Checkmate

Data Scientist

• Prompt Design & Evaluation: Develop, test, and refine prompts for tasks such as text generation, question answering, data classification, and structured data extraction to optimize Voice AI performance.
• Data-Driven Analysis & Quality Measurement: Design evaluation frameworks and analyze prompt outputs using quantitative metrics, human-in-the-loop evaluation, and user feedback to identify improvement opportunities.
• Experimentation & Iteration: Conduct experiments to test prompt variations, measure their business and operational impact, and iterate to enhance accuracy, consistency, and safety.
• Regression Testing & Compliance: Build principled regression test suites using tools like LangFuse and Galileo to ensure prompts remain compliant and high performing as models and use cases evolve.
• Collaboration Across Teams: Work closely with data science, product, legal, engineering, and operations teams to align prompt designs with business goals, operational workflows, and compliance requirements.
• Model Adaptation & Strategy: Develop prompts across multiple LLMs (GPT, LLaMA, Gemini, and Checkmate’s fine-tuned models), understanding model differences to optimize outputs effectively.
• Team Leadership & Mentorship: To lead a team of analysts focused on prompt evaluation and data quality analysis, guiding prioritization, experimentation, and reporting. I collaborate with ops teams for seamless deployment and feedback loops.
• Research & Continuous Learning: Stay current on emerging prompting techniques, LLM behaviors, evaluation frameworks, and AI safety practices to keep Checkmate’s AI solutions best-in-class.

Prompt Engineer – Data Science, Quality Analysis

Backend Engineer

• API Development & Integration: Design and build new REST API endpoints using Flask and the OpenAPI specification. You will also manage complex integrations with various external and internal third-party APIs (e.g., payments, loyalty, other services).
• Performance Optimization: Proactively identify and resolve performance bottlenecks. You'll use tools like Datadog APM, advanced SQL query optimization, indexing, and caching (e.g., Redis) to ensure a low-latency, high-performance end-user experience.
• Database & Core Logic: Work directly with MySQL without an ORM, writing and optimizing complex, custom SQL queries to interact with a database featuring hundreds of tables.
• System Modernization & Quality: Refactor and modernize parts of a large, 10-year-old codebase. You will also take ownership of expanding our automated test suite to ensure robustness and quality.
• Problem Solving: Track down and resolve complex bugs across the application.
• Feature Expansion: Build new backend functionalities that directly expand the feature set of our consumer-facing products.

Senior Python Developer

• Architect and build the conversation orchestration service: ASR → LLM inference → TTS streaming in real time
• Write robust, asynchronous Python code designed to handle high concurrency without deadlocks, race conditions, or memory leaks
• Design and maintain clean, well-structured APIs for future scalability and ease of debugging
• Manage interaction data using SQL Alchemy (or equivalent) with efficient schema design and safe migrations
• Implement observability: structured logging, metrics, and tracing across the system for instant issue diagnosis
• Partner with ML and Product teams to rapidly iterate on conversation flow and user experience
• Enforce a strong testing culture: automated unit tests, E2E flows, and load testing
• Build resilient systems capable of handling real-world edge cases like noisy audio, unreliable APIs, and flaky networks
• Continuously profile, optimize, and reduce latency and response times**

Senior Python Developer, Voice AI

• Architect and build real-time conversation orchestration service: ASR → LLM inference → TTS streaming
• Write robust, asynchronous Python designed for high concurrency without deadlocks, race conditions, or memory leaks
• Design and maintain clean, well-structured APIs for scalability and debugging
• Manage interaction data using SQLAlchemy or equivalent with efficient schema design and safe migrations
• Implement observability: structured logging, metrics, and tracing across the system
• Partner with ML and Product teams to iterate on conversation flow and user experience
• Enforce a strong testing culture: automated unit tests, E2E flows, and load testing
• Build resilient systems handling noisy audio, unreliable APIs, and flaky networks
• Continuously profile, optimize, and reduce latency and response times
• Monitor, debug, and improve the system as it runs in production

Senior Python Developer, Voice AI Platform

Full-stack Engineer

• Develop machine learning algorithms, focusing on Large Language Models (LLMs) like GPT, to improve accuracy and performance.
• Work with teams to turn business requirements into AI solutions.
• Use LLMs for restaurant-specific applications like real-time text generation and recommendation engines.
• Monitor and improve model performance.
• Stay up to date on the latest in machine learning and contribute to research and patents.
• Experiment with new methods like transfer learning and reinforcement learning.
• Collaborate with partners to integrate the latest AI innovations.
• Build new products from scratch and influence product direction and engineering roadmap.
• Improve customer interactions and ensure seamless, delightful experiences.

Senior Software Engineer, AI/LLM

Systems Engineer

• Manage, maintain, and evolve Linux-based in-store systems for Digital Menu Boards
• Design, package, and distribute software for Linux-based Digital Menu Board (DMB) devices
• Troubleshoot system-level issues using syslog, journalctl, etc., to ensure device stability and uptime
• Manage and optimize graphical systems (X, Wayland) and desktop environments (GNOME, KDE)
• Collaborate with frontend and backend teams to integrate dynamic menu board features
• Implement and maintain monitoring solutions (Prometheus, Grafana, Datadog, or similar) with custom metrics for device performance and health
• Contribute to hardening, security, and update processes for in-store Linux devices
• Leverage web development skills to support interactive and dynamic menu board experiences

Linux System Engineer

Technical Product Manager

• Lead development of next-generation Voice AI products for the restaurant industry (drive-thru and phone/take-out).
• Combine product strategy, rapid prototyping, light engineering, and UX research to bring ideas from concept to market.
• Work closely with engineers and data scientists to design, test, and iterate conversational AI capabilities.
• Translate product vision into clear, actionable roadmaps and delivery plans for Voice AI solutions.
• Gather and prioritize requirements from customers, internal stakeholders, and market research.
• Define, scope, and launch features that improve AI accuracy, expand coverage, and enhance operational performance.
• Set KPIs and success metrics, monitor performance, and adjust priorities based on data insights.
• Rapidly prototype and build proof-of-concepts to validate ideas before full-scale development.
• Conduct competitive analysis and stay ahead of trends in LLMs, speech recognition, and conversational AI.
• Manage timelines, dependencies, and trade-offs to ensure successful product launches.
• Continuously refine the product based on performance metrics and customer feedback.

Technical Product Manager - Voice AI

LLM Engineer

• We are seeking a highly skilled AI / LLM Engineer to help advance our Drive Through AI initiatives.
• Create, fine-tune, and deploy AI models that enhance restaurant ordering, streamline operational workflows, and improve system intelligence.
• Collaborate closely with product managers, ASR specialists, and prompt engineering teams to provide tools, integrations, and model improvements.
• Apply cutting-edge Large Language Models (LLMs) and NLP in real-world, high-volume environments for accurate understanding and intelligent responses.
• Build and maintain model infrastructure, APIs, and integrations supporting prompt engineering and audio processing pipelines.
• Fine-tune and adapt pre-trained LLMs (e.g., GPT, LLaMA, Falcon) for the QSR domain.
• Use observability tools like Langfuse to monitor, evaluate, and troubleshoot model performance in production.
• Embed AI models into production-grade systems connected to order management, displays, and kitchen workflows.
• Ensure solutions are efficient, scalable, and cost-effective for high-throughput, continuous operations.
• Stay current with LLM architectures, RAG, and generative AI techniques to improve product capabilities.
• Maintain documentation of model architectures, training data sources, and evaluation metrics for SOC 2 and compliance.