Principal Machine Learning Engineer (AI/ML) - Splore

** of Company: What is the Company About?**

Splore, supported by Temasek and Menyala, is a visionary startup dedicated to revolutionizing how individuals access and engage with information. In a digital landscape flooded with data, the emphasis has shifted from acquiring information to swiftly and effectively pinpointing pertinent insights. Utilizing an AI Answer Engine, Splore delivers direct, context-sensitive answers, revolutionizing how information is unearthed and absorbed.

Splore offers businesses the opportunity to unlock the considerable value of their data assets by employing the company's exclusive Knowledge Index. This enables businesses to expedite decision-making processes with a foundation of deeper insights. Simultaneously, consumers benefit from swift, pertinent, and dependable answers, eliminating the frustration of sifting through irrelevant search outcomes.

Engineered by skilled professionals in machine learning, distributed systems, and consumer applications, Splore.com merges web data with proprietary sources to generate customized and diverse information outputs.

Key Responsibilities

The ideal candidate will lead the ML/AI team as a Principal Machine Learning Engineer, overseeing the entire ML lifecycle from feature development to operationalization and deployment. Serve as the primary ML authority for the CEO and Product Managers, steering advancements in core ML capabilities and improving engineering processes for widespread adoption in target markets. Leading a group of 4-6 AI software engineers, the role involves cultivating a culture that encompasses both AI innovation and ML product implementation. Additionally, this position entails fostering the professional growth, guidance, and mentorship of team members.

Role Specifics:

• ML System Blueprint and Development: Conceptualize, design, and supervise the implementation of ML systems placing strong emphasis on data pipelines, MLOps for training/inferencing, and measuring service/model efficiency.

• AI Research: Remain current with the latest AI evolutions, focus particularly on generative AI, recognize technologies and models suitable for Splore use scenarios, implement stringent experimentation practices, and drive continuous enhancements to ML features.

• ML Evaluation and Enhancement: Institute a standardized model-evaluation approach emphasizing business impact, enforce appropriate model training/evaluation methods with relevant metrics justifying business value.

• POC Management and Execution: Work closely with Product Management and Sales departments to craft and deploy POC AI solutions, effectively demonstrating and substantiating their influence.

• ML Operations and Expansion: Evaluate business requirements to devise and implement appropriate ML training and inferencing frameworks and pipelines, endorse best practices in ML engineering for training/model optimization, and establish mechanisms for assessing and tracking model performance.

• Technical Leadership and Supervision: Provide technical direction to AI engineers, ensuring high-quality technical output and continuous advancement. Provide career guidance and advancement prospects for engineers at various levels of seniority.

Desirable Attributes

The ideal candidate should possess the following traits:

• Adaptability: Flourishes in evolving environments, making well-informed choices amidst uncertainties.
• Collaboration Skills: Strong teamwork abilities required to align AI strategies with business and technical needs, necessitating effective communication across engineering, product, business, and client teams.
• Learning Agility: Comfortable in a start-up environment, unafraid of hands-on problem-solving and learning through experimentation to tackle new challenges. Active engagement with the latest ML trends and technologies.
• Technical Proficiency: An Engineering/Mathematics MS/PhD graduate with 15+ years' experience in ML/AI (with 10 years specifically in AI/ML and 5 years in DL/NLP). Proficiency in Python and hands-on experience with TensorFlow and PyTorch. Proficient in MLOps practices, cloud platforms like Azure, AWS, or GCP, database systems, and large-scale microservice architecture.

The position can translate business requirements into technical needs, with expertise encompassing software testing, benchmarking, and continuous integration.