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. Our initial deployment is laser-focused on elevating the standards of customer experience centers. Testimonials from our partners reveal staggering double-digit improvements in mission-critical KPIs, coupled with boosts in CSAT and NPS. More than just a tool, our technology champions a bias-free workspace. This not only fosters a positive work environment but has also been instrumental in reducing employee attrition and curbing training expenditures.

Sanas is a 100+ strong team, established in 2020. In this short span, we’ve successfully secured over $50 million in funding. Our innovation have been supported by the industry’s leading investors, including Insight Partners, Google Ventures, 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.

Sanas is looking for an experienced machine learning operations engineer. We expect you to have a strong background in the practicalities of machine training and operations. You will be helping improve Sanas’s models and ML infrastructure. You will be responsible for running experiments about data and training. You will also be building libraries and implementing novel models in collaboration with data scientists.

Here's what you’ll be doing:

• Optimize training strategies
• Conduct experiments on details of training and data
• Improving in house models:
• Developing and maintaining libraries used in all ML operations
• Implementing novel models
• ETL optimizations
  
  
  
  
  

Must Have:

• Practical and theoretical knowledge of machine learning
• Proven experience in training novel models
• Proven experience in data engineering
• Experience in conducting ML experiments
• Proven experience with ML tools. Like PyTorch, Weights and Biases
• Bachelor's or Master's Degree in Computer Science, Engineering, or a related field
• 5+ years of Industry experience on the above skills
  
  
  
  
  

Preferred qualifications:

• Experience with audio or speech technology
• Experience with large-scale ML systems, cloud infrastructure, and IT
• Experience with DevOps practices such as containerization and CI/CD
• Experience with data engineering, data warehousing, and data processing technologies