Senior Machine Learning Engineer (AI Platform)

Femtech Insider Ltd.; Boston (MA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Generous equity package

Job summary

WHOOP is seeking a Senior AI/ML Engineer to help scale the intelligence behind our AI-powered experiences. This role involves designing and operating production AI systems, partnering closely with product and data science teams.

The ideal candidate has 3+ years of applied machine learning experience and expertise in modern language models, with a strong emphasis on collaboration and technical direction. Compensation ranges from $150,000 to $210,000 based on experience and qualifications.

Qualifications

• 3+ years of experience in applied machine learning or AI engineering.
• Hands-on experience building with modern language models.
• Solid understanding of ML fundamentals and data pipeline construction.

Responsibilities

• Design and build production AI systems for WHOOP products.
• Lead AI system initiatives, working with data science and product.
• Operationalize workflows for large language models in production.

Skills

Applied machine learning

Experience with language models

Data manipulation and analysis

Communication and collaboration

Job description

At WHOOP, we’re on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level and live longer by using AI to transform continuous physiological data into clear insights and actionable recommendations. Our AI platform is central to this mission, turning raw physiological signals into trusted, personalized guidance that members can act on every day.

WHOOP is hiring a Senior AI/ML Engineer to help scale the intelligence layer behind WHOOP’s AI-powered experiences, including WHOOP Coach, AI-powered Support, and new intelligent features across the product. In this role, you will own core components of the AI Platform that power our internal AI Studio: evaluation pipelines, fine‑tuning workflows, LLM observability, and experimentation tooling. You will partner closely with product and data science to translate real member needs into reliable, impactful AI systems that improve continuously based on real‑world usage.

Responsibilities

• Design, build, and operate production AI systems and scaffolding around language models that power conversational, predictive, and generative capabilities across WHOOP products.
• Lead end‑to‑end AI system initiatives spanning problem definition, data flows, dataset design, evaluation harnesses, deployment, and iteration in close partnership with data science and product.
• Build and maintain pipelines for collecting, curating, and reshaping messy, multi‑source data into high‑quality, well‑structured training and evaluation datasets for language model‑based systems.
• Operationalize fine‑tuning and evaluation workflows for large language models behind member‑facing features such as WHOOP Coach and AI Support, including defining datasets, labels, and taxonomies that reflect real member needs.
• Develop tooling and frameworks that make experimentation, offline/online evaluation, and model deployment faster, safer, and more repeatable, including robust observability for AI features in production.
• Build and maintain feedback loops that connect real member interactions, offline evaluations, and training data updates so that models improve continuously based on real‑world behavior.
• Mentor other engineers and data scientists, share best practices in applied AI/ML, and help elevate the overall technical bar of the AI Platform team.

Qualifications

• 3+ years of experience in applied machine learning, AI engineering, or ML‑focused software engineering roles, including significant work in production environments.
• Hands‑on experience building with modern language models (open‑weight or API‑based), including prompt design, fine‑tuning, and rigorous evaluation.
• Solid working understanding of ML fundamentals (dataset construction, feature engineering, training workflows, evaluation metrics, experiment design) sufficient to make good engineering tradeoffs and partner effectively with data scientists.
• Familiarity with modern LLM training and alignment techniques such as supervised fine‑tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL), and how they influence data requirements, evaluation strategies, and system design in production.
• Proven track record building, shipping, and operating ML‑powered systems end to end, from data pipelines (batch and/or streaming) that transform large datasets into usable training and evaluation sets to production deployments with inference optimization, observability, and lifecycle management.
• Strong proficiency in data manipulation and analysis, including working with messy, multi‑source, and semi‑structured data and translating product questions into well‑defined datasets, labels, and evaluation splits.
• Familiarity with best practices for secure, privacy‑aware AI and working with sensitive data.
• Excellent communication and collaboration skills, with the ability to influence across teams and drive alignment on technical direction.

Location: Boston, MA (relocation may be required).

Equal Opportunity Statement

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.

Compensation & Benefits

The U.S. base salary range for this full‑time position is $150,000 - $210,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job‑related skills, experience, performance, and relevant education or training.

In addition to the base salary, the successful candidate will also receive benefits and a generous equity package. These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.