Grupo Protege

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.

Machine Learning Researcher – RL and Agentic Systems

Director

• 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.

Director of Research, DataLab

Data Scientist

• Help design, construct, and validate complex healthcare data cohorts used for AI model training
• Act as a technical partner for complex data problems, including cohort construction and data validation
• Translate research and customer requirements into practical dataset definitions
• Build SQL and analysis needed to create datasets
• Collaborate with delivery engineers to implement solutions requiring data pipeline changes
• Validate datasets for quality and acceptance criteria
• Work with AI researchers to translate goals into practical dataset specifications
• Analyze partner datasets for schema understanding and data quality

Solutions Applied Data Scientist, Healthcare

Solutions Engineer

• Own cross-cloud data movement and delivery
• Execute and monitor large-scale data transfers across AWS S3, Google Cloud Storage, Azure Blob, Snowflake, and customer environments.
• Use CLI tools such as rclone, s5cmd, and cloud-native utilities to move data safely and efficiently.
• Manage credentials, permissions, manifests, and delivery packaging artifacts required for ingestion, subset delivery, and handoff workflows.
• Build structured data assembly and lightweight transformation workflows
• Use Python and SQL to join datasets, add derived columns, clean data, and validate CSV, Parquet, and database tables.
• Support customer-specific assembly work that turns raw inputs into delivery-ready datasets.
• Apply a high bar for data integrity, structure, and reproducibility before handoff.
• Operate internal pipelines with production discipline
• Leverage Protege's Dagster-based platform to orchestrate data processing and delivery.
• Maintain clean separation between pre-production and production workflows and validate configs before runs.
• Build lightweight scripts and command-line workflows for filtering, manifest generation, validation, and recovery.
• Create leverage for the team and platform
• Document steps, outputs, and recovery paths for auditability and repeatability.
• Identify recurring delivery patterns, failure modes, and manual toil.
• Partner with Engineering to turn one-off operational work into repeatable platform capabilities and test new tooling before go-live.

Solutions Engineer, Healthcare

Account Manager

• Lead onboarding of new healthcare data partners, establishing repeatable operational playbooks
• Coordinate secure data access, querying workflows, and transfer mechanisms
• Partner with Product and Engineering to align on ingestion, normalization, and technical setup
• Ensure privacy, compliance, and governance requirements are properly implemented in collaboration with Legal and Compliance
• Develop structured processes to scale partner onboarding efficiently
• Conduct structured evaluations of partner datasets across EHR, imaging, claims, genomics, device, or real-world data
• Partner with DataLab to assess data completeness, quality, modality coverage, and fitness for AI training use cases
• Collaborate with scientific teams to support feasibility assessments for customer projects
• Identify data gaps, operational risks, and areas requiring remediation
• Establish partner performance scorecards tracking responsiveness, reliability, and data quality
• Align partners on project requirements, qualification criteria, and delivery timelines
• Track feasibility analyses and ensure timely, accurate partner responses
• Monitor deal progress from qualification through fulfillment
• Coordinate data delivery milestones and resolve execution bottlenecks
• Proactively surface risks to internal stakeholders and propose mitigation plans
• Ensure partners are fulfilling their obligations across individual customer engagements
• Monitor utilization, revenue contribution, and operational performance
• Partner with the Strategic Partnerships Lead to identify expansion opportunities
• Maintain aligned communication between partners and internal teams
• Continuously improve operational workflows to increase reliability and speed
• Serve as the primary execution point of contact for healthcare data partners
• Build trusted relationships grounded in transparency, responsiveness, and accountability
• Reinforce Protege’s position as the premier platform for responsible AI data collaboration