Job Opportunities API

To get there, we need a platform engineer whose mandate is to make our prediction-to-trade runtime reliable, reproducible, observable, and safe to evolve. The role You will own PTL's prediction-to-trade runtime: the platform that runs forecasting pipelines, persists auditable reasoning traces, supports backtests and live/paper evaluations, and turns forecasts into trade intents, alerts, and broker-executed orders. This is not a generic developer role and not a pure infrastructure role. You will work across API contracts, event streams, state machines, background orchestration, database schemas, broker adapters, reconciliation, observability, and deployment safety. What you'll do • Own runtime correctness across prediction batches, prediction stages, pipeline specs, schedule runs, question snapshots, strategy states, target snapshots, order records, fills, positions, and audit events. • Harden orchestration across Trigger.dev: retries, idempotency, deterministic run keys, cancellation, lock strategy, failure classification, replay, and safe recovery. • Own API and event contracts across multiple repos: SSE events, OpenAPI/Zod schemas, structured artifacts, and stage/agent attribution. • Build headless prediction and evaluation workflows: scheduled batches, locked datasets, live-market and paper-trading probes, benchmark runs, and operator controls. • Build production observability: structured logs, Sentry, OpenTelemetry, pino, provider and tool-call timing, run-level dashboards, cost and token tracking, actionable alerts, and incident workflows. • Maintain data integrity across market ingestion, resolution syncing, cutoff dates, snapshot coverage, multi-choice market semantics, and source-specific schema quirks. • Support leakage-safe research workflows: frozen evidence, cutoff-date validation, trace replay, postmortem capture, and experiment-card audit trails. • Maintain deployment and environment hygiene across Vercel, Supabase, Trigger.dev, Doppler, AWS/EC2, and Cloudflare/SSM. • Improve platform velocity: contract tests, replay and regression harnesses, local-to-prod parity, paper-trade smoke tests, and reduction of flaky behavior. • Partner with Research, Data Science, Data Infrastructure, and Trading to turn evolving research logic into stable runtime contracts. You will not own the research thesis; you will own the systems that make research executable, measurable, and safe. Requirements • Strong TypeScript/Node backend engineering experience in production systems with real operational risk. • Experience designing stateful workflows where correctness depends on explicit status transitions, idempotency, and auditability. • Deep familiarity with Postgres-backed systems: schema design, migrations, constraints, indexes, RLS and auth boundaries, and data-quality checks. • Experience with asynchronous orchestration: queues, scheduled jobs, retries, cancellation, replay, compensating actions, and dead-letter or manual recovery paths. • Strong API and event-contract instincts: OpenAPI/Zod-style schemas, SSE or other streaming protocols, versioning, backward compatibility, and structured artifacts. • Practical observability experience: structured logs, tracing, Sentry or equivalent, dashboarding, alerting, and incident diagnosis. • Ability to work across app, runtime, and integration layers in one codebase without losing architectural discipline. • Fluency with AI-assisted development in large TypeScript systems; able to use agents and code assistants productively without sacrificing review discipline. • Strong product judgment under uncertainty: you can ship pragmatic runtime improvements while preserving correctness in high-stakes paths. • Ability to partner with research and data teams and translate evolving experimental logic into stable production contracts. Nice to have • Experience with Supabase, Trigger.dev, Drizzle, Hono, Next.js, or similar TypeScript runtime stacks. • Experience with trading systems, broker APIs, prediction markets, exchange APIs, order lifecycle management, or execution-critical fintech systems. • Experience with event-sourced or audit-ledger style systems: order events, fills, positions, reconciliation, or payment-state machines. • Familiarity with LLM pipelines, tool-calling, structured outputs, reasoning traces, or model-evaluation infrastructure. • Familiarity with ClickHouse or other OLAP systems and where analytical vs transactional boundaries should live. • Experience building deterministic replay or regression frameworks for workflows with external providers. • Experience with leakage-safe backtesting, frozen data snapshots, or time-consistent evaluation. Why PTL • Australia's highest powered team. Our founding team consists of Australia's Kaggle champion, SIG's Australia's top equities analyst, PhDs who reached 6th in ARC-AGI, and the founder of a time series foundation model lab. Our co-founders include the founder of Netlify, one of the world's largest DevOps unicorns, the creator of DLFinLab, and Forbes 30 Under 30 Alumini • Real traction. Our forecasting system already outperforms human superforecasters in internal and live evaluation. • High-leverage role. You own the runtime that connects forecasting, backtesting, evaluation, and trade execution. • Technically dense domain across AI reasoning, prediction markets, trading systems, data quality, and reliability engineering. • Compounding research loop. You will build the infrastructure that makes traces, evals, postmortems, paper and live probes, and production feedback compound over time. • Small, senior team with high ownership and fast iteration. • Backed by top-tier investors and operators. • Remote-friendly with Sydney and San Francisco presence. How to apply Send your resume and a brief note covering: • A production workflow you owned that required strict state transitions and idempotent background execution. What broke, how did you detect it, and how did you harden it? • An incident where async orchestration — queues, jobs, webhooks, or streaming — caused user-facing, operational, or financial risk. How did you mitigate it and prevent recurrence? • Design a recovery path for this scenario: a prediction batch is running, the SSE stream disconnects, the model provider times out, partial stage artifacts have been persisted, and a downstream trade alert depends on the final probability. What should the system persist, retry, replay, suppress, and alert on? • How would you evolve a human-in-the-loop paper trading stack into a reliability-first live trading platform without losing developer velocity or operator control? About Predictive Text Labs PTL builds AI that predicts the future. Our hybrid reasoning engine has achieved a Brier score of 0.121, beating human superforecasters. We're backed by Blackbird Ventures and notable angels, including Balaji Srinivasan, Synthesia founders, and Supabase founders.