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# Machine Learning Engineer

- **Company:** Brahma Consulting Group
- **Location:** Newark, CA, United States
- **Contract:** Permanent contract
- **Skills:** Data Transformation, Python (Programming Language), Machine Learning, Sensor Fusion, Signal Processing, Reinforcement Learning, Pytorch, Stream Processing
- **Published:** August 31, 2026
- **Apply:** https://www.disabledperson.com/jobs/74614218-machine-learning-engineer

## About the Role

* Education: PhD or Postdoc in Physics, Astrophysics, Materials Science, or a related quantitative field.
 * Experience: 0-4 years post-PhD experience (new grads accepted) focused on spectroscopy, signal processing, or applied ML with spectral data.
 * Technical Mastery: Strong Python and PyTorch proficiency.
 * Bandits & Online Learning: Practical experience implementing bandit algorithms (UCB, Thompson sampling, sleeping/contextual bandits) and handling classification under severe class imbalance.
 * Physics Depth: Strong foundational understanding of spectral physics and 1D sensor signal processing, rather than purely high-level applied ML.

Nice to Have

 * Spectroscopy or chemometrics experience with LIBS, Raman, NIR, or hyperspectral datasets.
 * Hands-on experience deploying contextual bandits or reinforcement learning in live production environments.
 * Familiarity with streaming systems, sensor fusion, and industrial measurement hardware.

## Description

The core technology relies on fusing spectral signatures with visual and multi-sensor data to classify materials and drive precision recycling. As a Spectral ML Engineer, you will own the core classification models and build the online learning system that selects the most informative shot locations on physical materials.

What You Will Do

 * Spectral Preprocessing: Own baseline correction, normalization, denoising, and derivative extraction.
 * Core Classification: Develop and optimize models spanning chemometrics baselines, 1D CNNs, and transformer architectures.
 * Online Learning & Decision Layer: Build, deploy, and monitor sleeping and contextual multi-armed bandit policies (e.g., UCB, Thompson Sampling) to choose optimal measurement locations under dynamic arm availability, delayed/noisy rewards, and drift.
 * Multimodal Sensor Fusion: Integrate 1D spectral data with visual and real-time streaming sensor inputs into cohesive, production-grade multimodal architectures.
 * Evaluation & Production: Establish rigorous offline/online evaluation frameworks and regret monitoring pipelines to push algorithms directly to physical machinery in production.


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