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Requirements:
Bachelor's degree in Electrical Engineering
7+ years of experience in board design working on multidisciplinary products
Experience in board design tools, preferably Siemens EDM / Xpedition
Deep understanding of Signal Integrity (SI) and Power Integrity (PI)
Experience in digital circuits design, power design, and analog circuit design
Strong lab debugging & root-cause analysis skills using tools such as oscilloscopes and signal analyzers for problem solving.
Experience with DDR design constraints and validation
Familiarity with PLM tools, especially Windchill
Advantages
Experience with FPGA-based systems
Experience in medical devices or other regulated industries
Background in thermal design and power optimization

Requirements:
B.Sc. in Mechanical or Materials Engineering or B.Sc. in Physics (MSc - advantage).
7+years of experience in process development of detectors assembly.
Experience working with Automatic Assembly systems.
Practical experience with Die Attach and Epoxy/Adhesive Dispensing.
Strong understanding of DFM/DFA principles.
A strong team player with an analytical mindset, comfortable working on long-term, innovative R&D projects where solutions aren't always "off-the-shelf."
Fluent English (written and verbal) for communication within a global organization.

Requirements:
What You Bring
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related engineering discipline, or equivalent practical experience.
5+ years of professional software engineering experience, including substantial experience building backend systems, distributed systems, or data-intensive applications in production environments.
Strong Python engineering skills, including modular architecture, dependency management, testing practices, observability, and production-grade code quality.
Strong SQL and data modeling expertise, including schema design, indexing strategies, event-driven data models, and scalable analytical aggregations.
Hands-on experience building incremental and idempotent data pipelines that handle duplicate, invalid, and late-arriving events without impacting downstream consumers.
Experience with at least one major cloud platform (Azure, GCP, or AWS) and modern lakehouse technologies such as Databricks, Delta Lake, Spark, or equivalent architectures.
Experience with streaming or messaging technologies such as Kafka, Pub/Sub, Event Hubs, or similar event-driven systems.
Proven ability to diagnose and resolve production data issues, including data quality problems, schema evolution, backfills, replay scenarios, and performance bottlenecks.
Strong written and verbal communication skills in English and experience collaborating effectively with globally distributed teams.

Nice to Have
Experience building industrial, IoT, manufacturing, or operational data platforms.
Familiarity with Digital Twin architectures and industrial data models.
Experience with graph databases, context graphs, knowledge graphs, or relationship-centric data modeling.
Exposure to AI/LLM-powered applications, including retrieval-augmented generation (RAG), agents, tool calling, or evaluation frameworks.
Experience working with Databricks or similar lakehouse platforms from both application and platform perspectives.
Experience building data products that directly support AI agents, intelligent applications, or machine learning workflows.

Requirements:
What You Bring
5+ years of professional software engineering, MLOps, or ML platform engineering experience in production environments.
Significant experience building or owning production ML infrastructure and lifecycle systems.
Strong Python engineering skills with production-grade architecture, modular design, testing, packaging, and robust error handling.
Strong understanding of the end-to-end ML lifecycle including training, deployment, monitoring, retraining, reproducibility, and lineage.
Experience working with large-scale data platforms such as Databricks, Spark, Delta Lake, or equivalent ecosystems.
Experience with ML platform and MLOps frameworks such as MLflow, Metaflow, Kubeflow, or equivalent ML lifecycle-management systems.
Proven ability to design reusable workflow orchestration using Airflow, Metaflow, or Databricks, covering automation, scheduling, dependency management, and production reliability.
Familiarity with operational patterns for LLMOps, AgentOps, and production AI systems.
Strong written and verbal communication skills in English.

Nice to Have
Experience with industrial, IoT or manufacturing platforms.
Experience with feature stores, model registries, dataset versioning, and lineage systems.
Experience with AI agents, RAG systems, production GenAI applications, or evaluation frameworks.

Requirements:
You Will Bring
6+ years of professional software engineering experience.
Strong Python development skills.
Strong backend engineering background, including APIs, services, integrations, or microservices.
Proven experience designing, shipping, or operating LLM-powered applications or GenAI systems.
Experience with RAG, AI agents, tool/function calling, prompt orchestration, evaluation, and observability.
Experience with microservices, distributed systems, and production backend architecture.
Strong understanding of system design, reliability, security, scalability, latency, and maintainability.
Ability to work with complex data models and expose them safely through AI systems.
Ability to operate in ambiguous technical areas and turn prototypes into production-ready systems.
Strong communication skills and ability to explain technical decisions clearly.

Preferred Qualifications
Experience with LangChain, LangGraph, or LangSmith.
Experience with Go, MongoDB, Databricks, Kubernetes, Docker, REST APIs, vector search, or event-driven systems.
Experience with Azure OpenAI, Gemini, or similar LLM platforms.
Experience building or using knowledge graphs, GraphRAG, or graph databases such as Neo4j.
Experience building multi-agent systems or orchestrating multiple specialized agents/tools in production.
Experience building internal platforms, SDKs, developer tools, or shared engineering infrastructure.
Experience with authorization, data segregation, multi-tenant systems, or user-level data scoping.
Experience in industrial, IoT, predictive maintenance, manufacturing, or operational-data domains.

Requirements:
Advanced degree (M.Sc. or Ph.D.) in Physics, Electrical Engineering, Computer Science, Mathematics, or a related quantitative field.
Over 4+ years of experience, demonstrated expertise in DSP-based methods (e.g., STFT, FFT, Hilbert, Wavelet, etc.), to extract features and anomalies from the data.
Over 2 years of professional experience in ML/AI, with a proven track record in modeling and analyzing complex time-series data.
Proven technical leadership experience in large-scale, multidisciplinary projects.
Hands-on experience working with real-world physical data and sensor signals.
High proficiency in Python for the full development lifecycle, including model deployment and performance monitoring.
Ability to independently research, architect, and implement innovative technological solutions.
Prior experience in the industrial or manufacturing sector is advantageous.
Capability to translate theoretical research into robust, scalable, and production-ready systems.
Experience thriving in agile environments with a focus on rapid iteration, continuous feedback, and professional growth.
Excellent communication skills for collaborating with cross-functional teams, including Product Management and Engineering.

Requirements:
Advanced degree (M.Sc. or Ph.D.) in Physics, Electrical Engineering, Computer Science, Mathematics, or a related quantitative field.
Over 4+ years of experience, demonstrated expertise in DSP-based methods (e.g., STFT, FFT, Hilbert, Wavelet, etc.), to extract features and anomalies from the data.
Over 2 years of professional experience in ML/AI, with a proven track record in modeling and analyzing complex time-series data.
Proven technical leadership experience in large-scale, multidisciplinary projects.
Hands-on experience working with real-world physical data and sensor signals.
High proficiency in Python for the full development lifecycle, including model deployment and performance monitoring.
Ability to independently research, architect, and implement innovative technological solutions.
Prior experience in the industrial or manufacturing sector is advantageous.
Capability to translate theoretical research into robust, scalable, and production-ready systems.
Experience thriving in agile environments with a focus on rapid iteration, continuous feedback, and professional growth.
Excellent communication skills for collaborating with cross-functional teams, including Product Management and Engineering.

Requirements:
What You Bring
Agentic-First Mindset & Proven Experience as an AI-Native Engineer utilizing tools like Claude, Cursor, or Gemini to enhance productivity and quality in commercial development.
5+ years of mobile development experience with proficiency in Flutter/Dart and native iOS/Android, BLoC state management, and designing scalable mobile architecture.
Comfortable with full-stack contributions to backend and frontend services/APIs to ensure end-to-end feature ownership. Proven ability to write high-quality, testable Dart code with cross-platform (iOS/Android) and RTL familiarity.

Strong Advantage
Experience with industrial IoT and field-service applications, including device commissioning workflows, hands-on IoT connectivity (Wi-Fi, NFC, BLE, and Beacons APIs), and familiarity with AR and Indoor Navigation.
Familiarity with Firebase, third-party support tooling (Intercom), and Offline/Sync capabilities.

Requirements:
M.Sc., or Ph.D. in Electrical Engineering, Computer Science, Physics, Mathematics, or a related field.
4+ years of experience in ML applications, including research on time-series data, ML-based vectoring, and Embeddings (as Transformers, TGNN, TSFM, Graph Transformer, GNN, CNN, etc.)
Proficiency in Python (Pytorch, Pandas, NumPy, PyG - nice to have), and Applied Science SDLC - Discovery, Research, development, fidelity, deployment, monitoring, Training, and fine-tuning
Experience in anomaly detection, abnormal behavior, outliers, pushing recall, precision, and tail metrics KPIs boundaries
Experience working with ML infrastructure such as MLFlow, Vertex AI, Sagemaker etc.
The ability to translate research into scalable, production-ready solutions.
Experience working on Agile teams with a passion for fast iterations, feedback, and continuous learning.
Proven ability to collaborate with diverse, cross-functional groups, including product managers, infrastructure, and data engineering teams.
Experience in feature engineering-based signal processing - a big advantage.

Requirements:
M.Sc., or Ph.D. in Electrical Engineering, Computer Science, Physics, Mathematics, or a related field.
5+ years of experience in ML applications in modeling Time-series data, & ML-based vectoring, and Embeddings for insight & recommendations systems (Transformers included) - Mandatory.
2+ years of experience in LLM and Agentic applications, Eval tools, methods, and workflows (HITL, LLM-as-a-judge, deterministic, Metrics & Embeddings), Fine-Tuning, and AI workflows and lifecycle (e.g. LangSmith, CrewAI, LangGraph, Embedding (BERT, w2v, FastText, FastEmbed, GloVe, etc.), Tokenizations (GPT. TikToken, Token validators, etc.), & Vector Similarities & search methods.
Proficiency in Python for model development, deployment, and monitoring.
Experience working on Agile teams with a passion for fast iterations, feedback, and continuous learning.
Proven ability to collaborate with diverse, cross-functional groups, including product managers, infrastructure, and data engineering teams.
The ability to translate research into scalable, production-ready solutions.
Experience in anomaly detection and AI/ML - an advantage.
Experience in feature engineering-based signal processing - an advantage.
Experience in optimizing costs of API calls - Nice to have.

Requirements:
Basic Qualifications
- Bachelors (or higher) Degree in Computer Science (CS), Electrical Engineering (EE) or related area.
- 8+/10+ years of programming with at least one software programming language experience.

Preferred Qualifications
- Experience as a mentor, tech lead or leading an engineering team.
- Experience leading the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.
- Data-center Networking.
- Network related simulators.
- Large-scale distributed environments.
- Storage and/or Transport protocols.
- Real-Time development.

Requirements:
Basic Qualifications
- B.Sc. degree (M.Sc. or Ph.D. an advantage) in Electrical Engineering, Applied Physics, Materials Science or related fields with practical work experience.
- Experience in semiconductor quality & reliability test definition, execution and analysis.
- Experience in leading a team of IC quality and reliability engineers.
- Deep knowledge in semiconductor physics, VLSI IC design, manufacturing, testing and FA, reliability engineering and statistics fundamentals.
- Good knowledge of semiconductor fabrication process.
- Experience in IC and system-level testing and debug.

Preferred Qualifications:
- Familiar with semiconductor industry production flows.
- Experience working with OSATs (Outsourced Semiconductor Assembly and Test) and ODMs (Original Design Manufacturers).
- Opportunity to relocate in the future (US).

Requirements:
Basic Qualifications:
- Bachelor's degree in electrical engineering or equivalent.
- 8+ years of experience developing and supporting hardware products.
- Demonstrated experience leading cross-functional efforts across HW, FW, test, and manufacturing teams.
- Working knowledge of server or complex electronic system architecture (PCB, power delivery, high-speed interfaces such as DDR4/5, PCIe Gen3/4/5).
- Hands-on experience with manufacturing test development, debug, and data analysis at scale.
- Familiar with scripting (Python/Bash) for practical uses such as data analysis.

Preferred Qualifications:
- Experience working directly with Contract Manufacturers (CMs) or ODMs in a sustaining or production engineering capacity.
- Volume manufacturing - including yield management, failure analysis, root-cause investigation, and corrective/preventive action processes.
- Knowledge of networking, storage, or Linux at a system level.
- Track record of technical leadership in a matrixed, multi-site environment.