Intern - France

6 months Internship – AI Agent & Time-Series Algorithm Optimization Overview/Job Summary Traditional algorithm development relied heavily on manual trial and error—engineers spent weeks tweaking hyperparameters, restructuring neural network layers, and hand-crafting features. Today, autonomous AI agents are transforming algorithm design from a manual coding task into an automated, self-correcting feedback loop. Rather than functioning as static code-generation assistants, modern agents act as adaptive system orchestrators.

They orchestrate the entire algorithm optimization lifecycle Internship Description As part of the Software Engineering Team in Grenoble to build and optimize autonomous AI agent workflows that manage the entire lifecycle of time-series and sensor data algorithms. In this role, you will design stateful agent loops that interact with databases, execute continuous KPI benchmarking pipelines, dynamically reconfigure algorithm architectures (e.g., DNN, autoencoders, time-series Transformers), and perform automated comparative analysis to continuously improve predictive accuracy and efficiency.

Your mission is to: Autonomous Database & Data Lifecycle Management Build AI agents that directly query, aggregate, and structure sensor data in frequency domain and time domain from underlying databases (time-series DBs). Automate data validation, feature extraction, sliding-window chunking, and pipeline trigger mechanisms via agent function calling. Automated KPI Tracking & Performance Benchmarking Develop agent flows to calculate and log continuous KPI metrics (e.g., RMSE, MAE, inference latency, drift metrics, and computational cost).

Design dynamic thresholding logic for agents to detect model decay and auto-initiate model re-evaluation or recalibration workflows. Algorithm Architecture Mutation & Comparative Evaluation Implement mechanisms allowing agents to dynamically test, mutate, and switch model backends/architectures. Build automated comparative analysis pipelines where agents run parallel experiments, generate standardized evaluation reports, and deploy optimal model configurations.

Time-Series & Sensor Modeling Implement and tune deep learning models tailored for multivariate sensor streams, applying self-supervised pre-training Qualifications Strong proficiency in Python and deep learning frameworks (Tensorflow, PyTorch). Direct experience with time-series modeling, sensor data algorithms, or signal processing. Hands-on experience building structured AI agent workflows (state machines, tool use, dynamic routing).

Solid working knowledge of SQL, time-series databases, and metric generation/KPI tracking. Familiarity with dynamic model evaluation, hyperparameter tuning, or AutoML loops. Very good English and communication skills Duration: 6 months American R&D Center (ARDC) is a central R&D organization of the TDK Group. Its mission is to contribute into the design of new products within TDK with the ambition to create new product lines.

ARDC is working closely with InvenSense Inc., a TDK Group Company, who is the world’s leading provider of MEMS sensor platforms. InvenSense’s vision of Sensing Everything™ targets the consumer electronics and industrial markets with integrated Motion, Sound, Pressure, Gas solutions. InvenSense’s motion tracking, audio, and services can be found in many of the world’s largest and most iconic brands including smartphones, tablets, wearables, drones, gaming devices, internet of things, automotive products, and remote controls for smart TVs.

Both ARDC & InvenSense are headquartered in San Jose, CA, and have offices in Boston, China, Taiwan, Korea, Japan, France, Slovakia, and Italy.