Instrumentation & Test Engineer

Experimental Design & Analysis: Lead the Design of Experiments (DoE), defining acquisition parameters, sensor requirements, sampling frequencies, and test methodologies to guarantee repeatability and statistical reliability.

Signal Validation: Perform immediate signal analysis (time & frequency domain) to verify data quality before it enters the AI pipeline, ensuring physical phenomena are correctly captured.

Instrumentation Setup: Select, install, and validate industrial and scientific sensors (vibration, temperature, pressure, torque, strain, flow), ensuring proper mounting, calibration, grounding, shielding, and overall signal integrity.

DAQ Operation: Configure and operate high-fidelity DAQ systems (e.g., HBK/HBM, Dewesoft, Siemens, NI), managing channel setups, filtering, synchronization, and triggering for complex test campaigns.

Automation Scripting: Develop and maintain Python-based tools specifically for hardware automation and signal processing (using libraries like Scipy/Numpy) to streamline data ingestion and validation.

System Integration: Work closely with mechanical and automation teams to ensure test benches are properly instrumented, electrically integrated, and safe for operation.

Documentation: Ensure traceability and comprehensive documentation of test configurations, wiring diagrams, sensor setups, DAQ settings, and calibration routines.

Troubleshooting: Diagnose and resolve issues related to electrical noise, grounding loops, synchronization failures, connector faults, or hardware limitations.

Continuous Improvement: Optimize acquisition workflows, tooling, and laboratory best practices to increase testing throughput and data reliability.

Degree in Electrical Engineering, Electronics Engineering, Mechatronics, Physics, Computer Engineering, or a related field. (Candidates with equivalent experience in data acquisition or experimental testing environments are also encouraged to apply.)

Advanced English and Portuguese.

Availability to work on-site in São Paulo (Chácara Santo Antonio - Zona Sul).

Hands-on experience with data acquisition systems and laboratory instrumentation in industrial, automotive, aerospace, or experimental R&D environments.

Familiarity with DAQ hardware and software platforms, such as HBK/HBM, Dewesoft, Siemens, or NI (National Instruments).

Strong understanding of sensor physics, signal conditioning, sampling theory, noise mitigation, grounding, and measurement uncertainty.

Proficiency in Python, with a focus on Signal Processing and acquisition automation (not just general data manipulation).

Experience working with time-series data and synchronous multisensor setups.

Ability to operate in experimental or laboratory environments, comfortable with physical tasks (cabling, mounting sensors).

Strong problem-solving skills related to instrumentation and signal integrity.

Experience working in automotive, aerospace, or NVH laboratories, where high-precision data acquisition is standard.

Strong background in vibration analysis, signal processing, or frequency-domain techniques (FFT, spectral analysis, filtering).

Familiarity with strain gauge measurements, Wheatstone bridges, and instrumentation amplifiers.

Experience with MATLAB or LabVIEW for signal processing or measurement workflows.

Knowledge of synchronization techniques (PTP, GPS timing, hardware triggering).

Exposure to machine learning workflows, understanding how physical data feeds into feature extraction.

Hands-on experience designing or building custom test fixtures or sensor mounts.

Experience managing large experimental datasets and knowledge of SQL or Time-Series databases for long-term data storage and traceability.