Machine Learning Engineer - Document Intelligence & Applied GenAI
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Requirements
• Skills needed: Experience with machine learning algorithms and natural language processing. Familiarity with Python programming is essential as it's the primary development language used at PandaDoc for AI projects. Knowledge of TensorFlow/Keras, PyTenzier, or similar libraries would be beneficial but not mandatory. • Years of experience: 3+ years in a related field such as machine learning engineering with an emphasis on document intelligence and generative AI applications is preferred. Experience working within the Document Intelligence industry can provide additional advantages. • Education: Bachelor's degree or higher, ideally in Computer Science, Data Analytics, Statistics, Mathematics, Engineering (preferably related to machine learning), Information Technology Management, Business Administration with a focus on technology management is required. A Master’s degree and/or relevant certifications such as Certified Machine Learning Engineer are preferred but not mandatory for this role. • Must-haves: Strong problem-solving skills in the context of machine learning projects; ability to work independently or within a team setting, with an emphasis on collaboration when necessary is required. Excellent communication and interpersonal skills needed as you will be working closely with other teams across PandaDoc's business units are essential for success in this role. • Certifications: While not mandatory, having certifications such as AWS Solutions Architect or Microsoft Azure AI & Machine Learning can provide a competitive edge and demonstrate expertise to potential employers.
Responsibilities
• Develop and maintain machine learning models to improve document processing efficiency. • Collaborate with cross-functional teams to integrate AI solutions into existing workflows at PandaDoc. • Monitor model performance metrics and make necessary adjustments for continuous improvement. • Stay updated on the latest advancements in natural language understanding (NLU) technologies relevant to document intelligence applications. • Participate in code reviews, contribute to open source projects related to Applied GenAI when possible. • Provide technical support and guidance to team members unfamiliar with machine learning concepts or tools used within the organization.
Benefits
• Salary: Explicitly stated as part of the benefits. • Equity: Mentioned in the provided text indicating that equity is offered to eligible employees and their families after a probation period. This benefit includes stock options, restricted shares, or other forms of ownership interest within PandaDoc's company structure which can be valuable as they may appreciate over time if the company grows significantly. • Paid Time Off (PTO): The posting mentions that eligible employees receive 14 days paid vacation annually after a probation period and an additional two weeks of annual leave for all full-time PandaDoc staff, which can be used as sick or personal time if needed without the need to use accrued vacation. • Insurance: The posting states that eligible employees receive comprehensive health insurance coverage with a company contribution and dental/vision plans available at an additional cost for full-time PandaDoc staff, which provides financial protection against medical expenses. • Remote Work Options: The posting indicates the possibility for remote work, suggesting that employees may have some flexibility in their working location as part of their employment benefits package at PandaDoc.