Staff ML Engineer

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 have 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
We're looking for an experienced and forward-thinking Staff ML Engineer to lead the design and implementation of our end-to-end data infrastructure for industry leading Voice AI products. This is a high impact role where you will help shape the technical vision, strategic architecture decisions, and deliver reliable and scalable data systems for Machine Learning at scale
You'll work cross-functionally with AI research scientists, Infrastructure and product teams to ensure that data - from raw audio to training-ready features - is consistently accessible, compliant and optimized for speed and scale. You'll help push the boundaries of real-time Voice AI Key Responsibilities: Architect and lead the development of large scale data pipelines and data lakes to ingest, transform and serve high quality data for AI model training,product telemetry and analytics. Drive long‑term data infrastructure strategy across streaming and batch,feature store extensions, Iceberg/Delta lake choices, metadata management, and lakehouse evolution. Drive platform and infrastructure decisions, optimizing compute fleets (e.g.Ray, Spark clusters), orchestration tooling Airflow, Dagster), and streaming stacks Kafka, Flink). Collaborate with AI research scientists, engineering leads, product, finance,marketing, and legal to align data architecture with business and regulatory requirements. Advocate best practices in data governance, lineage, observability, testing,tooling, and disaster recovery across pipelines and data stores. Act as a mentor and technical leader - review design and code, share patterns, elevate team capability, and support recruitment and hiring. Drive build vs buy decisions for tools to implement data quality and observability solutions to achieve high data quality. Qualifications: 7+ years of experience in Data Engineering, Infrastructure, or ML SystemsExpertise in building distributed batch and real-time data systems Expertise in Databases (like Postgres) and Data Lakes (like Snowflake,Databricks and ClickHouse) Experience using Data Processing frameworks like Spark, Flink and Ray Deep Experience with cloud platforms AWS/GCP, object storage (e.g., S3,and orchestrators like Airflow and Dagster Strong knowledge of data lifecycle management, including privacy, security,compliance and reproducibility. Comfortable working in a fast-paced startup environment Strategic mindset and proven ability to collaborate across engineering, ML and product teams to deliver infrastructure that scales with the business. 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. Joining us means contributing to the world's first real-time speech understanding platform revolutionizing Contact Centers and Enterprises alike

Our technology empowers agents, transforms customer experiences, and drives measurable growth. But this is just the beginning. You'll be part of a team exploring the vast potential of an increasingly sonic future

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