Senior Software Engineer - Machine Learning (NLP Focus)

at  Mitratech

At Mitratech, we are a team of technocrats focused on building world-class products that simplify operations in the Legal, Risk, Compliance, and HR functions of Fortune 100 companies. We are a close-knit, globally dispersed team that thrives in an ecosystem that supports individual excellence and takes pride in its diverse and inclusive work culture centered around great people practices, learning opportunities, and having fun! Our culture is the ideal blend of entrepreneurial spirit and enterprise investment, enabling the chance to move at a rapid pace with some of the most complex, leading-edge technologies available.
Given our continued growth, we always have room for more intellect, energy, and enthusiasm - join our global team and see why it’s so special to be a part of Mitratech!

JOB OVERVIEW

We are seeking a highly skilled Senior Software Engineer specializing in Machine Learning, with a focus on Natural Language Processing (NLP), to join our dynamic team. The ideal candidate will play a pivotal role in understanding business objectives and leveraging machine learning models to meet these goals effectively. This position requires a blend of expertise in data science and software engineering, along with a passion for staying at the forefront of machine learning advancements.

Essential Duties & Responsibilities:

• Model Development: Understand business objectives and develop machine learning models to achieve these goals, complete with performance tracking metrics.
• Data Management: Ensure data quality through rigorous verification and cleaning processes. Explore and visualize data to understand it thoroughly, identifying any distribution differences that could impact model performance in real-world applications.
• Research and Data Acquisition: Proactively find and utilize available datasets online for model training purposes.
• Strategy and Validation: Define and implement robust validation strategies for model evaluation.
• Model Training and Tuning: Take charge of training models and fine-tuning hyperparameters to optimize performance.
• Model Deployment: Skillfully deploy models to production environments, ensuring seamless integration and operational efficiency.
• Industry Awareness: Maintain an up-to-date understanding of the latest developments in the machine learning field, with a keen eye on advancements in NLP.

REQUIREMENTS & SKILLS:

• ML Experimentation: A solid understanding of setting up machine learning experiments, communicating results, and managing stakeholder expectations.
• Data Quality Management: Experience in verifying and ensuring data quality through comprehensive data cleaning processes.
• Model Development: Proven experience in training custom models with available data and conducting rapid experimentation for proof-of-concept projects.
• Technical Proficiency: Hands-on knowledge of at least one major machine learning framework, with a preference for PyTorch.
• MLOps Knowledge: Familiarity with MLOps practices, including model deployment and offering machine learning models as a service, is highly desirable.
• LLMs and Third-Party Services: Understanding of Large Language Models (LLMs) and third-party services, with the ability to evaluate the benefits of using these over in-house model development.

EDUCATION:

• A Master’s degree in Machine Learning, Computer Science with a preference for specialization in the NLP domain.

• Model Development: Understand business objectives and develop machine learning models to achieve these goals, complete with performance tracking metrics.
• Data Management: Ensure data quality through rigorous verification and cleaning processes. Explore and visualize data to understand it thoroughly, identifying any distribution differences that could impact model performance in real-world applications.
• Research and Data Acquisition: Proactively find and utilize available datasets online for model training purposes.
• Strategy and Validation: Define and implement robust validation strategies for model evaluation.
• Model Training and Tuning: Take charge of training models and fine-tuning hyperparameters to optimize performance.
• Model Deployment: Skillfully deploy models to production environments, ensuring seamless integration and operational efficiency.
• Industry Awareness: Maintain an up-to-date understanding of the latest developments in the machine learning field, with a keen eye on advancements in NLP