18/06/2024 Machine Learning Engineer
2 weeks ago
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Be a part of building the future of tax technology
We are seeking a top-performing, versatile, and passionate Machine Learning Engineer with over 6 years of experience in machine learning for an open position in our Content Engineering department, where we curate all the tax content for various Avalara product suites.
The ideal candidate will have solid expertise in Machine Learning model development, MLOps, cloud technologies (especially AWS), API development and CI/CD.
As an ML Engineer specializing in Natural Language Processing (NLP) and Named Entity Recognition (NER), you will play a pivotal role in developing and implementing intelligent automation solutions that leverage machine learning, artificial intelligence, and NLP techniques.
Additionally, as a gracious presenter and communicator, you should be capable of building strong relationships with peers, partners, and leadership.
This role provides an enriching environment for professional development, collaborative innovation, and significant contributions to the organization's success.About the Content Platform:
Avalara's Content Platform is the foundation of our tax compliance solutions. It plays a critical role in ensuring accurate and up-to-date tax content is delivered to our clients. As a Technical Lead, you will have the chance to directly influence this vital platform and our clients' success.
Key Responsibilities:
- Design, develop, and implement AI/ML algorithms and models, particularly focusing on NLP and NER techniques, to solve complex tax compliance challenges related to textual data (e.g., legal text, contracts, invoices, regulations). In this role, you'll be instrumental in developing solutions for tasks like relevance detection and entity extraction, which are crucial for understanding the meaning and intent within various documents.
- Apply NLP and NER techniques to develop and improve chatbots that can effectively interact with users, answer their questions, and provide them with the information they need in a clear and concise manner.
- Collaborate with data scientists, software engineers, and product managers to define and execute AI/ML initiatives involving NLP and NER. This cross-functional collaboration will ensure the accuracy, performance, and real-world applicability of your NLP solutions.
- Develop and maintain robust data pipelines to ensure the quality and integrity of textual training and inference data. You will play a vital role in data pre-processing and optimization, like how we prepare data for NLP models.
- Train, evaluate, and deploy NLP and NER models in production environments. Your expertise in interpreting test results and performance metrics will be essential for continuous improvement.
- Monitor model performance and identify opportunities for improvement in NLP and NER tasks. This focus on continuous improvement aligns with our commitment to delivering high-performing NLP solutions.
- Stay up to date on the latest advancements in NLP, NER, AI, and ML and explore new applications for these technologies within Avalara's product suite. Your drive for innovation will be key to developing cutting-edge NLP solutions that address critical business needs.
Qualifications:
- Bachelor's/master's degree in computer science with 6+ years of industry experience in software development, along with experience developing and deploying machine learning models, particularly NLP and NER models, in production environments.
- Knowledge of Machine Learning(ML), Deep Learning(DL), Natural Language Processing(NLP) libraries (like spaCy, NLTK), Neural Networks
- Experience in atleast one of Pytorch, Tensorflow, Keras, Scikit-Learn and knowledge in DistilBert, BERT, Computer Vision, YOLO
- Experience working in Python
- Knowledge of GenAI, Large Language Models(LLMs)
- Proficiency working in cloud computing environments (AWS, GCP, Azure), machine learning frameworks, and software development best practices.
- Demonstrated ability to keep up with technological advancements and apply them in a fast-paced work environment.
- Familiarity with algorithms, and data structures.
- Good analytical, design and debugging skills.
- Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques
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