Senior Sensor Intelligence Engineer (Tools & Validation)
Whoop is hiring a Senior Sensor Intelligence Engineer (Tools & Validation) in Boston, United States. It pays $150k-$215k a year and Level rates it ; you can apply on Whoop.
Level's read on this job
- Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting mentions Python, SQL and signal processing among its tools and skills.
- Level classes it as a senior role.
- Whoop has 124 other open roles on Level.
AI in this role
Develop verification methodologies, validation tools, and algorithms to transform physiological time-series signals into member insights.
At WHOOP, we’re on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.
WHOOP is seeking a Senior Sensor Intelligence Engineer to help develop, validate, and scale the sensor algorithms and tools that transform noisy physiological signals into meaningful member insights. As part of a cross-functional team spanning Sensor Intelligence, Hardware, ML Research, WHOOP Labs, Firmware, Apparel & Accessories, and other engineering disciplines, you will develop robust algorithm verification and validation methodologies, evaluate sensor and algorithm performance against appropriate reference data, build diagnostic and analysis tools, characterize failure modes, and help determine algorithm readiness for real-world deployment across our diverse member population.
RESPONSIBILITIES:
Develop end-to-end verification and validation methodologies for sensor algorithms and biosensor systems, including test design, reference-data comparison, performance metrics, acceptance criteria, and readiness assessment across large-scale datasets
Partner with Data Science, WHOOP Labs, Research, Firmware, and other engineering teams to design representative data collections, evaluate algorithm performance against reference measurements, characterize failure modes, and determine readiness for real-world deployment
Apply signal processing, statistical analysis, and machine learning techniques to physiological time-series data to identify performance opportunities, investigate algorithm failures, and improve sensor-derived insights
Build and maintain scalable workflows, dashboards, and diagnostic tools for algorithm validation, regression testing, performance monitoring, data quality assessment, and sensor behavior across diverse populations and use cases
Contribute to software development, debugging, testing, and validation processes, ensuring analytical tools, algorithms, and results meet production-quality standards
Communicate findings through clear technical documentation, reports, and visualizations that enable cross-functional teams to make informed product and engineering decisions
QUALIFICATIONS:
Bachelor’s or Master’s degree in electrical engineering, biomedical engineering, applied mathematics, statistics, computer science, or a related quantitative field, or equivalent practical experience
5+ years of industry or research experience working with signal processing, machine learning, statistical analysis, algorithm validation, or related quantitative methods
Experience analyzing time-series data and applying core signal processing techniques such as filtering, spectral analysis, peak detection, and windowing
Demonstrated experience independently designing and driving validations studies, including one or more of test methodology development, reference-data comparison, performance evaluation, acceptance-criteria definition, or failure-mode investigation.
Proven track record of investigating intricate or ill-defined technical challenges, pinpointing root causes, and delivering solutions across algorithm, sensor, data, and system domains
Proficiency in Python and/or C, with experience developing reliable tools or software for data processing, analysis, or algorithm validation
Strong foundation in statistical inference and experimental analysis, including the ability to select appropriate methods, interpret results, and communicate analytical trade-offs
Experience working with large datasets and modern engineering or data infrastructure such as Git, CI/CD systems, cloud platforms, or data warehouses
Strong written and verbal communication skills, with the ability to translate complex technical findings into concise documentation, visualizations, and actionable recommendations
Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions.
Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.
WHOOP is an Equal Opportunity Employer and participates in E-Verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
How we rate this
Senior Sensor Intelligence Engineer (Tools & Validation) at Whoop rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where signal processing was part of your work. What did you do?
- Tell me about a project where statistical analysis was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where algorithm validation was part of your work. What did you do?
- Walk me through how you've used Python in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Signal Processing, Statistical Analysis, Machine learning, Algorithm Validation, and Python. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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