Build the infrastructure that makes AI safe in regulated environments.

What you'll own

• CR-110 backtest, replay, and simulator infrastructure with deterministic runs, reproducible inputs, and audit-ready outputs
• PIT-safe market data handling, source timestamp discipline, stale-data controls, and RTH guards that block invalid trading assumptions
• Simulator versus production parity across trigger evaluation, order staging, fill modeling, trigger-vs-fill reconciliation, and broker-facing behavior
• Slippage models, replay harnesses, regression tests, and drift reports that make methodology changes visible before release

What we're looking for

• Strong Python systems engineering for trading, simulation, replay, market-data processing, or quantitative platform infrastructure
• Experience building deterministic test harnesses, regression suites, and parity checks for production-facing financial systems
• Comfort with PIT semantics, market-session boundaries, stale data, fill reconciliation, slippage assumptions, and audit-grade evidence
• Spec-first development discipline where CR contracts, verification gates, and failure policies define implementation boundaries

What we don't expect

• DevOps or SRE ownership as the center of the role
• ML model training, marketing analytics, or advisor-facing product ownership
• Autonomous ownership of production trading logic without architecture review, replay evidence, and approval gates

What you'll own

• Financial data ingestion pipelines into Postgres and TimescaleDB with source timestamps, source hashes, provenance records, and replayable ingestion state
• Knowledge Graph expansion across issuers, securities, sectors, subsidiaries, board members, suppliers, clients, and event relationships
• Entity resolution across tickers, CIKs, LEIs, aliases, subsidiaries, directors, suppliers, customers, and source-specific identifiers
• CR-114 and CR-137 style source verification, ingestion quality checks, deduplication, lineage reporting, and escalation for suspect inputs

What we're looking for

• Five-plus years building production data platforms, financial data pipelines, or knowledge-graph-backed systems
• Strong SQL and Python with Postgres, TimescaleDB, ingestion workers, schema migrations, data-quality gates, and operational debugging
• Practical experience with provenance, source hashing, source timestamps, idempotent ingestion, and multi-vendor data normalization
• Entity-resolution judgment across messy financial identifiers, corporate hierarchies, ownership links, and relationship data

What we don't expect

• Frontend product ownership, marketing-site work, or CRM plugin development as the center of the role
• ML model training with PyTorch or TensorFlow
• Ownership of trading signals, backtest methodology, or advisor-facing workflow design

What you'll own

• CR-130 numeric immutability services that verify client-visible numbers remain byte-for-byte consistent through personalization and delivery
• CR-135 material surface verification for approved views, advisor notes, client outputs, evidence links, and delivery artifacts
• CR-136 failure orchestration with retries, hard stops, escalation routing, operator visibility, and auditable failure records
• CR-137 ingestion quality orchestration across validators, schema-first APIs, audit records, evidence bundles, and downstream blocking gates

What we're looking for

• Senior backend engineering experience in regulated, correctness-critical, or high-stakes software environments
• Deep Python proficiency with schema-first APIs, validators, state machines, retries, audit records, and failure handling
• Strong SQL, data validation, and contract tooling such as JSON Schema, Pydantic, Protobuf, or Avro
• Experience turning compliance, AI safety, or verification requirements into deterministic services with clear escalation paths

What we don't expect

• Manual testing or tester-management as the core of the role
• ML model training, prompt-only evaluation, feature engineering, or trading logic ownership
• Frontend product ownership beyond simple operator and audit surfaces

What you'll own

• Pytest regression suites for spec-conformance checks, frozen examples, schema validation, replay determinism, and release blockers
• Validation corpora, expected-output fixtures, evidence reports, and audit matrices that make correctness claims reviewable
• GitHub Actions verification gates with clear failure output, trend visibility, and controlled exceptions
• Fault-injection campaigns, cross-environment drift checks, CR acceptance tests, and regression coverage for known failure modes

What we're looking for

• Four-plus years in QA automation, validation engineering, data validation, or systems verification
• Strong Python and pytest experience for contract probes, fixtures, fault injection, evidence generation, and CI gates
• Hands-on schema validation tooling and GitHub Actions experience with gates that remain trustworthy, not just green
• Ability to turn specs, known gaps, and failure policies into automated checks with clear evidence reports

What we don't expect

• Manual testing as the center of the job
• Feature engineering, ML modelling, trading-signal design, or production incident response ownership
• Owning senior architecture decisions without review from backend, verification, or trading-system leads

What you'll own

• Advisor and operator dashboards for review queues, approvals, exceptions, source evidence, and delivery readiness
• Review and approval workflows with reason capture, role-aware actions, status history, and integration with backend verification APIs
• Tenant and admin surfaces for configuration, access, workflow controls, data visibility, and operational settings
• Audit and evidence views that make validation state, source links, failures, retries, and approval history easy to inspect

What we're looking for

• Senior React and TypeScript experience shipping polished operational interfaces for complex workflows
• Strong product judgment around dashboard density, state handling, empty/error/loading states, and role-specific task flows
• Experience integrating frontend applications with backend APIs, auth, validation errors, audit records, and real-world data latency
• Comfort building internal tools and regulated-workflow surfaces where clarity, traceability, and operator trust matter

What we don't expect

• Marketing pages, brand campaigns, or landing-page design as the center of the role
• Backend architecture ownership beyond API-contract collaboration
• ML model training, quant research, or production trading logic ownership

What you'll own

• Python validators, adapters, internal APIs, and service utilities built from frozen CR contracts and schema definitions
• Implementation support for CR-130, CR-136, and CR-137 once senior architecture, data contracts, and acceptance rules are defined
• Tests, fixtures, documentation, and operational notes that make contract behavior understandable and reviewable
• Small internal tools that reduce manual verification work and expose implementation state clearly to senior reviewers

What we're looking for

• Three-plus years of Python backend engineering with APIs, validators, adapters, tests, and production code review
• Ability to implement from precise specs, ask sharp questions when contracts are incomplete, and keep behavior aligned with acceptance criteria
• Comfort with SQL, JSON Schema or Pydantic, service integration, CI checks, and clear technical documentation
• Good engineering hygiene under senior review: small changes, readable tests, explicit edge cases, and disciplined error handling

What we don't expect

• Owning senior architecture, compliance interpretation, or final production trading decisions
• Frontend product ownership, data engineering ownership, or ML model training
• Working independently around frozen specs without review

What you'll own

• Small Python utilities, scripts, fixtures, and test helpers that support senior backend, verification, and data engineering work
• Tests for known gaps, validation corpora updates, audit/evidence matrices, and simple evidence-report generation
• Classification and reporting helpers for controlled AI/backend experiments under review
• Documentation cleanup, reproducible examples, and narrow implementation tasks with explicit acceptance criteria

What we're looking for

• Early-career Python ability with readable code, tests, debugging discipline, and willingness to work from precise instructions
• Interest in AI backend systems, validation, audit evidence, and regulated software delivery
• Ability to communicate blockers early, keep tasks small, and incorporate senior review quickly
• Reliability in hourly trial work with clear time tracking, concise notes, and careful handling of sensitive context

What we don't expect

• Data engineering ownership, quant development, or production trading logic ownership
• Independent architecture decisions or unsupervised changes to regulated workflows
• Frontend product ownership, ML model training, or client-facing delivery ownership