Sanas.ai 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 transformation platform capable of accent translation, noise elimination, speech enhancement, and cross-language communication.

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.

Since going to market in 2023, Sanas has scaled at an extraordinary pace, growing from $0 to $32M ARR in under two years, with a projected >$50M ARR by the end of 2025. The company recently recorded its first $10M quarter and is on track to achieve $120M in ARR next year. With a SaaS-based model, Sanas serves some of the world's largest enterprises, including Comcast, UPS, UHG. Today, Sanas technology is deployed across >17 of the Fortune 500 and continuing to accelerate growth.

The company's valuation has a clear trajectory toward multi-billion-dollar market capitalization as it continues to expand into new verticals and product categories. With a TAM that spans all human in the loop communications and beyond, Sanas has the potential to impact every industry and every global interaction.

Sanas is revolutionizing the way we communicate with the world's first real-time algorithm, designed to modulate accents, eliminate background noises, and magnify speech clarity. Pioneered by seasoned startup founders with a proven track record of creating and steering multiple unicorn companies, our groundbreaking GDP-shifting technology sets a gold standard.

Sanas is a 200-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.

About the role

Were 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.

Youll 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. Youll 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.

The pay range for this role is:
250,000 - 350,000 USD per year(United States)

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Sanas

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