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About Mappa Mappa builds real-time voice-AI infrastructure that decodes over 100,000 behavior patterns per person—based on prosodic and linguistic signals—and turns them into actionable insights for hiring and beyond. Our data science team transforms raw audio into structured understanding at scale. Role Purpose Analyze and model complex behavioral and audio data sets to power Mappa’s AI systems. You’ll design and implement data pipelines, extract actionable insights, and prepare datasets for training cutting-edge machine learning models. Key Outcomes (First 6 Months) Deliver at least 3 key insights from exploratory analysis that inform product or model design. Build and deploy at least one production-grade ML-ready dataset using Airflow. Identify and implement ≥ 2 performance improvements in existing data pipelines. Collaborate with ML and Product to define data strategies for new features or behavior signals. Core Responsibilities Analyze voice, text, and behavioral datasets using statistical and ML methods. Prepare, clean, and transform data for downstream machine learning tasks. Design and maintain data pipelines using Airflow and Dockerized workflows. Contribute to model development using tools like Hugging Face and PyTorch. Collaborate with ML Engineers and Product to align data initiatives with product goals. Present findings and prototypes to cross-functional stakeholders, including CTO and COO. Monitor data health, quality, and coverage across all stages of the pipeline. Must-Have Skills 3–5 years in data science, ML, or analytics with experience in real-world data problems. Proficiency in Python and ML/data libraries (e.g., pandas, scikit-learn, Hugging Face). Experience building and maintaining data pipelines with Apache Airflow. Familiarity with Docker and modern data architecture principles. Strong foundation in statistics, data wrangling, and model evaluation. Comfortable communicating complex results to non-technical stakeholders. Fluent in English. Nice-to-Have Experience with audio, voice, or behavioral datasets. Familiarity with time-series analysis or sequence modeling. Knowledge of vector embeddings or NLP model fine-tuning. Who You Are A curious and rigorous thinker who’s fluent in data and comfortable navigating ambiguity. You love turning messy signals into structured insight—and insights into products. You value reproducibility, impact, and collaborating with multidisciplinary teams. Why Join Mappa Deep impact: Your insights shape the core of our AI product. Modern stack: Work with Python, Hugging Face, Airflow, and Docker in a fast-moving team. Remote-first: Join a distributed team across LatAm and the U.S.

QA / Test Automation Engineer Location: Remote (LatAm time zones preferred) Type: Full-time Reports to: CTO About Mappa Mappa builds real-time voice-AI infrastructure that decodes over 100,000 behavior patterns per person—based on prosodic and linguistic signals—and turns them into actionable insights for hiring and beyond. Quality and reliability are core to our product—and we’re looking for someone who ensures both scale with us. Role Purpose Design and maintain test automation systems that guarantee the stability, performance, and trustworthiness of Mappa’s voice-AI platform. You'll lead the creation of robust test cases, frameworks, and regression suites integrated with our CI/CD pipelines. Key Outcomes (First 6 Months) Implement automated test coverage for all core application components (frontend and backend). Reduce manual regression testing time by ≥ 60% via automation. Detect and prevent critical bugs pre-deployment in at least 90% of release cycles. Integrate testing into our CI/CD workflows and ship reliable builds multiple times per week. Core Responsibilities Design and implement end-to-end, integration, and regression test cases for web and backend components. Build and maintain test automation frameworks using Python, TypeScript, Selenium, Puppeteer, and PyTest/Bun. Integrate testing pipelines with CI/CD tools (Jenkins, GitHub Actions) for automated validation. Collaborate with Engineering and Product to define test plans and acceptance criteria. Monitor test coverage, identify gaps, and proactively improve testing strategies. Triage bugs, write detailed bug reports, and contribute to root cause analysis post-deployment. Maintain test documentation and ensure testing processes are well-documented and repeatable. Must-Have Skills 3+ years of experience in QA engineering or test automation. Strong proficiency in Python and/or TypeScript for scripting and test development. Experience with test automation tools (e.g., Selenium, Puppeteer) and test frameworks (PyTest, Bun or similar). Solid understanding of CI/CD pipelines and automated testing in release workflows. Familiarity with test case management, regression testing, and performance validation. Excellent attention to detail and process ownership. Fluent in English. Nice-to-Have Experience in testing AI or voice-based applications. Familiarity with monitoring tools or observability stacks (e.g., Sentry, Datadog). Exposure to performance and load testing frameworks. Who You Are A quality guardian who builds systems to break things—before users ever see a bug. You bring structure to chaos, automate relentlessly, and think like both an engineer and a user. You’re the last line of defense and proud of it. Why Join Mappa Product-critical role: Your tests keep our behavioral AI system stable and trustworthy. Modern stack: Work with engineers shipping Python, TypeScript, CI/CD, and agents at scale. Remote-first: Join a distributed team across LatAm and the U.S.

Machine Learning Engineer Location: Remote (LatAm time zones preferred) Type: Full-time Reports to: CTO About Mappa Mappa builds real-time voice-AI infrastructure that decodes over 100,000 behavior patterns per person—based on prosodic and linguistic signals—and turns them into actionable insights for hiring and beyond. Our ML stack is core to transforming raw audio into structured behavioral data at scale. Role Purpose Design, train, and deploy machine learning models that power Mappa’s behavioral inference engine. You'll work across the ML lifecycle—from data ingestion to optimized neural network deployment—ensuring speed, precision, and reliability at scale. Key Outcomes (First 6 Months) Develop and productionize at least two core models contributing directly to behavior extraction. Optimize inference performance by reducing model latency and memory consumption by ≥ 30%. Implement a modular training pipeline that supports continuous experimentation and deployment. Collaborate with data and infra teams to streamline the ML-to-production workflow using MLOps best practices. Core Responsibilities Design, train, and evaluate ML models—especially neural networks—for behavioral and linguistic data. Optimize tensor operations for efficient computation and memory usage in real-time environments. Implement scalable pipelines for data preprocessing and feature engineering using tools like Spark or Kafka. Apply model evaluation techniques, error analysis, and performance monitoring in production. Collaborate with Engineering and Data Science to integrate models into the voice-AI infrastructure. Contribute to MLOps processes including CI/CD for models, versioning, and experiment tracking. Must-Have Skills 3–5 years of experience in machine learning or applied deep learning roles. Strong Python skills and deep proficiency in PyTorch (or equivalent frameworks). Experience with neural network design, training optimization, and inference tuning. Familiarity with MLOps practices and tools for model deployment and monitoring. Understanding of distributed data processing tools (e.g., Apache Spark, Kafka). Strong grasp of model evaluation, bias/error analysis, and validation workflows. Fluent English. Nice-to-Have Experience with voice/audio data or NLP tasks. Familiarity with vector databases, real-time inference, or edge model deployment. Exposure to infrastructure-as-code tools and scalable cloud environments. Who You Are You turn data into models and models into real-world impact. You care about performance—computational, behavioral, and business—and thrive in fast-moving environments where research meets production. You bring scientific rigor and engineering discipline to every experiment. Why Join Mappa Cutting-edge tech: Tackle voice-AI, real-time inference, and behavioral modeling at once. High ownership: Build and ship models that power our core product. Remote-first: Join a distributed team across LatAm and the U.S.