Job Description

Position Overview

• Location Type: Hybrid (Silicon Valley & Bangalore, India)
• Employment Type: Full-Time
• Salary: (Salary information not provided in the text)

Ema is building the next generation AI technology to empower every employee in the enterprise to be their most creative and productive. Our proprietary tech allows enterprises to delegate most repetitive tasks to Ema, the AI employee. We are founded by ex-Google, Coinbase, Okta executives and serial entrepreneurs. We’ve raised capital from notable investors such as Accel Partners, Naspers, Section32 and a host of prominent Silicon Valley Angels including Sheryl Sandberg (Facebook/Google), Divesh Makan (Iconiq Capital), Jerry Yang (Yahoo), Dustin Moskovitz (Facebook/Asana), David Baszucki (Roblox CEO) and Gokul Rajaram (Doordash, Square, Google). Our team is a powerhouse of talent, comprising engineers from leading tech companies like Google, Microsoft Research, Facebook, Square/Block, and Coinbase. All our team members hail from top-tier educational institutions such as Stanford, MIT, UC Berkeley, CMU and Indian Institute of Technology. We’re well funded by the top investors and angels in the world. Ema is based in Silicon Valley and Bangalore, India. This will be a hybrid role where we expect employees to work from office three days a week.

Requirements

• Passionate about solving complex problems.
• Experience with large datasets.
• Strong team player and autonomous worker.
• Knowledge of machine learning techniques (Transformer-based models, reinforcement learning, ensemble learning, agent-based systems).
• Experience with Natural Language Processing, Information Retrieval, and related Machine Learning technologies.
• Strong understanding of the entire ML model development lifecycle (problem definition, data exploration, feature engineering, model training, validation, and deployment).

Responsibilities

• Conceptualize, develop, and deploy machine learning models.
• Implement advanced machine learning algorithms.
• Lead the processing and analysis of large, complex datasets.
• Conduct A/B testing and statistical analysis to validate model effectiveness.
• Ensure the integrity and robustness of ML solutions through automated testing.
• Communicate technical aspects and benefits of ML models to stakeholders.