Senior Machine Learning Engineer - Active Learning

Sense; Town of Cambridge; On-site; AUD 235,000 - 263,000; Full time

Benefits offered by this job

Health Plan

Retirement Plan

Life Insurance

Paid Time Off

Family Leave

Disability Insurance

Work From Home

Free Food & Snacks

Wellness Resources

Stock Option Plan

Job summary

Sense is seeking a Senior Machine Learning Engineer specializing in active learning to scale data operations and improve production models. The role is hands-on: you will write code, train models, and guide annotators to generate high‑quality labels efficiently.

You will own the ground truth plan, design active learning strategies, and collaborate with data engineers, product, and hardware teams to drive label quality and model performance.

Qualifications

• MS or PhD in CS, Electrical Engineering, Physics, Statistics, or related field, or equivalent experience.
• 5+ years of experience in data science or machine learning, including 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 impact.
• Experience finding rare or hard examples in large datasets from model errors or uncertainty.
• Experience training deep learning models, with weak supervision or model-assisted labeling.
• Experience measuring label quality and fixing label errors.
• Experience with Python, SQL, and data pipelines.
• Track record of working with product, software, or hardware teams.

Responsibilities

• Design and run active learning to select homes, devices, and time periods to label next.
• Turn detections and field results into new ground truth; track model improvement after retraining.
• Find rare events and hard cases in home data and add them to labeling sets.
• Own the ground truth plan: data, quantity, and labeling order.
• Direct a team of annotators; set priorities, track throughput, and ensure data quality.
• Develop annotation tools to improve data collection and model support.
• Measure label quality with metrics, outlier detection, and human review.
• Coordinate with data engineers and ML ops to version labels and separate eval sets from training data.

Skills

Active learning

Machine learning

Data science

Collaboration

Model evaluation

Education

MS or PhD in CS/EE/Physics/Statistics or related field

Tools

Python

SQL

Data pipelines

Job description

Senior Machine Learning Engineer - Active Learning

About Sense

Sense is a climate tech company that turns electric signals in homes and on the grid into real-time insight about energy use, devices, and faults. These insights help people lower their electric bills and use less electricity, and they help utilities prevent wildfires, among other applications. These insights come from load disaggregation and fault detection models that are running on meters today. The technology team at Sense researches, trains, deploys, and supports these models.

We are looking for an experienced Senior Machine Learning Engineer with expertise in active learning systems to help us scale our data operations so we can continue to improve our core production models and build the foundation model of electrical devices and energy.

The role

This Senior Machine Learning Engineer will own the loop from data to labels to improved models. You will use active learning and weak supervision methods to generate labels, decide what to label next, and turn errors we find in the field into new labels.

This is a hands‑on role. You will be expected to write code, train models, analyze data, and document experiments. You will work with data scientists, annotators, product, and engineers to get labels and improve models. You will direct the development of annotation tools and the work of our annotators to speed up and scale labeling.

Success in this role is measured by the reduction in cost per annotation (i.e., time per annotation), while keeping label quality at or above our targets.

Responsibilities:

• Design and run active learning to choose which homes, devices, and time periods to label next, based on where models are uncertain or wrong.
• 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: which data, how much, and in what order.
• Direct a team of annotators: set priorities, track throughput, and provide guidance. Set data quality standards.
• Direct development of our annotation tool. Add features that enhance our ability to collect new and better data, including features that rely on models.
• Measure and improve label quality through metrics, outlier detection, human review, etc.
• Work with data engineers and ML operations to version labels and track which labels trained which model. Keep evaluation sets separate from training data so offline results predict performance in the field.
• Be a good teammate: review code, provide peer feedback on research and experiments, and contribute to shared documentation. Help mentor/coach less experienced team members.

Qualifications:

• MS or PhD in Computer Science, Electrical Engineering, Physics, Statistics, or a related field, or equivalent experience.
• 5+ years of experience in data science or machine learning, including 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, i.e., from model errors, uncertainty, or similarity search.
• Experience training deep learning models, and with weak supervision or model‑assisted labeling.
• Experience measuring label quality and fixing label errors.
• Experience with Python, SQL, and data pipelines.
• A track record of working with other teams, i.e., product, software, or hardware.

Preferred qualifications:

• 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.

Logistics:

• Location: able to work from our Cambridge, MA office at least 2 days per week.
• Salary range: $165,000 to $185,000
• Work authorization: required.

• Health Care Plan (Medical, Dental & Vision)
• Retirement Plan (401k, IRA)
• Life Insurance (Basic, Voluntary & AD&D)
• Paid Time Off (Vacation, Sick & Public Holidays)
• Family Leave (Maternity, Paternity)
• Short Term & Long Term Disability
• Work From Home
• Free Food & Snacks
• Wellness Resources
• Stock Option Plan