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# Senior Machine Learning Engineer

- **Company:** INVENTURES INCORPORATED
- **Location:** San Francisco Bay Area, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $210,000.0 - $240,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Computer Vision, Automation of Tests, Continuous Integration, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Pattern Recognition, Recommender Systems, Mixpanel, SQL Databases, Tableau (Software), Retrieval-Augmented Generation, Large Language Models
- **Published:** September 23, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pm2txu916d

## About the Role

* You have designed, built or operated a recommendation system in production that combines multiple sources into a single customer-facing output, not just contributed data to someone else's model.
 * You have trained and evaluated models for anomaly detection, pattern recognition or similar applied ML problems in production.
 * You have built recommendation or personalisation logic using LLMs, such as prompt-based scoring, retrieval-augmented generation or agent reasoning, in a live product.
 * You are comfortable in production Python and SQL to source and prepare model inputs, and you ship model changes with CI/CD discipline, measuring real impact.
 * You have around five or more years in applied ML, recommendation systems or closely related work, and a genuine bias toward action.
 * Nice to have: anomaly or fraud detection and forecasting; computer vision or IoT sensor data as a model input; feature stores or ML feature pipelines; and tools such as Hex, Mixpanel or Tableau.
 * You are based in the Bay Area and comfortable with a hybrid pattern.

## Description

As the Recommendations Engineer, you will:

 * Build and operate the customer-facing recommendation engine that turns food-waste data into actionable outputs: purchasing suggestions, anomaly explanations and operational nudges, including LLM-based logic where it makes sense.
 * Train, evaluate and iterate on models for anomaly detection, pattern recognition and recommendation on production food-waste data.
 * Design the features and signals, from IoT sensors to usage data, that feed your models.
 * Define and track the metrics that measure whether recommendations actually matter to customers, not just whether the pipeline ran.
 * Bring CI/CD and experimentation discipline to model changes: automated testing, staged rollout, A/B testing or holdouts, clear rollback paths, and monitoring that catches degradation in production.


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