Staff Machine Learning Engineer, Bolt

• This should be someone with experience, creativity, and passion for producing world-class technology
• Minimum of seven years’ post-secondary education or relevant work experience, along with minimum of five years’ software development experience with Python and SQL
• Minimum of three years’ experience using PyTorch, Tensorflow, building pipelines to deploy NLP and deep learning models into production in a cloud (AWS, GCP, Azure) environment
• Experience building advanced workflows such as retrieval augmented generation, model chaining, dynamic prompting, PEFT/SFT, etc. using LangChain and similar tools
• Experience in establishing model guardrails and developing bias detection and mitigation techniques for AI applications using tools such as NeMo
• Experience with various embedding models and setting up and tuning vector databases to improve performance of semantic search and retrieval systems
• Understand the underlying fundamentals such as Transformers, Self-Attention mechanisms that form the theoretical foundation of LLMs
• Expertise in standard software engineering methodology, e.g., unit testing, test automation, continuous integration, code reviews, design documentation

Desirable

• The Data Platform team works closely with all teams and cross-functional partners (including Product, Engineering, and Data Analysts) to build a foundational data stack powering business analytics
• We are looking for someone to play a mission-critical role in designing and building the machine models for risk, fraud, recommendation, and personalization that powers Bolt
• Companies and consumers alike will rely heavily on what you build, and you’ll have a ton of trust and responsibility
• Architect, build, maintain, and improve new and existing suite of GenAI applications and their underlying systems
• Automate machine learning pipelines, monitor performance and costs, and optimize models by using techniques such as LoRA/QLoRA
• Establish reusable frameworks to streamline model building, deployment and monitoring, incorporating comprehensive monitoring, logging, tracing, and alerting mechanisms
• Build guardrails, compliance rules and oversight workflows into the GenAI application platform, such as establishing approval chains for model updates and staged rollout for production releases
• Ensure development of user-facing applications in the GenAI application platform is easy and safe by enforcing rigorous validation testing before publishing user-generated models and implement a clear peer review process of applications
• Mentor and educate team members to adopt best practices in writing and maintaining production machine learning code
• Collaborate with Product Managers to ensure proper tracking of algorithmic performance KPIs and prioritize performance improvements based on effort and impact