Grupo Protege

Data Scientist

• 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

Product Manager, Data Ingestion – Quality

Engineer

• 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.

Forward Deployed Engineer – New Verticals

• 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.

Forward Deployed Engineer, Healthcare

Accountant

• 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

Accountant – Temporary

AI Research Scientist

• 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.

Machine Learning Researcher – Audio

• 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.

Product Manager, Data Ingestion – Quality

Forward Deployed Engineer – New Verticals

Forward Deployed Engineer, Healthcare

Accountant – Temporary

Machine Learning Researcher – Audio

Machine Learning Researcher – RL and Agentic Systems