About Optro
Optro is a leading audit, risk, ESG, and InfoSec platform, having surpassed $300M ARR and experiencing continuous growth. Over 50% of the Fortune 500, including 7 of the Fortune 10, utilize Optro's technology. The company is highly rated on G2.com and Gartner Peer Insights, and has been recognized as one of the 500 fastest-growing tech companies in North America for seven consecutive years by Deloitte. Optro fosters an environment of innovation, customer focus, and mutual support to create the most loved platform in its industry.
About the Role
Optro is seeking a passionate and skilled Machine Learning Engineer to contribute to the advancement of risk management. This role provides an exciting opportunity to work with cutting-edge Large Language Models (LLMs) and apply techniques such as Retrieval-Augmented Generation (RAG), Few-Shot Learning, Prompt Engineering, Fine-Tuning, Semantic Search, and Knowledge Distillation. The successful candidate will build advanced AI/ML solutions to enhance the AuditBoard product portfolio with features like chat systems, agentic workflows, intelligent data extraction, and personalized insights. This is a customer-focused team dedicated to continuous learning and innovation.
Key Responsibilities
- Build, ship, and own product features end-to-end.
- Collaborate with designers and product managers to create high-performing product features.
- Apply a range of techniques, from classical ML to LLM-based approaches (RAG, prompt engineering, fine-tuning, semantic search), with a strong focus on reliability, performance, and maintainability.
- Write well-designed, maintainable, and testable code.
- Write clear and well-defined design documentation.
- Troubleshoot, debug, and resolve software bugs.
- Maintain a product-minded approach with a focus on the customer.
- Stay updated on AI/ML advancements and explore new techniques and tools.
- Participate in an Agile software development life cycle.
- Work with technologies such as Python, JavaScript, Node.JS, Docker, PostgreSQL, and Kubernetes.
Attributes of a Successful Candidate
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
- 4+ years of hands-on experience in developing and deploying machine learning models.
- Ability to write scalable production-quality code.
- Proficiency in classical machine learning methods and familiarity with newer techniques like LLMs.
- Excellent programming skills in Python, Java, or similar languages.
- Experience with machine learning frameworks such as TensorFlow, PyTorch, Hugging Face, Keras, MXNet, or scikit-learn.
- Familiarity with search/information retrieval and ranking systems.
- Strong communication skills and ability to work collaboratively.
- Analytically minded with a focus on metrics and evaluation.
- Motivation to work hard and always be learning.
Preferred Qualifications
- Experience with Node.JS and modern ES6 or TypeScript.
- Experience working on SaaS web applications.
- Basic understanding of distributed systems.
- Bonus: Docker, Kubernetes experience, AWS/Azure cloud infrastructure.