Staff Machine Learning Engineer (Training)
Job Summary
Job Description
WHOOP is seeking a Staff Machine Learning Engineer to join their Training team. The role involves developing algorithmic features and metrics related to daily movement, exercise, and training by integrating various data sources and grounding work in clinical theory. The engineer will collaborate with data scientists, MLOps engineers, and software developers to design, train, and deploy machine learning algorithms, and partner with product managers to deliver data-driven insights to members. Responsibilities include optimizing ML algorithms, contributing to technical roadmaps, ensuring reliable deployment, and mentoring junior data scientists. Experience with time series data, scientific Python, and SQL is required.
Must have:
- 7+ years ML engineering/applied research experience
- 4+ years applying advanced mathematical/statistical techniques
- Experience deploying production ML systems on cloud platforms
- Familiarity with MLOps best practices
- Experience with time series data
- Proficiency in scientific Python and SQL
- Excellent communication skills
Good to have:
- Bachelor's Degree in Mathematics, Statistics, Computer Science, or related field
- Collaborate with infrastructure teams on Docker, CI/CD, model versioning, observability
- Experience with wearable data applications
12 skills required
Job Details
WHOOP is an advanced health and fitness wearable, on a mission to unlock human performance. WHOOP empowers its members to improve their health and perform at a higher level by providing a deep understanding of their bodies and daily lives.
As a Staff Machine Learning Engineer on our Training team, you will develop algorithmic features and metrics that capture aspects of daily movement, exercise, and training. You'll integrate diverse data sources—including WHOOP sensor data and gold-standard reference datasets—while grounding your work in clinical theory and scientific literature.
You’ll work cross-functionally with data scientists, MLOps engineers, and software developers to design, train, deploy, and maintain machine learning algorithms that power training features. You'll also partner with product managers to identify opportunities for delivering novel, data-driven insights to WHOOP members.
RESPONSIBILITIES:
- Design, train, and optimize machine learning algorithms for movement, exercise and training applications across diverse backend platforms
- Collaborate closely with data scientists, ML Ops and software engineering teams to ensure reliable deployment, observability, and robust integration with the WHOOP ecosystem
- Contribute to technical roadmap development and architectural decision-making and strategy across the data science and research department
- Collaborate with product managers to design impactful, member-facing features
- Work closely with a team of data scientists in developing algorithms that power member-facing features
- Work with Data Engineers to improve data pipelining, tooling for machine learning, and systems for quality and validation
- Mentor other data scientists on the team, by providing actionable feedback for more junior data scientists on technical areas of growth
- Periodically serve as the on-call data scientist to respond in real time to incidents affecting production services
QUALIFICATIONS:
- Bachelor's Degree in Mathematics, Statistics, Computer Science, or a related field
- 7+ years of ML engineering, applied research, or a similar role
- 4+ years experience applying advanced mathematical and statistical techniques
- Experience deploying and maintaining production ML systems on cloud platforms (e.g., Kubernetes, AWS, GCP)
- Familiarity with MLOps best practices and the ability to collaborate effectively with infrastructure teams on Docker, CI/CD workflows, model versioning, and observability tools
- Experience working with time series data, preferably with wearable data applications
- Proficiency in scientific Python and SQL
- Excellent verbal and written communication skills
This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.
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.
Similar Jobs
warner bros games
Bengaluru, Karnataka, India (Hybrid)
• 7 Months ago
Open Systems Technologies
San Francisco, California, United States (Hybrid)
• 1 Week ago
Get notifed when new similar jobs are uploaded
Similar Skill Jobs
Zones
Islamabad, Islamabad Capital Territory, Pakistan (On-Site)
• 1 Week ago
Shield AI
Dallas, Texas, United States (On-Site)
• 1 Week ago
Western Digital
Prachin Buri, Thailand (On-Site)
• 2 Weeks ago
Get notifed when new similar jobs are uploaded
Jobs in Boston, Massachusetts, United States
Coherent corp.
San Jose, California, United States (On-Site)
• 1 Month ago
Feld Entertainment
Jessup, Maryland, United States (On-Site)
• 9 Months ago
Trailer park group
Los Angeles, California, United States (On-Site)
• 2 Months ago
Get notifed when new similar jobs are uploaded
Research Development Jobs
bytedance
San Jose, California, United States (On-Site)
• 3 Months ago
Apple
Cupertino, California, United States (On-Site)
• 2 Months ago
bytedance
San Jose, California, United States (On-Site)
• 3 Months ago
Qualcomm
San Diego, California, United States (On-Site)
• 3 Months ago
Ello
San Francisco, California, United States (On-Site)
• 4 Months ago
Get notifed when new similar jobs are uploaded
