Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading sleep technology company is focused on improving human performance through personalized sleep and recovery experiences. Its products combine advanced hardware, software, artificial intelligence, and data-driven insights to help individuals optimize their sleep and overall performance.
The organization operates as a high-performance technology team focused on rapid innovation, continuous improvement, and delivering sophisticated consumer-facing products. Its work brings together machine learning, sensor data, personalization, and AI to create individualized experiences for users across multiple markets worldwide.
The Role
The organization is seeking a Machine Learning Engineer (Foundation Models & Personalization) to build and deploy consumer-facing AI systems that power personalization, coaching, and next-generation sleep intelligence. The role works across data, machine learning, product development, and engineering to translate research into reliable and measurable improvements for users.
The position is suited to an engineer who enjoys end-to-end ownership, including problem definition, prototyping, offline evaluation, online experimentation, production deployment, and continuous iteration.
How the Candidate Will Contribute
- Build and deploy machine learning models that improve sleep experiences through personalization, prediction, and behavior understanding, including readiness forecasting, event detection, and individualized recommendations.
- Apply and adapt foundation-model capabilities to real-world product workflows, including LLMs, tools and retrieval-augmented generation (RAG), multimodal modeling, and policy learning.
- Develop user behavior models that connect longitudinal signals such as sleep, environment, and routines to actionable interventions.
- Design robust evaluation strategies using offline metrics, slice-based analysis, calibration, reliability, and fairness measurements.
- Partner with Product teams to design and execute high-quality online experiments.
- Productionize machine learning models through scalable training and inference pipelines.
- Implement model monitoring, drift detection, alerting, and continuous improvement processes.
- Collaborate with Product, Mobile, Backend, and other cross-functional teams to define requirements and deliver high-impact features.
Minimum Qualifications
- 2+ years of experience building machine learning systems in production, ideally for consumer-facing products.
- Strong machine learning fundamentals across supervised learning, sequence and time-series modeling, and modern deep learning.
- Hands-on experience with large-scale model training and evaluation using PyTorch, TensorFlow, or JAX.
- Strong Python engineering skills and experience developing reliable production software.
- Experience with personalization systems, including ranking and recommendation systems, segmentation, lifecycle modeling, propensity modeling, or behavior modeling.
- Understanding of causal and experimentation-aware approaches to machine learning.
- Experience with data technologies such as SQL and distributed computing frameworks including Spark or Ray.
- Experience working with cloud-based storage and computing environments.
- Strong product sense and the ability to translate ambiguous objectives into measurable outcomes.
- Ability to work effectively with stakeholders and iterate rapidly based on product requirements and results.
Preferred Qualifications
- Experience applying LLMs or foundation models to product features, including tool use, retrieval, structured outputs, guardrails, and model evaluations.
- Experience working with multimodal data, including sensor signals and contextual information.
- Experience working with health or biometric data.
- Knowledge of privacy-preserving machine learning approaches such as on-device learning, federated learning, differential privacy, or data minimization.
- Experience designing experimentation frameworks for personalization.
- Experience applying causal inference techniques to personalization systems.
- Experience with MCP-style integrations or similar approaches for connecting AI systems with tools and external capabilities.
Disclaimer
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