Machine Learning Engineer III

About the role

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.

About the Role

We are looking for a hands-on Machine Learning Engineer III to build and run the ML and AI systems behind our agents for content, media, and sales. You will work closely with Data Science, Engineering, Platform, Product, and Operations teams to take models and LLM-based agents from idea to production, and keep them reliable, fast, and cost-efficient at scale. This is an engineering role first: you will own your systems end to end, including the infrastructure they run on. We look for builders who ship v0s quickly, iterate against evals, and stay adaptable about models and frameworks as the field moves.

Key Responsibilities :

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AI Agents & Harnesses: Design, build, and ship production agents and the harnesses around them: tool and action design, context window management (retrieval, summarization, compaction), model selection and routing, memory and state, and subagent and parallel tool-call patterns. Iterate until agents reliably complete real customer tasks.

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ML Pipelines: Build end-to-end batch and real-time pipelines for classical ML and LLM workloads, from data ingestion to deployment.

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Infrastructure: Own the infrastructure your systems run on: containers, Kubernetes, queues, caches, and cloud services on AWS/GCP, managed as code.

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Reliability & Quality: Meet latency, throughput, and cost budgets using caching, batching, and parallel execution. Handle rate limits, retries, and failures gracefully. Instrument agents with tracing to profile behavior, find bottlenecks, and catch quality regressions before customers do.

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Evals: Build eval sets from production traces, expert examples, and past failures, plus automated checks and LLM judges that measure model and agent quality. Use them to gate every prompt, model, and code change.

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Feedback & Experimentation: Turn reviewer and customer feedback into measurable improvements through human-in-the-loop workflows, A/B tests, offline replays, and staged rollouts.

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Technical Ownership: Review designs and code, raise the engineering bar, and contribute to the ML and AI roadmap with Data Science and Platform teams.

Required Skills :

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