Machine Learning Research Engineer

About us

Skit (previously known as http://vernacular.ai/" target="_blank">Vernacular.ai) is an AI-first SaaS voice automation company. Its suite of speech and language solutions enable enterprises to automate their contact centre operations. With over 10 million hours of training data, its product - Vernacular Intelligent Voice Assistant (VIVA) can currently respond in 16+ languages, covering over 160+ dialects and replicating human-like conversations.

Skit currently serves a variety of enterprise clients across diverse sectors such as BFSI, F&B, Hospitality, Consumer Electronics and Travel & Tourism, including prominent clients like Axis Bank, Hathway, Porter and Barbeque Nation. It has been featured as one of the top-notch start-ups in the Cisco Launchpad’s Cohort 6 and is a part of the World Economic Forum’s Global Innovators Community. It has has also been listed in Forbes 30 Under 30 Asia start-ups 2021 for its remarkable industry innovation.

We are looking for ML Research Engineers to work on the following problems:

• Spoken Language Understanding and Dialog Management.
• Language semantics, parsing, and modeling across multiple languages.
• Speech Recognition, Speech Analytics and Voice Processing across multiple languages.
• Response Generation and Speech Synthesis.
• Active Learning, Monitoring and Observability mechanisms for deployments.

Responsibilities

• Design, build and evaluate Machine Learning solutions.
• Perform experiments and statistical analyses to draw conclusions and take modeling decisions.
• Study, implement and extend state of the art systems.
• Take part in regular research reviews and discussions.
• Build, maintain and extend our open source solutions in the domain.
• Write well-crafted programs at all levels of the system. This includes the data pipelines, experiment prototypes, fast and scalable deployment models, and evaluation, visualization and monitoring systems.

Requirements

• Practical Machine Learning experience as demonstrated by earlier works.
• Knowledge of and ability to use tools from theoretical and practical aspects of computer science. This includes, but is not limited to, probability, statistics, learning theory, algorithms, software architecture, programming languages, etc.
• Good programming skills and ability to work with programs at all levels of a finished Machine Learning product. We prefer language agnosticism since that exemplifies this point.
• Git portfolios and blogs are helpful as they let us better evaluate your work.

• What information we collect during our application and recruitment process and why we collect it;
• How we use that information; and
• How to access and update that information.

• Your name, address, email address, telephone number and other contact information;
• Your resume or CV, cover letter, previous and/or relevant work experience or other experience, education, transcripts, or other information you provide to us in support of an application and/or the application and recruitment process;
• Information from interviews and phone-screenings you may have, if any;
• Details of the type of employment you are or may be looking for, current and/or desired salary and other terms relating to compensation and benefits packages, willingness to relocate, or other job preferences;
• Details of how you heard about the position you are applying for;
• Reference information and/or information received from background checks (where applicable), including information provided by third parties;
• Information about your educational and professional background from publicly available sources, including online, that we believe is relevant to your application or a potential future application (e.g. your LinkedIn profile); and/or
• Information related to any assessment you may take as part of the interview screening process.

• Assessing your skills, qualifications and interests against our career opportunities;
• Verifying your information and carrying out reference checks and/or conducting background checks (where applicable) if you are offered a job;
• Communications with you about the recruitment process and/or your application(s), including, in appropriate cases, informing you of other potential career opportunities at Skit;
• Creating and/or submitting reports as required under any local laws and/or regulations, where applicable;
• Making improvements to Skit's application and/or recruitment process including improving diversity in recruitment practices;
• Proactively conducting research about your educational and professional background and skills and contacting you if we think you would be suitable for a role with us.