Applied ML engineer


  Machine Learning


We are building next gen enterprise AI platform to automate repetitive tasks for everyday business users. A Mckinsey study recently showed that 60% of white collar jobs have at least a third of their work that is automatable. The automation and API integration tools scratched the surface by automating simple rules-based processes but there is still a long way to go to use cutting edge AI to automate more complex tasks. We are building the first self-service AI automation platform for everyday business users. It observes and learns from users’ day to day work and automatically creats the scripts to automate both rule based tasks and more complex tasks that require AI and ML.


We are backed by top-tier VC’s with a track record of backing category defining companies. The founding team has deep knowledge of automation space and deep AI domain expertise, coming from Google and UiPath.


About the Role:

We are looking for an ML lead to own the building of a scalable “auto ML” platform from scratch. This platform will be the foundation of our products for enabling AI powered automations. You will work closely with other stakeholders to:

  • take full ownership of scalable auto ML platform
  • Influence overall product and engineering strategy
  • Brainstorm, innovate and prototype cutting edge ML models and infrastructure.
  • Led the development of all aspects of the Machine Learning (ML) process, including data collection and labeling, building training and validation pipelines, data analysis, and everything else required to develop and launch models into production.
  • Led the design and implementation of auto ML pipelines for various ML tasks, including classification, regression, clustering, recommendation and search.
  • Drive experiments and iterate on refining and improve ML models
  • Collaborate with product engineers to develop and integrate ML models.
  • Ensure models and the overall inference pipelines run efficiently on Cloud and/or browser
  • Adopt standard machine learning methods to best exploit modern parallel environments (e.g., distributed clusters, multicore SMP, and GPU)



  • Great algorithm, data structure, and coding skills
  • Experience with end-to-end ML application development, data engineering, model tuning, model serving etc.
  • Experience with NLP model development (experience with large language model development preferred)
  • Experience developing and debugging in Python, C/C++ or equivalent programing languages.
  • Experience with neural network architecture search
  • Exposure to architecutal patterns of large-scale software applications



  • Python, Tensorflow, Pytorch, NLP, distributed systems, GCP (or other cloud services), Ray (optional)