About the Role We are looking for an experienced Wearable Hardware Design Engineer with a strong background in miniaturizing consumer and medical-grade wearables. The ideal candidate will bring expertise in RF design and impedance matching , wireless power transmission systems , and flexible PCB (FPCB) design . You will work closely with cross-functional teams (mechanical, firmware, and industrial design) to develop next-generation ultra-compact wearable devices. Key Responsibilities Lead the end-to-end hardware design for wearable devices, from concept to production. Design, simulate, and optimize RF circuits and antennas with a focus on size, efficiency, and regulatory compliance. Develop wireless power transmission systems (inductive/ resonant coupling) optimized for wearables. Design flexible and rigid-flex PCBs , ensuring reliability under mechanical stress and tight packaging constraints. Collaborate with mechanical engineers to achieve seamless integration of electronics into ergonomic form factors. Work with manufacturing partners to ensure DFM (Design for Manufacturability) and scalability. Conduct lab testing, validation, and troubleshooting of prototypes, including RF and power performance tuning. Stay updated with new materials, miniaturization techniques, and standards relevant to wearable technologies. Required Qualifications Bachelor’s/Master’s in Electronics Engineering, Electrical Engineering, or a related field . 5+ years of experience in wearable or IoT device hardware design . Proven track record in RF design and antenna impedance matching for small form-factor devices. Strong experience in wireless power transfer (Qi, NFC, resonant coupling, or custom solutions). Hands-on expertise in flex PCB and rigid-flex PCB layout/design . Proficiency with design tools such as Altium Designer, HFSS, CST, ADS, or similar . Solid understanding of low-power electronics, signal integrity, and EMI/EMC considerations . Strong problem-solving skills and experience working in fast-paced, cross-disciplinary teams.
Role Overview: We are seeking a highly motivated Procurement Specialist with expertise in sourcing and procuring electronic components, sensors, PCBs, and related hardware for wearable and IoT devices. The ideal candidate will be responsible for identifying reliable suppliers, negotiating pricing and contracts, ensuring quality standards, and optimizing the supply chain to meet production timelines and cost targets. Key Responsibilities Source, evaluate, and procure electronic components including sensors, PCBs, flex PCBs, ICs, batteries, and other hardware. Identify and develop relationships with global suppliers and manufacturers , especially in China, Taiwan, and India. Manage RFQs, vendor negotiations, purchase orders , and contract terms to achieve cost efficiency. Work closely with the engineering and R&D teams to understand technical requirements and translate them into sourcing specifications. Ensure compliance with quality standards, certifications (RoHS, REACH, IEC, BIS, etc.) , and regulatory requirements. Monitor inventory levels, lead times, and supply risks , proactively mitigating shortages or delays. Evaluate and onboard alternate suppliers to reduce single-source dependency . Maintain accurate procurement records and develop cost analysis reports . Support new product introduction (NPI) by ensuring timely component availability. Qualifications Bachelor’s degree in Electronics, Supply Chain Management, or related field . 3+ years of experience in procurement/supply chain for electronics or hardware . Strong knowledge of electronic components, PCBs, and sensors . Experience in vendor management and global sourcing (China/East Asia experience preferred). Familiarity with import/export procedures, logistics, and customs clearance . Proficiency in Excel, ERP systems, and procurement tools . Excellent negotiation, communication, and problem-solving skills.
Company Description InnerGize is a mental health wearable company focused on revolutionizing mental wellness by combating stress. The company's mission is to help individuals find calm, feel better, and live fuller lives with effective solutions. InnerGize aims to provide everyone with the tools to take control of their well-being. About the Role We’re building next-gen hydrogel materials for biomedical and wearable applications. You’ll own formulation, characterization, and scale-up of stimuli-responsive, skin-safe hydrogels—working end-to-end from bench science to pilot manufacturing with QA/RA, product, and clinical teams. Key Responsibilities Formulation & Synthesis: Develop PEG/PVA/HA/alginate/chitosan/PNIPAM-based hydrogels; tune swelling, adhesivity, ionic conductivity, and mechanical properties. Crosslinking Chemistry: Design covalent/ionic/UV/enzymatic systems; optimize gelation kinetics, residual monomer levels, and extractables/leachables. Characterization: Run rheology, DMA/DSC/TGA, FTIR/NMR, SEM; model WVTR, diffusion coefficients, and drug-release kinetics. Biocompatibility & Safety: Plan ISO 10993 screens (cytotoxicity, irritation, sensitization, hemocompatibility) with external labs; draft protocols and reports. Process & Scale-Up: Transfer lab recipes to pilot lines; write batch records/SOPs; execute DoE; drive Cpk/yield improvements; support GMP readiness. Quality & Documentation: Maintain ELNs, specifications, CoAs, risk assessments; contribute to DHF/DMR and RA submissions. Cross-Functional Delivery: Partner with design/EE/ME for wearable patch integration (adhesives, electrodes, aging, shelf-life). Sustaining Engineering: Lead failure analysis (root cause, CAPA) and continuous improvement. Minimum Qualifications B.E./B.Tech or M.S./M.Tech in Polymer Science/Materials/Chemical Engg. 3–6 years hands-on hydrogel or soft-polymer formulation experience. Demonstrated proficiency in rheology and crosslinking chemistry (show 1–2 shipped formulations or pilot runs). Experience with ISO 10993 planning and report interpretation. Strong technical writing (SOPs, specifications, validation reports) and DoE/statistics literacy.
Company Description InnerGize is a mental health wearable company focused on revolutionizing mental wellness by combatting stress. The company aims to help individuals find calm, feel better, and lead fuller lives with effective solutions. InnerGize believes in empowering people to take control of their well-being. Role Description We are looking for a Bioelectronics Engineer with strong expertise in signal processing, algorithm development, and health parameter monitoring for IoT-based wearable devices. The role will focus on working with multimodal biosignals (PPG, GSR/EDA, Accelerometer, ECG, EEG), involving signal acquisition, preprocessing, and time-series analysis. You will work closely with hardware, data science, and product teams to translate raw biosignal data into actionable health insights, focusing on stress, sleep, cognitive performance, and overall well-being. The ideal candidate will have hands-on experience in experimental research and machine learning for biological signal processing. Requirements Academic: Master’s/PhD in Biomedical Engineering, Electrical/Electronics Engineering, Neuroengineering, or a related field. Job-Specific: Signal Processing Expertise: In-depth experience in signal preprocessing (filtering, noise reduction, artifact removal) for physiological signals such as PPG, ECG, EEG, GSR/EDA, and motion sensors. Time Series Analysis: Proficiency in handling time-series data, feature extraction, and analysis of dynamic biosignals. Hands-on Experimental Research: Direct experience with experimental research, including data collection, signal acquisition, and working with experimental hardware (sensors, wearable prototypes, etc.). Python Expertise: Strong proficiency in Python for data processing, algorithm development, and machine learning model implementation (libraries like NumPy, SciPy, pandas, scikit-learn, TensorFlow, PyTorch). Health Monitoring and DSP: Experience in Digital Signal Processing (DSP) and health parameter monitoring algorithms, specifically in applications related to biosignal analysis (heart rate variability, stress, sleep, cognitive performance). Project Deployment & Machine Learning: At least 3 years of experience contributing to health monitoring systems, including real-world deployment or regulatory trials, with a strong understanding of machine learning models and mathematical modeling for biosignal analysis.
As a Bioelectronics Engineer at InnerGize, you will utilize your expertise in signal processing, algorithm development, and health parameter monitoring to contribute to the development of IoT-based wearable devices focused on mental wellness. Your role will involve working with multimodal biosignals such as PPG, GSR/EDA, Accelerometer, ECG, and EEG, including signal acquisition, preprocessing, and time-series analysis. Collaborating closely with hardware, data science, and product teams, you will play a key role in translating raw biosignal data into actionable health insights, with a particular focus on stress, sleep, cognitive performance, and overall well-being. The ideal candidate will have a strong background in experimental research and machine learning for biological signal processing. To excel in this role, you should possess a Masters or PhD in Biomedical Engineering, Electrical/Electronics Engineering, Neuroengineering, or a related field. Your expertise should include in-depth knowledge of signal preprocessing techniques for physiological signals, proficiency in time-series data analysis, and hands-on experience with experimental research, including data collection and working with experimental hardware. Strong proficiency in Python programming for data processing, algorithm development, and machine learning model implementation using libraries such as NumPy, SciPy, pandas, scikit-learn, TensorFlow, and PyTorch is essential. Additionally, you should have experience in Digital Signal Processing (DSP) and health parameter monitoring algorithms, particularly in the context of biosignal analysis related to heart rate variability, stress, sleep, and cognitive performance. With a minimum of 3 years of experience in contributing to health monitoring systems, including real-world deployment or regulatory trials, you should possess a solid understanding of machine learning models and mathematical modeling for biosignal analysis. If you are passionate about leveraging your expertise to drive innovation in mental health wearable technology and are eager to make a meaningful impact on individuals" well-being, we invite you to join our dynamic team at InnerGize.,
InnerGize is a consumer health and wellness platform that uses wearable patches to help people manage stress, sleep and mental performance. We are building algorithms that make our product more accurate and user-friendly. We are looking for a Machine Learning Engineer with 2–4 years of experience to design and deploy convolutional neural network (CNN) algorithms that can detect and validate correct patch placement based on sensor data. This position focuses on developing and refining CNN-based models from data collection through to deployment. Job Requirements : Design, train, and optimize CNN architectures for correct wearable patch placement detection using available sensor data. Build and maintain data pipelines for collection, labeling, augmentation, and validation of training datasets. Evaluate model performance and iterate to improve accuracy and robustness across users. Prepare models for deployment on mobile or embedded systems (optimizing for latency and size). Document experiments, models, and performance metrics clearly for internal teams. Collaborate with data, product and firmware teams to integrate model outputs into the InnerGize ecosystem. Education Requirements: B.Tech / B.E. in Computer Science or a related field with specialization in Machine Learning, OR Master’s / Ph.D. in Machine Learning, Data Science, or Applied Mathematics (preferred). Experience: 2–4 years of professional experience developing machine learning models, with a strong focus on CNN architectures. Technical Skills: Proficiency in Python and frameworks such as PyTorch or TensorFlow. Demonstrated experience designing, training, and deploying CNNs for classification or regression tasks. Ability to work with real-world datasets, including preprocessing and augmentation. Familiarity with on-device or edge deployment tools such as TensorFlow Lite or ONNX (a plus). Soft Skills: Strong analytical and problem-solving skills. Good communication and documentation abilities. Comfortable working cross-functionally with other teams. Preferable Skills: Exposure to time-series or sensor-based data. Understanding of mathematical/statistical models for signal behavior. Prior experience deploying models on constrained devices. CTC: ₹12–18 LPA , commensurate with experience and expertise.
InnerGize is a consumer health and wellness platform that uses wearable patches to help people manage stress, sleep and mental performance. We are building algorithms that make our product more accurate and user-friendly. We are looking for a Machine Learning Engineer with 24 years of experience to design and deploy convolutional neural network (CNN) algorithms that can detect and validate correct patch placement based on sensor data. This position focuses on developing and refining CNN-based models from data collection through to deployment. Job Requirements : Design, train, and optimize CNN architectures for correct wearable patch placement detection using available sensor data. Build and maintain data pipelines for collection, labeling, augmentation, and validation of training datasets. Evaluate model performance and iterate to improve accuracy and robustness across users. Prepare models for deployment on mobile or embedded systems (optimizing for latency and size). Document experiments, models, and performance metrics clearly for internal teams. Collaborate with data, product and firmware teams to integrate model outputs into the InnerGize ecosystem. Education Requirements: B.Tech / B.E. in Computer Science or a related field with specialization in Machine Learning, OR Master's / Ph.D. in Machine Learning, Data Science, or Applied Mathematics (preferred). Experience: 24 years of professional experience developing machine learning models, with a strong focus on CNN architectures. Technical Skills: Proficiency in Python and frameworks such as PyTorch or TensorFlow. Demonstrated experience designing, training, and deploying CNNs for classification or regression tasks. Ability to work with real-world datasets, including preprocessing and augmentation. Familiarity with on-device or edge deployment tools such as TensorFlow Lite or ONNX (a plus). Soft Skills: Strong analytical and problem-solving skills. Good communication and documentation abilities. Comfortable working cross-functionally with other teams. Preferable Skills: Exposure to time-series or sensor-based data. Understanding of mathematical/statistical models for signal behavior. Prior experience deploying models on constrained devices. CTC: ?1218 LPA , commensurate with experience and expertise.