Senior AI/ML Engineer

San Francisco | On-site | $150k–$275k + equity

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We are working with a fast-growing AI startup building the operating brain for the supply chain. They’ve grown 10x in the last year with a small engineering team and are now building out the model layer underneath their production AI systems.

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They’re looking for their first dedicated ML Engineer to own models end-to-end, from raw data through to production. You’ll work with years of real-world operational data across 500k+ SKUs, building systems that directly impact how the business operates.

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This is not a research role, and it’s not an LLM-wrapper role. They’re looking for someone who can build, deploy and operate production ML systems - and take ownership when reality changes.

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What you'll own

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• Build production forecasting models across messy, intermittent and seasonal demand, including cold-start SKUs, promotions, perishability and long-tail demand
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• Build datasets and fine-tune models using LoRA / PEFT, with rigorous evaluations determining what actually ships to production
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• Build the representation layer that allows AI systems to reason across inconsistent products, vendors, pack sizes and units of measure
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• Own the infrastructure around those models, including deployment, versioning, monitoring, drift detection and automated retraining
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• Build large-scale ML and data workloads using Python + Spark
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• Work with AWS SageMaker, S3, Glue + Step Functions
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• Build production inference and evaluation infrastructure
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• Use MLflow, Kubeflow or equivalent MLOps tooling
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• Contribute outside the model layer when needed, including enough TypeScript/React to work across the wider product
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There are no handoffs. You’ll build the model, put it into production, monitor it and fix it when reality changes.

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What we're looking for

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• 5-7 years of experience building production ML systems
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• Experience building and maintaining time-series forecasting models serving production traffic
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• Hands-on experience with AWS SageMaker
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• Experience fine-tuning LLMs using LoRA or PEFT on real datasets
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• Experience building systems backed by ontologies or knowledge graphs
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• Strong experience engineering large-scale data pipelines with Spark
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• Experience owning production models through deployment, monitoring, drift detection and retraining
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• Strong architecture skills, with the ability to explain and defend technical decisions in detail
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• Comfortable working across the full ML lifecycle rather than owning just one part of the process
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They’re looking for someone who can talk about what happened after the model shipped - when it degraded, how you detected it, what it got wrong and what you changed.