Senior Machine Learning Engineer - Active Learning

Sense•Cambridge, MA

1d•$165,000 - $185,000•Hybrid

About The Position

Sense is a climate technology company that turns electrical signals in homes and on the grid into real-time insights about energy use, devices, and faults. Its insights help people lower electric bills and use less electricity, and help utilities prevent wildfires. Sense's technology team researches, trains, deploys, and supports load disaggregation and fault detection models. The Senior Machine Learning Engineer will own the loop from data to labels to improved models, applying active learning and weak supervision to generate labels, prioritize what should be labeled next, and turn errors found in the field into new labels. This is a hands-on role involving coding, model training, data analysis, and experiment documentation. The engineer will collaborate with data scientists, annotators, product, and engineering teams, and direct the development of annotation tools and annotator workflows to scale labeling. Success is measured by reducing cost per annotation, including time per annotation, while maintaining label quality at or above targets. The position is based on working from Sense's Cambridge, Massachusetts office at least two days per week. Work authorization is required. The listed salary range is $165,000 to $185,000.

Requirements

MS or PhD in Computer Science, Electrical Engineering, Physics, Statistics, or a related field, or equivalent experience.
At least 5 years of experience in data science or machine learning, including at least 2 years building or managing labeled datasets for ML.
Hands-on experience applying active learning to real data, including choosing a sampling strategy and measuring whether it helped.
Experience finding rare or hard examples in large datasets using model errors, uncertainty, or similarity search.
Experience training deep learning models and using weak supervision or model-assisted labeling.
Experience measuring label quality and correcting label errors.
Experience with Python, SQL, and data pipelines.
A track record of working with other teams, such as product, software, or hardware.
Ability to work from the Cambridge, Massachusetts office at least two days per week.
Work authorization is required.

Nice To Haves

Experience with time series, audio, sensor, or other signal data.
Experience leading an annotation team or directing development of annotation tools.
Experience using embeddings from large or foundation models for data analysis.

Responsibilities

Design and run active learning to select which homes, devices, and time periods to label based on model uncertainty or errors.
Turn bad detections, field accuracy results, and user corrections into new ground truth, and track whether retrained models improve.
Find rare events and hard cases in home data, such as uncommon or mislabeled devices, and add them to labeling and evaluation sets.
Own the ground truth plan, including which data to label, how much to label, and in what order.
Direct annotators by setting priorities, tracking throughput, providing guidance, and establishing data quality standards.
Direct development of the annotation tool, adding features that improve collection of new and better data, including model-assisted features.
Measure and improve label quality using metrics, outlier detection, human review, and other methods.
Work with data engineering and ML operations to version labels and track which labels trained each model.
Keep evaluation sets separate from training data so offline results predict performance in the field.
Review code, provide peer feedback on research and experiments, contribute to shared documentation, and help mentor or coach less experienced team members.