Machine Learning Engineer III

The Company CommerceIQ is building the AI platform that runs commerce for the world's largest brands. We are not selling AI demos. We are shipping AI agents for content, media, and sales into the workflows of the Fortune 100 every week. 2,200+ Customers 10 of Top 12 CPG Companies 900+ Retailers Connected $200M+ Raised Customers include Coca-Cola, Nestlé, Colgate-Palmolive, Mondelez, Samsung, and Kellogg's. Backed by SoftBank, Insight Partners, and Madrona. Headquartered in Mountain View with teams across the US, India, Canada, and the UK. Pre-IPO. Company Overview At CommerceIQ, we help consumer brands accelerate their retail ecommerce market share growth and profitability through machine learning algorithms. We are building the world’s most complete and sophisticated Retail Ecommerce Management Platform, which connects and intelligently automates the management of retail ecommerce channels like Amazon, Walmart, and Instacart, across the entire ecommerce operational chain of retail media management, sales operations, supply chain, and digital self analytics. We are in hyper growth mode, having recently raised our Series D funding at unicorn valuation (>$1B) and ended our third year of triple-digit revenue growth. Continued acceleration of our growth is fueled by landing new customers, expanding our platform through new products, managing new retail ecommerce platforms, and delivering exceptional customer service to unlock high net retention rates. What You’ll Be Doing As an ML Engineer in the team, you will work closely with Data Science, Engineering, Platform, Product and Operations teams to build state-of- the-art ML based solutions for B2B SaaS products. This will entail applying advanced ML algorithms at scale for core products and developing robust end to end production pipelines which includes Human-in-the-Loop component to boost the quality. The ideal candidate will have a strong background in machine learning model development, deploying large scale, high throughput machine learning pipelines to production, experience with developing and managing frameworks for machine learning platforms, which they can utilise to manage and improve our company’s AI/ML initiatives. Contribute to development of multiple AI driven end to end pipelines that allows for deployment and scalability of machine learning models Build an end-to-end machine learning platform, covering all lifecycle stages of a model, to ease model development and deployment Build tools and capabilities that help with data ingestion to feature engineering, data management and organisation Deploy cutting edge algorithms like LLMs etc. on GPUs along with distributed computing for scalability Contribute to tools and capabilities for model management and model performance monitoring Implement the best engineering practices for scaling ML-powered features, with a goal to enable the fast iteration of and efficient experimentation with novel features