Note: The job is a remote job and is open to candidates in USA. iFIT is a health and fitness technology company developing a personalized, interactive platform spanning physical fitness, mental health, nutrition, and active recovery. The Senior Machine Learning Engineer will own the strategy and development of recommendation and search personalization systems, building end-to-end ML solutions and partnering with Product, Engineering, Data, and Security to deliver measurable user impact.
Responsibilities
- Own the recommendation + search personalization strategy across product surfaces—define the approach, prioritize experiments, and drive decisions from offline results to real user impact
- Design and build end-to-end ML systems for ranking, retrieval, and personalization (from data pipelines and feature design through model training, evaluation, and serving)
- Improve relevance with a rigorous experimentation loop—set up experiments, interpret results, and iterate quickly when signals change (new content, new user behavior, new product surfaces)
- Partner with product and engineering to ship—translate model behavior into product outcomes, collaborate on integration, and ensure the system performs reliably in production
- Collaborate with data and security on safe, scalable personalization—work through data access, privacy/security constraints, and operational requirements so the system can grow without surprises
Skills
- Proven experience building recommendation and/or search systems that operate in production (not just research prototypes)
- Strong ML engineering fundamentals: ranking/retrieval concepts, feature engineering, model training/evaluation, and practical deployment considerations
- Experience designing measurable improvements—you can connect model changes to user/product outcomes and run experiments that actually inform decisions
- Strong software engineering skills: building reliable pipelines/services, writing maintainable code, and debugging complex systems
- Clear communication: you can explain trade-offs and progress to product/engineering partners and align on next steps
- Proficiency using Claude Code effectively to accelerate building and improving ML systems (e.g., code generation/refactoring, test automation, and debugging support)
- Experience with large-scale personalization (multiple surfaces, high traffic, and evolving catalogs)
- Familiarity with modern LLM-adjacent personalization workflows (e.g., using LLMs to enhance retrieval/ranking or building AI-native development workflows)
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