Field Service Engineer 3

The group you’ll be a part of

The impact you’ll make

What you’ll do

• Perform FDC (Fault Detection & Classification), Inform Note, and wafer performance data analysis on semiconductor etch equipment (CE/DE and DEP) to identify subsystem-level failure patterns and anomaly signatures. 

  Conduct feature engineering, statistical analysis (PCA, Mahalanobis Distance), and multivariate sensor screening using EI-DA platform tools all SVID parameters and multiple process steps to isolate predictive indicators.

  Design, train, and validate PdM models targeting subsystems, with target precision > (Target score) as defined by customer KPI. 

  Lead customer co-validation sessions to review model outputs, refine detection thresholds, reduce false positives, and drive consensus on PdM deployment readiness through bi-weekly working group meetings.

  Develop and maintain SVID data standardization documentation (Ontology/Dictionary) to support customer LLM training, internal analytics systems, and cross-chamber model scalability. 

  Explore and evaluate emerging AI solution opportunities including Smart Scheduler optimization (JIT/TPO), edge computing for real-time inference, gRPC-based high-frequency data collection (1000Hz), and RUL (Remaining Useful Life) modeling. 

  Maintain weekly model performance dashboards, action trackers, and customer validation logs to ensure transparency and cadence of deliverables.

  Collaborate cross-functionally with EI Apps, Software & Control, and Product Group (PG) teams to align PdM roadmap, sensor strategy, and EI-DH infrastructure deployment.

Who we’re looking for

Minimum Qualifications:

• Bachelor's degree in Electrical Engineering, Computer Science, Data Science, Physics, or related field and 5+ years of experience; or Master's degree with 3+ years; or equivalent work experience.

  Previous experience in data analytics, machine learning model development, or equipment diagnostics in semiconductor manufacturing or related high-tech industry.

  Demonstrated proficiency in Python-based data analysis and ML/DL frameworks (e.g., scikit-learn, XGBoost, TensorFlow, or PyTorch) for time-series anomaly detection, classification, or predictive modeling.

  Working knowledge of semiconductor etch equipment subsystems (RF delivery, gas systems, ESC, vacuum, thermal control) or willingness to develop deep domain expertise.

  Experience with statistical methods including PCA, multivariate analysis, Mahalanobis Distance, and feature engineering from high-dimensional sensor data.

  Able to work in a clean room environment while wearing personal protective safety equipment or cleanroom suit.

  Able to travel domestically and internationally based on customer needs, with flexibility to work a variety of shifts including compressed and alternative workweeks.

Preferred qualifications

• Experience with FDC/SPC systems, SECS/GEM or EDA2 semiconductor communication protocols, and equipment sensor data in HVM fab environments.

  Familiarity with EI (Equipment Intelligence) analytics platforms, Data Hub architectures, or similar big-data tool log analytics systems.

  Knowledge of NLP/LLM concepts, ontology design, or knowledge graph construction for equipment parameter documentation.

  Communication skills, both written and verbal, in English and Korean.

  Experience presenting technical findings to cross-functional and customer audiences in a collaborative validation setting.

Our commitment

We believe it is important for every person to feel valued, included, and empowered to achieve their full potential. By bringing unique individuals and viewpoints together, we achieve extraordinary results.

Lam Research ("Lam" or the "Company") is an equal opportunity employer. Lam is committed to and reaffirms support of equal opportunity in employment and non-discrimination in employment policies, practices and procedures on the basis of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex (including pregnancy, childbirth and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, or military and veteran status or any other category protected by applicable federal, state, or local laws. It is the Company's intention to comply with all applicable laws and regulations. Company policy prohibits unlawful discrimination against applicants or employees.

Lam offers a variety of work location models based on the needs of each role. Our hybrid roles combine the benefits of on-site collaboration with colleagues and the flexibility to work remotely and fall into two categories – On-site Flex and Virtual Flex. ‘On-site Flex’ you’ll work 3+ days per week on-site at a Lam or customer/supplier location, with the opportunity to work remotely for the balance of the week. ‘Virtual Flex’ you’ll work 1-2 days per week on-site at a Lam or customer/supplier location, and remotely the rest of the time.