and its unique challenges, like large file sizes, time- series features, metadata handling, is a strong plus.
• Experience with Voice AI models like ASR, TTS and speaker verification.
• Familiarity with real-time data processing frameworks like Kafka, Flink, Druid and Pinot
• Familiarity with ML workflows including: MLOps, feature engineering, model training and inference.
• Experience with labeling tools, audio annotation platforms, or human-in-the- loop annotation pipelines.
• Experience at a high-growth startup or tech company operating at scale.
• Deep experience with ML tooling for training and serving models, ideally in audio or speech domains (e.g., PyTorch, ONNX, Hugging Face Transformers, torchaudio).
• Experience deploying real-time ASR, TTS, or voice synthesis models in production.
• Background in DSP, audio augmentation, or working with noisy or multilingual datasets.