6 month growth↓ -7%
1 year growth↑ 1%
2 year growth↑ 0%
- Significant experience in training, fine-tuning, and maintenance of large-scale LLMs
- Proficiency in Python and C/C++
- Bachelor's degree in Computer Science, Applied Mathematics, or related field
- Proven track record of deploying ML models in production environments
- Familiarity with deep learning architectures such as transformers and GPT
- Architect and construct robust data pipelines for training, fine-tuning, and evaluation of Large Language Models (LLMs)
- Drive optimization efforts to enhance speed and accuracy of LLM inference processes
- Manage and enhance foundational infrastructure supporting Owl.co's LLMs
- Advanced degree (Master's or PhD) in Machine Learning, Computer Science, or related discipline
- Prior experience working with distributed systems and data pipelines
We are not working with recruitment agencies at this time.
Owl.co empowers insurers to combat illegitimate claims on a large scale while eliminating human bias from the process. Our clients are top insurance companies across North America, achieving remarkable results through our AI-powered, evidence-based platform. We are on a mission to integrate state-of-the-art ML and NLP methods to transform this traditionally manual activity into a fair process. We are well-funded and have engineering offices in New York City, Toronto, and Vancouver.
We’re currently seeking a Multimodal AI Engineer (NLP) to play a key role in advancing our core intelligence capabilities. In this position, you’ll collaborate closely with cross-functional teams to design, implement, and optimize systems that are reshaping how insurers detect and handle illegitimate claims.
- Architect and construct robust data pipelines tailored for the training, fine-tuning, and evaluation of Large Language Models (LLMs).
- Drive optimization efforts to enhance both the speed and accuracy of LLM inference processes, ensuring efficient and reliable performance.
- Take ownership of managing and enhancing the foundational infrastructure supporting Owl.co’s LLMs, including model management and orchestration systems.
- Significant experience in the training, fine-tuning, and maintenance of large-scale LLMs, demonstrating a deep understanding of NLP principles and methodologies.
- Proficiency in programming languages such as Python and C/C++, coupled with a strong grasp of systems architecture and optimization techniques.
- Bachelor’s degree in Computer Science, Applied Mathematics, or a related field, providing a solid foundation for tackling complex AI challenges.
- Proven track record of deploying ML models in production environments and actively contributing to the machine learning community through publications or participation in relevant forums.
- Familiarity with state-of-the-art deep learning architectures, such as transformers and GPT, and a willingness to stay updated on emerging trends in the field.
- Advanced degree (Master’s or PhD) in Machine Learning, Computer Science, or a related discipline, showcasing a deeper level of expertise in AI and NLP.
- Prior experience working with distributed systems and data pipelines, enabling seamless integration of AI technologies into large-scale enterprise environments.
Join us at Owl.co and be part of a dynamic team driving innovation in the insurance industry. If you’re passionate about leveraging AI to tackle real-world challenges and eager to make a meaningful impact, we’d love to hear from you.
- Medical: 100% paid medical, dental, and vision coverage
- Pension: 401K matching
- Short & long-term disability
- Recharge: 4 weeks of paid time off, 10 public holidays, additional sick days, and time off over the winter holidays
- Personal development: $1,200/year toward your fitness expenses, favorite activities, or professional development
- Hybrid working environment
- Weekly team lunches in the office
Salary Range: $150,000 - $230,000
Our salary ranges are benchmarked and are determined by role and level. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all Canadian locations and could be higher or lower based on a multitude of factors, including job-related skills, experience, location, and relevant education or training.