Senior Machine Learning Engineer (NLP)

Job Responsibility:

Who are we?

Railtown AI Technologies Inc. is a fast-growing innovative technology company committed to developing and using technology to increase developer velocity in the software industry. Leveraging our expertise in software development and AI / machine learning, we are building an AI cloud service that integrates your ticket tracking system (Github, Jira, Azure), CI/CD process and application logs to detect, analyze, and fix application errors quickly and efficiently. At Railtown AI, we value innovation, teamwork, ownership and leadership, and provide you with a fun, dynamic, and rewarding work experience to realize your potential.

About the role

As a Senior Machine Learning Engineer (NLP), you will work with our growing AI team in developing our Application General Intelligence (AGI) solution for problems in the software development domain. This position will provide a mix of research and engineering problems around large language models (LLM) as you will be involved in the development of the whole machine learning pipeline from model training to cloud deployment. This position will provide you the opportunity to develop and roll out innovative products. As a fast-growing start-up, we cultivate an environment that will help you meet your full potential. You'll be encouraged to seize on challenges, learn from our interdisciplinary teams, and grow. This is a hybrid role with 3 days a week in the Vancouver office.

You may be a good fit for this role if you:

• Have strong analytical and problem-solving skills, and willing to dive deep to find creative solutions
• Have deep experience around NLP and building custom LLM solutions
• Have an entrepreneurial spirit and love to work in a fast-growing start-up environment, where you will meet like-minded people and celebrate successes

What you'll do here:

• Brainstorm, design and develop the company's AGI solution
• Working with different types of data including computer logs, errors, natural languages, and other multimodal data
• Work with state of the art large language models
• Document findings, approaches and present the results to the team
• Optimize algorithms and prototypical solutions, such as hyperparameter search, model fine-tuning, etc.
• Collaborate with the engineering team to implement (build, test and deploy) the ML models
• Create unit, integration, end-to-end, and/or performance tests.
• Keep current with technology and industry developments and be on the lookout for new approaches and opportunities to integrate them into the existing solutions

Skills and Qualifications

• BS or MS/PhD degree in computer science, engineering, statistics, applied mathematics, or related quantitative discipline
• Proficiency in programming languages commonly used in NLP such as Python
• Strong knowledge of state-of-the-art pre-trained language models such as BART, Mistral, GPT, etc., along with expertise in fine-tuning these models for specific downstream tasks.
• Experience in designing and implementing custom architectures and adaptations for large language models to optimize performance on specific tasks or domains.
• Familiarity with transfer learning techniques and methodologies for fine-tuning pre-trained language models on diverse datasets.
• Proficient in model evaluation and benchmarking methodologies for fine-tuned language models, including standard NLP evaluation metrics as well as task-specific metrics for tasks such as text generation, text classification, language modeling, etc.
• Strong understanding of memory and computational constraints associated with fine-tuning large language models, along with experience in optimizing model inference and deployment for production environments.
• Basic knowledge of agile software development (e.g. version control, kanban processes, and cloud deployment).
• Team player. Comfort working in a dynamic group with open problems to solve.

? Location Vancouver, BC Department Technology Employment type Full-time employee Minimum experience 3+ years

Ready to join us?