Grupo Protege

• Manage accounts payable and vendor payment workflows • Generate customer invoices and support collections efforts • Prepare domestic and international wire transfers for Controller/CFO approval • Serve as a primary point of contact for vendor payment inquiries • Assist with cash tracking and short-term cash flow reporting • Maintain accurate financial records across systems and spreadsheets • Identify opportunities to improve finance processes and controls

Role Description - Gerenciar a agenda executiva e organizar compromissos por meio do Outlook. - Assessorar executivos em reuniões, elaborando atas e acompanhando pendências. - Recepcionar visitantes e direcionar demandas às áreas responsáveis. - Executar e acompanhar rotinas administrativas, garantindo o cumprimento de prazos. - Organizar e controlar salas de reunião, incluindo solicitação de alimentos e bebidas. - Apoiar as rotinas administrativas e financeiras da área. - Atuar com contas a pagar e contas a receber. - Prestar suporte às atividades de Assistente Administrativo Financeiro. Qualifications - Ensino Superior completo em Administração, Secretariado ou áreas correlatas. - Experiência mínima de 1 ano na função. - Vivência com rotinas administrativas e suporte executivo. - Experiência com contas a pagar e contas a receber. - Conhecimento em Pacote Office, especialmente Outlook. - Boa organização e gestão de prioridades. Requirements - Discrição e postura profissional. - Organização e atenção aos detalhes. - Facilidade para lidar com múltiplas demandas. Benefits - Auxílio Refeição ou Alimentação: R$ 31,00 por dia. - Reembolso-creche: R$ 474,50. - Seguro de vida.

• Own the supply side of Protege's data platform • Define the stages, validation gates, and quality checks that data passes through • Own the product decisions around metadata generation • Define what 'catalog-ready' means and build the tooling that enforces it • Work with vertical stakeholders to translate their requirements into platform-level standards

• Own healthcare customer engagements end-to-end • Lead customer-facing technical work from initial scoping through implementation, delivery, and post-delivery support. • Become the primary technical partner for assigned healthcare customers. • Build trusted relationships with customer engineering and technical teams. • Manage multiple concurrent healthcare engagements while maintaining delivery quality and customer confidence. • Develop and deploy integrations, data transformations, and applications that solve customer-specific challenges. • Navigate customer technical environments and adapt Protege’s platform capabilities to real-world deployment constraints. • Translate ambiguous customer needs into clear technical plans, delivery milestones, and practical implementation paths. • Identify where customer-specific work points to repeatable product opportunities. • Surface actionable product feedback based on implementation patterns, customer pain points, and delivery blockers. • Partner with product and engineering to turn bespoke solutions into reusable platform capabilities where appropriate. • Handle healthcare data environments with appropriate care, including PHI and regulatory considerations. • Make sound technical decisions in complex, regulated settings. • Communicate clearly with customers and internal teams about tradeoffs, risks, dependencies, and implementation choices. • Contribute playbooks, tooling, templates, and best practices that improve future healthcare deal execution. • Document repeatable patterns and lessons from customer engagements. • Help Protege move faster over time by turning one-off implementation work into reusable delivery assets.

• Partner with the GM and early customers to define what the vertical actually needs technically. • Build the first MVP of reusable patterns, integrations, and tooling that future engagements will run on, leveraging our core platform where it fits and extending it where the vertical requires something new. • Make architectural decisions about what belongs in the platform layer versus vertical-specific tooling. • Create the initial technical playbook so future FDEs can build on a real foundation rather than starting from scratch. • Lead the first customer engagements in the vertical, from technical scoping through delivery and post-launch support. • Write robust code that solves immediate customer problems while compounding into reusable infrastructure. • Navigate real-world complexity across customer data, integrations, workflows, and stakeholder dynamics. • Translate messy customer requirements into systems that are durable and maintainable. • Partner with Product and Engineering to identify which patterns from early customer work should become core platform capabilities. • Surface repeatable use cases, infrastructure gaps, and product opportunities from live engagements. • Help determine when the vertical is ready to evolve from bespoke delivery into a repeatable product motion. • Work directly with the GM or Solutions Lead to define the vertical’s technical strategy and commercial approach. • Partner with Data Lab on domain-specific data and research questions. • Collaborate with other FDEs on shared patterns, tools, and approaches that should compound across verticals. • Serve as the technical voice of the vertical as it grows.

• Design, build, and operate the ingestion systems that process large volumes of multimodal data into usable, well-structured datasets • Own the ingestion path end to end, from how data lands to how it is validated, processed, tracked, and made available downstream • Build modality-specific processing steps for real-world source data, such as medical imaging processing, audio and video metadata extraction, quality validation, and notes processing • Build parsers, validators, and normalization logic that can systematically handle messy, non-standard, and high-variance source formats • Turn repeated one-off data handling work into reusable processing patterns, internal tooling, and platform capabilities • Build for high volume and high throughput, optimizing systems for reliability, cost, and speed • Work across distributed and parallel compute systems to process workloads that do not fit well on a single machine • Choose the right execution model for the workload, including batch processing, distributed execution, and modern compute patterns for unstructured data and inference-heavy processing • Diagnose and resolve bottlenecks across ingestion and processing systems, and keep performance from degrading as volume and modality complexity grow • Build validation and quality checks that catch bad, incomplete, or malformed data before it propagates downstream • Handle sensitive and regulated data, including PHI, with the security and care the domain demands, including de-identification where required • Track provenance, metadata, and usage constraints through the ingestion path so downstream use remains compliant and auditable • Raise the quality bar for observability, debuggability, and operational reliability across the ingestion layer • Partner with product and Data Lab to support new modalities, new partner requirements, and non-standard source data • Work directly with partner engineering teams when needed to translate source-system realities into robust ingestion and processing design • Surface recurring patterns that are worth standardizing into reusable transforms, validators, and internal tooling • Help shape how Protege handles new data types as the platform expands into more complex data environments

• Record transactions in our ERP (currently Quickbooks; switching to Campfire) and participate in month-end close including preparing journal entries • Manage vendor schedules and prepare domestic transfers and international wire transactions • Ensure deals are properly recorded, accurately reflecting gross and net revenue, as well as recording delivery and billing dates • Invoicing customers • Categorize transactions in Ramp and sync to ERP

• Research audio data quality for machine learning • Investigate how audio quality, signal properties, dataset composition, and localized acoustic issues affect downstream model training, evaluation, and deployment. • Develop new metrics, benchmarks, diagnostics, and evaluation frameworks for measuring audio data quality in ways that are predictive of ML model performance. • Analyze and summarize Protege’s audio catalog and maintain clear, up-to-date quality scorecards and metrics for key speech datasets. • Develop methods to measure true acoustic properties directly from the waveform, including effective bandwidth, spectral energy distribution, high-frequency roll-off, noise, clipping, reverberation, distortion, and codec artifacts. • Build workflows that evaluate diarized or segmented speech regions, surfacing localized degradation that file-level averages may miss. • Design and run targeted evaluations connecting audio quality issues to downstream model behavior, including ASR performance, speaker embedding stability, learned speech representations, and synthesis quality. • Translate research findings into reproducible filtering rules, quality gates, and dataset selection strategies that improve dataset consistency across training runs.

• Design and build datasets, tasks, and environments for benchmarking agentic systems and multi-step model behavior. • Translate real-world workflows into structured tasks, interaction traces, trajectories, stateful environments, and verifiable outcomes that can be used to evaluate advanced AI systems. • Develop frameworks that assess diversity, realism, coverage, fidelity, informativeness, and downstream usefulness of datasets for agentic systems. • Build quality scorecards and evaluation methods that make dataset strengths, weaknesses, and failure modes legible across teams. • Evaluate planning, tool use, robustness, recovery from failure, task completion, and generalization behavior in RL-style or agentic environments. • Connect model failures back to concrete dataset, environment, or task-design gaps and recommend improvements grounded in empirical evidence. • Contribute to tools and systems that automate dataset validation, environment generation, rollout analysis, benchmark construction, and evaluation workflows. • Improve internal infrastructure for reproducible experimentation, benchmark management, and evaluation quality. • Collaborate closely with research and engineering teams to identify data bottlenecks, improve evaluation methodology, and shape internal best practices around task-grounded AI training data. • Represent DataLab’s perspective in cross-functional discussions around dataset quality, benchmark design, and frontier agentic-system evaluation.

• Define and lead the research strategy for Protege's Data Lab, aligning experimentation with company priorities and product direction. • Partner closely with Product, Engineering, and GTM teams to identify high-value research opportunities tied to AI data quality, evaluation, and marketplace performance. • Design and oversee experiments that evaluate dataset quality, model performance, synthetic data workflows, and privacy-preserving methodologies. • Build scalable systems for benchmarking, labeling quality analysis, and training data evaluation across multiple AI modalities. • Serve as a customer-facing research partner to GTM, representing DataLab in sales, technical discovery, delivery, and customer strategy conversations while translating research depth into practical guidance that helps Protege deliver value to our partners and customers. • Translate ambiguous technical questions into clear research frameworks, measurable hypotheses, and actionable recommendations. • Publish internal research findings that directly influence product decisions, customer strategy, and platform capabilities. • Lead, manage, and scale a high-performing team of researchers and data scientists, driving execution, technical excellence, and career development across the Data Lab organization. • Establish operational rigor around experimentation, reproducibility, and research documentation. • Represent Protege externally through technical conversations with customers, partners, and the broader AI ecosystem. • Stay at the forefront of advancements in foundation models, evaluation methodologies, data infrastructure, and AI alignment research.