Member of Technical Staff [AI/ML Engineer]

theburntapp.com • Burnt Prairie, IL

2d • $150,000 - $275,000 • Onsite

About The Position

Burnt is building the Operating Brain for the global supply chain, a living, evolving system designed to run businesses on autopilot. The brain operates on models derived from historical data, transforming it into forecasts and an ontology for agent reasoning. The company is starting in the food industry, a complex and unforgiving sector, to solve challenging modeling problems that have been historically unaddressed. Burnt fosters a highly competitive culture, focused on customer success and building the future of supply chain operations for the next two decades.

Requirements

• Python at expert level.
• Apache Spark and the surrounding data engineering ecosystem.
• AWS SageMaker as the core platform, plus S3, Glue, Step Functions and the rest.
• MLflow, Kubeflow, or equivalent.
• LoRA and PEFT libraries such as HuggingFace PEFT or TRL.
• Prophet, NeuralForecast, statsmodels, or similar.
• Neo4j, RDF, OWL, SPARQL, or similar.
• Enough TypeScript and React to be useful in our codebase.
• Owned MLOps end to end, from model creation through deployment, iteration, monitoring, and support.
• Fine-tuned LLMs with LoRA or PEFT on real datasets, not toy ones.
• Built and maintained time series forecasting models serving production traffic.
• Worked inside systems backed by ontologies and knowledge graphs.
• Operated across the AWS ecosystem beyond SageMaker.
• Engineered data pipelines at scale with Spark.
• Built model versioning, drift detection, and retraining pipelines that ran without you watching them.
• Architected production systems end to end and can walk through the tradeoffs you chose and what you would do differently now.
• Worked outside the model layer when it was needed, shipping application code alongside product engineers.
• Python at an expert level. Demonstrable, not claimed.
• ML models, not agents, deployed and maintained in production.
• AWS SageMaker hands-on.
• A time series forecasting model in production.
• LLM fine-tuning with LoRA or PEFT. You've done it, not read about it.
• Ontology or knowledge-graph-backed systems in a real product context.
• Can articulate system design decisions you personally architected.
• Willing and able to pick up full stack work when the team needs it. No "that's not my job."

Nice To Haves

• Able to ramp fast on the rest of the tech stack.

Responsibilities

• Own MLOps end to end: model creation, deployment, iteration, monitoring, and support.
• Build and maintain time series forecasting models that serve production traffic and hold up under backtest against real order books.
• Fine-tune LLMs with LoRA and PEFT on our own data, and build the evals that decide what ships.
• Design and maintain the ontology and knowledge graph our agents reason over.
• Engineer data pipelines at scale with Spark, over messy multi-tenant supply chain data.
• Build the versioning, drift detection, and retraining pipelines that keep models honest after launch.
• Run the AWS ML stack: SageMaker at the core, with S3, Glue, and Step Functions around it.
• Architect the systems your models live inside, not just the models, and own those design decisions.
• Step into full stack work when the team needs it, from the API that serves a prediction to the interface a buyer actually uses.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed