Research Engineer

Join Snap Inc. as a Research Engineer, where you will work on cutting-edge machine learning for time series data and wearable device sensing. Based in our Paris office, you will collaborate with global hardware and software teams, conduct focused research projects, build signal-processing and ML pipelines, and develop algorithms for cognitive and physiological decoding. You will also translate findings into clear recommendations for stakeholders and leadership. Enjoy a comprehensive benefits package, including generous time off, medical coverage, and retirement plans.

Responsibilities

• Conduct focused, product-related research projects, defining hypotheses and designing experiments using electrophysiological and cognitive data.
• Build signal-processing and ML pipelines for noisy, real-world time-series sensor data, and develop algorithms and ML models for cognitive and physiological decoding.
• Collaborate with software and research teams on model development and evaluation, data capture and demos, and translate findings into clear recommendations for stakeholders.
• Ability to turn research or product questions into concrete technical plans, experiments, metrics, and deliverables
• Ability to clearly communicate model limitations, uncertainty, and technical tradeoffs clearly to cross-functional partners

Requirements

Familiarity with AI-assisted engineering workflows

• Working knowledge of ML for multivariate time series and multimodal sensor fusion
• 3+ years of relevant research or engineering experience developing algorithms or models for time series, biometric, physiological, wearable, behavioral, or multimodal data
• Strong signal-processing fundamentals applied to noisy, real-world biosensor data
• Master’s degree in machine learning, computer science, biomedical engineering, computational neuroscience or related fields
• Hands-on experience with wearable biometrics products or platforms, such as smartwatches, fitness trackers, health trackers, sports-performance wearables, or similar real-world sensing devices
• Proficiency in Python and experience with scientific-computing and ML tools
• PhD in biomedical engineering, computational neuroscience, signal processing with a focus on biosignals or an equivalent record of applied research and engineering impact
• Experience in electroencephalography, Brain-Computer Interface or cognitive-state estimation

Preferred

• Experience designing ML systems for multivariate time series, representation learning, calibration, or domain adaptation
• Experience with artifact-aware modeling, noisy labels or physiological proxy labels
• Experience deploying or optimizing ML models for real-life environments
• Experience with agentic AI workflows, skills-based automation, or AI-assisted research