Diabolocom enables organizations worldwide to build brand loyalty and drive sales growth. Our innovative cloud-based solution leverages its proprietary AI technologies to optimize contact centers of medium to large sizes, enabling efficient management of customer interactions across all communication channels. As a telecom operator, we ensure premium voice quality, efficient handling of high call volumes, and provide phone numbers worldwide.

Our comprehensive range of AI services, specifically designed for customer relations, elevates data quality and automates tasks. These services, fully integrated into our cloud contact center solutions, are also accessible through APIs in multiple languages.

We empower Customer Service and Sales teams to effectively handle all inbound and outbound interactions thanks to our state-of-the-art dialer. Our solution seamlessly integrates with leading CRMs and offers real-time monitoring and analytics capabilities.

Diabolocom, with offices across Europe, the USA, Brazil, and the UAE, proudly serves 350+ clients, with agents operating from 60+ countries.

About the Project:

Join our team as a Machine Learning Research Engineer and play a key role in the development and maintenance of our AI-based systems. From conceptualizing experiments to implementing innovative ideas, you will be responsible for all an AI/ML project’s life cycle. You will have the opportunity to impact the industry in a meaningful way, by participating in its transformation with AI. More concretely, our projects predominantly focus on text and speech data, emphasizing Natural Language Processing (NLP) and speech processing, including ASR, speech analysis, and denoising systems.

As an ML Research Engineer, your responsibilities encompass a broad spectrum of tasks:

- Conceiving and developing end-to-end AI systems, from data collection and processing to module development, and evaluation methods and metrics.

- Defining and implementing AI systems, creating data pipelines, and incorporating features based on infrastructure and performance requirements.

- Establishing a benchmarking system to compare and select system evolutions using tools like Hydra, Metaflow/MLFlow, Weight&Biases, etc.

- Defining data collection, annotation, and processing protocols, developing processing pipelines, and creating datasets for specific applications.

- Managing data collection and processing for training and testing machine learning-based systems.

- Analyzing data to optimize products, adapting models, reconsidering modalities, and creating new user behavior metrics.

(Optional) Contributing to publications such as blogs, scientific articles, and interviews.

What You Will Use:

- Python

- Machine learning libraries like TensorFlow, PyTorch, ONNX and huggingface's transformers library

- Database technologies like MongoDB and SQL

- MLOps platforms

- Dependency management tools like Docker

- Version control tools: git/github

We Are Looking for Someone With:

Required Skills:

- Excellent proficiency in Python coding.

- Proven experience in implementing machine learning systems.

- Capability to work with popular machine learning frameworks and libraries such as TensorFlow, PyTorch, ONNX, huggingface's transformers library, and MLOps platforms. Proficiency with both TensorFlow and PyTorch is preferred.

Preferred Skills:

- Background in NLP or speech processing (ideally both).

- Strong theoretical foundation.

- Experience with NLP libraries and generative models.

- Familiarity with ASR, speaker recognition, speech synthesis, and speaker diarization.

- Proficiency in Docker for efficient deployment.

- Knowledge of MLOps methodology and implementation.

- Familiarity with database technologies (SQL or NoSQL) for effective data management.

Recruitment process

- Technical assignment

- Call with a Talent Acquisition Manager

- Technical interview with an engineer

- Interview with a manager

At Diabolocom, diversity and inclusion are in our DNA. All qualified applicants will receive equal consideration for employment without regard to color, language, religion, sex, sexual orientation, gender identity, national or social origin, opinion disability, age.