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Requirements:
Minimum Qualifications
BSc in Computer Science or a related field.
7+ years of hands-on experience in native iOS (Swift/Objective-C) development.
5+ years of experience building full stack Python-based applications.

Preferred Qualifications
Proven track record of independently leading projects end to end: defining scope, designing the architecture, breaking down the work, and driving execution through to delivery.
Proven expertise designing and shipping large-scale, production-grade applications.
Solid understanding of REST/gRPC API design, databases, and service architecture.
Experience with CI/CD, debugging across platforms, and performance optimization in distributed systems.
Strong analytical, architectural, and problem-solving skills.
Excellent communication and collaboration skills, with experience mentoring or guiding peers.
Proficiency in Git and modern development workflows.
Familiarity with Bluetooth/BLE protocols and IoT device communication (advantage).
Familiarity with on-device ML/AI processing, specifically CoreML (advantage).

Requirements:
Minimum Qualifications
BSc in Electrical Engineering, Computer Engineering, Mechanical Engineering, Physics, or a related field - advanced degree strongly preferred.
7+ years of experience in environmental testing, sensor characterization, or embedded system validation.
Strong Python proficiency - automation, data analysis, instrument control, and framework development.
Proven experience bringing up sensors and subsystems on new development platforms.
Hands-on experience with HW/SW automation design, control systems, and development workflows.
Strong system-level vision - ability to comprehend the full system architecture and understand how individual sensor components interact within the broader product context.
System-to-component testing capability - proven ability to conduct system-level validation while diving deep to the component level for root-cause analysis, failure isolation, and detailed characterization.
Strong experience in environmental test design and execution for electro-optical systems.
Proficiency in system-level Test firmware development for embedded sensor platforms.
Demonstrated ability to architect and implement automation frameworks (Python-based) supporting: Instrument control and data acquisitio; API development for system-level integration; End-to-end test orchestration and regression capabilities.
Knowledge of optical performance metrics - MTF, field of view, distortion, stray light, and dynamic range under varying environmental conditions.
Familiarity with environmental simulation equipment - solar simulators, rain chambers, thermal chambers, vibration tables, and motion stages.
Understanding of laser sensor behavior under environmental stress - including atmospheric scattering, thermal drift, and background noise rejection.
Experience with sensor alignment and calibration methodologies in controlled laboratory settings.

Preferred Qualifications
MSc in Physics, Electro-Optics, Electrical Engineering, or a related discipline - considered a significant advantage.
Experience working with VXcl platforms and Sony image sensors - configuration, bring-up, characterization, and integration.
Experience developing custom test fixtures and mechanical setups for optical evaluation - including design, specification, and validation of purpose-built environmental and alignment rigs.

Requirements:
Minimum Qualifications
BSc in Electrical Engineering, Computer Engineering, Physics, or a related field.
7+ years of extensive experience in embedded systems, electro-optical sensor integration, or related disciplines.
Strong Python proficiency - automation frameworks, data analysis, instrument control, and test development.
Demonstrated experience with HW/SW automation, design control, and development methodologies.
Solid understanding of camera and laser sensor technologies and associated interface protocols.
Proven ability to bridge the gap between physics-level understanding and engineering implementation.
Deep understanding of electro-optical sensor principles - including photodetection, quantum efficiency, noise characterization (read noise, dark current, shot noise), and signal-to-noise ratio analysis.
Experience with optical test bench setups: integrating spheres, collimators, laser sources, spectral filters, and beam profiling equipment.
Familiarity with LiDAR / ToF sensor technologies - including timing resolution, range accuracy, and multi-path interference analysis.
Knowledge of image sensor characterization methodologies.
Experience with radiometric and photometric measurements and calibration procedures.
Understanding of laser safety classifications, optical power measurement, and wavelength characterization.
Ability to define and execute environmental and stress testing protocols for optical sensor assemblies.

Preferred Qualifications
Advanced degree (MSc or PhD) in Physics, Electro-Optics, or Electrical Engineering.
Experience with embedded firmware development or firmware-hardware co-validation.
Familiarity with CI/CD pipelines for hardware validation environments.

Requirements:
Minimum Qualifications
BS or MS in CS, EE, or related degree.
3+ years of industry experience in deep learning through applied research roles.
Deep understanding of Machine Learning fundamentals.
Proficiency in Python and at least one deep learning framework (PyTorch, TensorFlow, or JAX).
Collaborative skills for dependable and consistent steering of novel research alongside fellow teams.

Preferred Qualifications
Ph.D. in CS, EE or a related field.
Advanced background and hands-on experience in speech ML technology (e.g., multi-modals, speaker embeddings, voice isolation, ASR, multichannel sensor fusion, generative speech).
Background in digital signal processing (DSP) for audio signals.
Experience training large models using both supervised and self-supervised methods.
Track record of shipping ML features in a production environment.

Requirements:
Minimum Qualifications
M.Sc. in Computer Science or a related field, or equivalent practical experience.
Deep understanding of Machine Learning fundamentals.
Proficiency in Python and at least one deep learning framework (PyTorch, TensorFlow, or JAX).
3+ years of industry experience in deep learning through applied research roles.
Hands-on experience with the full deep learning lifecycle at scale, including dataset curation, architecture design, distributed training, error analysis, and production deployment.
M.Sc. in Computer Science or a related field, or equivalent practical experience.

Preferred Qualifications
Ph.D. in Computer Science or a related field.
Advanced background and hands-on experience in speech technology (e.g., ASR, TTS, speaker recognition).
Background in NLP, including language modeling, text processing, and linguistic understanding.
Experience training large models using both supervised and self-supervised methods.
Track record of shipping ML features in a production environment.

Requirements:
Minimum Qualifications
M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or a related field - or equivalent practical experience.
Strong foundation in deep learning theory and hands-on experience training large-scale models.
Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow/JAX.
Hands-on experience with model compression and optimization techniques (quantization, pruning, distillation, etc.).
Familiarity with on-device inference frameworks such as Core ML, TensorFlow Lite, ONNX Runtime, or TensorRT.
Experience working with multimodal data (e.g., images, audio, time-series, or sensor fusion).
Strong analytical and problem-solving skills; ability to translate research ideas into production-quality code.

Preferred Qualifications
Experience deploying models to embedded systems, mobile devices, or custom silicon (NPU/DSP).
Familiarity with hardware-aware neural architecture search (NAS) or AutoML techniques.
Exposure to low-level optimization techniques such as mixed-precision training or operator fusion.
Hands-on experience with our Neural Engine and Core ML for on-device inference.
Publications or open-source contributions in efficient deep learning or edge AI.
Experience with MLOps workflows and CI/CD pipelines for model development.