Technical leadership in semiconductor defect inspection, data analytics, and development of advanced defect detection and classification capabilities to improve product quality, process control, and manufacturing yield.
Compensation
The expected annual pay range for this position is $120,000 - $160,000.
Responsibilities
Develop image-processing algorithms and AI/ML models to detect, classify, and quantify semiconductor defects such as particles, pits, and scratches.
Analyze large datasets from wafer inspection, metrology, and MOCVD processes to identify trends, correlations, anomalies, and root causes.
Develop automated analysis and visualization workflows using Python, SQL, JMP, Excel, and related tools.
Correlate incoming substrate defects with epitaxial defects and develop data-driven methods for substrate quality screening.
Integrate defect, process, equipment, and wafer-level data to improve understanding of defect mechanisms and yield drivers.
Develop software solutions for calibration and optimization of quantum dot devices, building robust routines for tuning, calibrating gates, and maintaining peak performance in real time.
Compensation
The base salary for this position is targeted between $150K - $225K and will ultimately be determined by specific job-related skills, experience, level, location and other contributing factors. Depending on the position offered, this role may be eligible for annual discretionary bonuses, equity and other forms of compensation outside of the targeted range.
Responsibilities
Perform cryogenic measurements and characterization of quantum dot-based qubits.
Invent, implement, and test novel control protocols for all the required bring-up steps, starting from coarse tuning to optimizing two-qubit gates.
Develop scalable protocols for processor-wide optimization (e.g., cross-talk mitigation, leakage reduction, and defect mitigation) and real-time retuning.
Collaborate with your colleagues in the engineering and product teams to translate ideas and research outcomes into production-level software.
Explain, document, and present research solutions in accessible language.
Requirements
PhD in Physics, Engineering or a closely related discipline
The custodian of the AI and Data Science craft across X, bigly labs, spanning Data Science, Machine Learning, AI Engineering and MLOps/ LLMOps. The role independently leads day-to-day technical direction, craft operations and delivery assurance across tribes and domains.
Responsibilities
Define and maintain standards for data science, AI engineering, ML/LLM lifecycle, experimentation, documentation, version control, and deployment
Maintain role profiles, competency frameworks and technical expectations for Data Scientists, AI Engineers and ML/MLOps practitioners
Set quality gates for data suitability, model validation, bias and robustness checks, LLM evaluation, security, deployment readiness, and post-release monitoring
Establish reproducible development and deployment practices, model/experiment tracking, registries, release controls, and monitoring with platform teams
Establish and run AI and Data Science communities of practice with charters, roadmaps, and practical knowledge-sharing
Lead RF Data Scientist responsible for developing RF statistical‑learning techniques, leading experiments, and translating results into operational capabilities.
Locations (3)
Clarksburg, Maryland, United States
Arlington, Virginia, United States
Durham, North Carolina, United States
Compensation
US Salary Range $175,000—$220,000
Responsibilities
Lead RF technique development for detection, classification, parameter estimation, and complex signal environment understanding
Define technique inputs, targets, loss functions, uncertainty outputs, and decision rules for real RF collections
Build disciplined data practice including versioned datasets, label quality rules, leakage checks, and reproducible training and evaluation recipes
Software Engineer, App Ads Bidding and Optimization
Develop and optimize advertising technology for Google Ads, focusing on bidding algorithms, machine learning models, and data analysis to improve campaign performance.
Improve the accuracy and stability of the UAC bidder with statistical analysis and new ML/optimization algorithms
Improve/modernize bidding infrastructure and design and implement bidding algorithms for faster cold start for new campaigns
Debug inaccuracies in bidding infrastructure and suggest improvements and launch with design, implementation, experimentation and analysis
Incorporate and engineer input features from ad creatives, publisher pages/content, as well as users’ personalized ad history to improve bidding and modeling accuracy
Improve the efficiency and reliability of ML models by designing novel model architectures and adopting robust development and release processes
Requirements
Bachelor's degree in Computer Science or related technical field
Collaborate with traders, developers, and researchers to evaluate, validate, and improve quantitative models used in IMC’s proprietary trading strategies through statistical analysis, simulation, and empirical research.
Locations (2)
Chicago, Illinois, United States
New York, New York, United States
Compensation
$175,000—$275,000 USD
Responsibilities
Collaborate with traders, developers, and researchers to evaluate, validate, and improve quantitative models used in IMC’s proprietary trading strategies through statistical analysis, simulation, and empirical research
Design quantitative methods to gather and analyze market data, trading statistics, or new data sources and use the result of that research as the basis for new trading strategies
Build research-oriented computational tools, simulations, visualizations, and backtesting frameworks to evaluate quantitative hypotheses and measure model performance before production deployment
Formulate trading hypotheses as mathematical, statistical, and machine-learning models; prototype algorithms and coordinate with developers to integrate validated research into trading systems
Apply statistical inference, optimization, numerical methods, and financial mathematics to diagnose model behavior, improve forecasting accuracy, and address complex research problems in proprietary trading
Requirements
Bachelor's degree in Computer Science, Financial Mathematics, Financial Engineering, Computational Mathematics, or a closely related quantitative, mathematical, or computational field
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