The Senior Applied Scientist will research and engineer NLP solutions, design algorithms, train LLMs, and contribute to platform development.
About Us
Black Ore is building the leading AI platform for financial services. By combining LLMs, proprietary AI/ML and automation we accelerate core workflows for the industry, allow financial services professionals to be more productive and enable consumers to enhance their personal finance. Our flagship product, Tax Autopilot, combines AI with federal and state tax codes & regulations to simplify the tax preparation and review process for Certified Public Accountants (CPAs) and accounting firms.
Founded in 2022, we launched with $60 million in early stage funding from some of the world’s leading investors including a16z, Founders Fund, General Catalyst, Khosla Ventures, Oak HC/FT, Trust Ventures and leading tech founders/angel investors including Jason Gardner (Founder and CEO of Marqeta), Max Levchin (Founder of Paypal and Affirm), Tom Glocer (Former CEO of Thomson Reuters), Gokul Rajaram, and Mark Britto (EVP, CPO, PayPal).
Our team has an incredibly ambitious vision to completely transform the way businesses and consumers interact in financial services. We’re looking to hire strong team members to grow the team. Some of the traits we look for are:
- Owner Mentality - Desire to take initiative, identify problems and implement solutions
- Mission Driven - Passion for building AI/ML solutions that reimagine how businesses and consumers operate
- Intellectually Curious - Excitement going deep and building detailed understanding of the function, role, customer and problem space
- Team Oriented - Ability to collaborate respectfully and put the team above the self
The Role
We are seeking a skilled and driven Senior Applied Scientist with 7+ years of industry experience to join our team. You’ll play a key role in building and deploying machine learning models and AI systems that are reliable, scalable, and impactful. The ideal candidate has experience applying NLP techniques in production environments and thrives in a fast-paced, collaborative setting.
Responsibilities
- Research and engineer NLP solutions to solve real-world problems within the tax industry and push state of the art LLMs and assistants.
- Independently design and implement algorithms, train state of the art large language models (LLM) on large data, and evaluate their performance.
- Drive engineering and science that can be applied to Black Ore platform development
- Fine tune models
Basic Qualifications
- Masters degree in Computer Science, Computer Engineering, Artificial Intelligence or relevant technical field, or equivalent practical experience. PhD preferred but not required.
- Applied Research experience in one or more of these areas: NLP, NLU, machine learning, deep learning, or related fields.
- Direct experience in Summarization, Classification and/or Extraction
- Experience with NER (named-entity recognition)
- End to end experience delivering production-ready code
- 3+ years of experience with development and implementation of LLM algorithm/systems and model training.
- Direct experience in generative AI and LLM's, and implementing solutions to production
- Experience working with machine learning libraries like Pytorch.
- Familiar with scripting languages such as Python and shell scripts.
- Direct experience in Prompt Engineering
- Continued interest in LLM trends and the latest in cutting edge models within AI
- Proficient in Python and ML libraries such as TensorFlow, PyTorch, scikit-learn, or similar
- Strong understanding of ML fundamentals, including supervised/unsupervised learning, model evaluation, feature engineering, and overfitting/underfitting
- Experience working with large datasets and building production-ready data pipelines
What We Offer (for U.S. Based Employees)
- Competitive salary and equity based compensation
- Employer-paid medical, dental and vision insurance
- Ability to define your own success
- Continuous learning and new challenges to master
Black Ore Austin, Texas, USA Office
Austin, TX, United States
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