As a Lead - Customer Success Management, you will play a crucial role in supporting Checkmate's clients in maximizing their digital performance and ensuring their overall success with all Checkmate products. This is a dynamic role where you'll contribute to both strategic services for our First-Party Ordering products (Loyalty & Marketing, Kiosks, Catering, Web/App Ordering, and the Design Editor) and general customer success management across the entire Checkmate suite. You'll work directly with enterprise restaurant brands, primarily in the US, providing hands-on support and helping them leverage Checkmate's solutions effectively. Checkmate operates in a fast-paced environment where our company's needs and strategy can evolve quickly. This role requires a highly adaptable individual who is comfortable with change and eager to contribute to new initiatives as they arise. You will start as an individual contributor, learning our service offerings, assisting with client onboarding, and providing support for a portfolio of accounts. You'll also act as a cross-functional connector - liaising with Product, Marketing, Sales, and Customer Success teams to ensure consistent client outcomes and clear communication. Essential Job Functions: Agency Services & Strategy Support: Oversee the implementation of service offerings for Loyalty & Marketing, Web/App Ordering, Kiosk, Catering, and other Checkmate products. Assist in using customer data across channels to inform campaign effectiveness and digital revenue growth Help prepare materials that support ongoing success for enterprise clients Customer Success Management Act as a key support contact for a portfolio of enterprise clients, assisting them with the adoption and optimization of all Checkmate products Provide clients with product demonstrations, training, and best practices to help them maximize product usage Be able to provide hands-on support and training for key marketing tools, including email, segmentation, customer journeys, and promotional events Assist in presenting data-driven insights and building client trust Proactively identify and escalate client challenges, contributing to high levels of satisfaction and retention across all Checkmate product usage Identify opportunities to enhance client engagement and encourage the adoption of additional products or services Reporting and Documentation: Maintain accurate and up-to-date records of client interactions, feedback, and status updates in the CRM system Prepare and present basic reports on client activity and satisfaction levels Monitor client satisfaction levels and escalate concerns to senior team members when necessary Adaptability & Process Contribution: Proactively adapt to evolving company needs and strategic shifts Contribute to building internal documentation and support resources Requirements 3 - 5 years of experience in client support, customer service, or marketing coordination - ideally within B2B SaaS, loyalty/CRM platforms, or consumer-facing industries like food tech, travel, or retail Bachelor's degree from a recognized University or a related field (or equivalent experience) Excellent communication, interpersonal, and analytical skills with the ability to engage effectively with clients and internal teams Superior organizational and time management skills, with a capacity to handle multiple highpriority projects simultaneously Familiarity with tools like Asana, Trello, MS Project, or similar for managing projects and tracking progress Familiarity with CRM platforms (e.g., Hubspot) and customer success tools Ability to adapt to changing environments and manage multiple priorities in a fast-paced setting The candidate must be flexible to work in the US shift hours at least till 5 pm EST is essential for this role Candidate must have their own system for remote work

We're seeking a Mid-Level Machine Learning Engineer to join our growing Data Science & Engineering team. In this role, you will design, develop, and deploy ML models that power our cutting-edge technologies like voice ordering, prediction algorithms and customer-facing analytics. You'll collaborate closely with data engineers, backend engineers, and product managers to take models from prototyping through to production, continuously improving accuracy, scalability, and maintainability. Essential Job Functions Model Development: Design and build next-generation ML models using advanced tools like PyTorch, Gemini, and Amazon SageMaker - primarily on Google Cloud or AWS platforms Feature Engineering: Build robust feature pipelines; extract, clean, and transform largescale transactional and behavioral data. Engineer features like time- based attributes, aggregated order metrics, categorical encodings (LabelEncoder, frequency encoding) Experimentation & Evaluation: Define metrics, run A/B tests, conduct cross-validation, and analyze model performance to guide iterative improvements. Train and tune regression models (XGBoost, LightGBM, scikit-learn, TensorFlow/Keras) to minimize MAE/RMSE and maximize R² Own the entire modeling lifecycle end-to-end, including feature creation, model development, testing, experimentation, monitoring, explainability, and model maintenance Monitoring & Maintenance: Implement logging, monitoring, and alerting for model drift and data-quality issues; schedule retraining workflows Collaboration & Mentorship: Collaborate closely with data science, engineering, and product teams to define, explore, and implement solutions to open-ended problems that advance the capabilities and applications of Checkmate, mentor junior engineers on best practices in ML engineering Documentation & Communication: Produce clear documentation of model architecture, data schemas, and operational procedures; present findings to technical and non-technical stakeholders Requirements Academics: Bachelors/Master's degree in Computer Science, Engineering, Statistics, or related field Experience: 5+ years of industry experience (or 1+ year post-PhD). Building and deploying advanced machine learning models that drive business impact Proven experience shipping production-grade ML models and optimization systems, including expertise in experimentation and evaluation techniques. Hands-on experience building and maintaining scalable backend systems and ML inference pipelines for real-time or batch prediction Programming & Tools: Proficient in Python and libraries such as pandas, NumPy, scikit-learn; familiarity with TensorFlow or PyTorch. Hands-on with at least one cloud ML platform (AWS SageMaker, Google Vertex AI, or Azure ML). Data Engineering: Hands-on experience with SQL and NoSQL databases; comfortable working with Spark or similar distributed frameworks. Strong foundation in statistics, probability, and ML algorithms like XGBoost/LightGBM; ability to interpret model outputs and optimize for business metrics. Experience with categorical encoding strategies and feature selection. Solid understanding of regression metrics (MAE, RMSE, R²) and hyperparameter tuning. Cloud & DevOps: Proven skills deploying ML solutions in AWS, GCP, or Azure; knowledge of Docker, Kubernetes, and CI/CD pipelines Collaboration: Excellent communication skills; ability to translate complex technical concepts into clear, actionable insights Working Terms: Candidates must be flexible and work during US hours at least until 6 p.m. ET in the USA, which is essential for this role & must also have their own system/work setup for remote work