Sixtyfour

About Sixtyfour Sixtyfour is a data orchestration engine for company and people data. Our AI agents specialize in enriching detailed datapoints about individuals and organizations, gathering information from both public and proprietary sources. We enable enterprise customers to build custom data pipelines that help them find, enrich, qualify, and act on company-specific requirements for people and business data. Our Mission To power how enterprises understand and act on people and companies with world-class intelligence. What you’ll do Design and ship agentic systems (tool calling, multi-agent workflows, structured outputs) that reliably fetch, extract, and normalize data across the web and APIs. Own robust web scraping: directory crawling, CAPTCHA handling, headless browsers, rotating proxies, anti-bot evasion, and backoff/retry policies. Develop backend services in Python + FastAPI with clean contracts and strong observability. Scale workloads on AWS + Docker (batch/queue workers, autoscaling, fault tolerance, cost control). Parallelize external API requests safely (rate limits, idempotency, circuit breakers, retries, dedupe). Integrate third-party APIs for enrichment and search; model and cache responses; manage schema evolution. Transform and analyze data using Pandas (or similar) for normalization, QA, and reporting. Pitch in across the stack: billing (Stripe), and occasional front-end changes to ship end-to-end features. Minimum requirements Hands-on experience with agentic architectures (tool calling, structured outputs/JSON, planning/execution loops) and prompt engineering. Proven web scraping expertise: solving CAPTCHAs, session/auth flows, proxy rotation, stealth techniques, and legal/ethical constraints. AWS + Docker in production (at least two of: ECS/EKS, Lambda, SQS/SNS, Batch, Step Functions, CloudWatch). Building high-throughput data/IO pipelines with concurrency (asyncio/multiprocessing), resilient retries, and rate-limit aware scheduling. Integrating diverse external APIs (auth patterns, pagination, webhooks); designing stable interfaces and backfills. Strong data wrangling with Pandas or equivalent; comfort with large CSV/Parquet workflows and memory/perf tuning. Excellent ownership, product sense, and pragmatic debugging. Nice to have Entity resolution/record linkage at scale (probabilistic matching, blocking, deduping). Experience with Langfuse, OpenTelemetry, or similar for tracing/evals; task queues (Celery/RQ), Redis, Postgres. Search relevance (BM25/vector/hybrid), embeddings, and retrieval pipelines. Playwright/Selenium, stealth browsers, anti-bot frameworks, CAPTCHA providers. CI/CD, infrastructure as code (Terraform), and cost/perf observability. Security & compliance basics for data handling and PII.

Sixtyfour is a data enrichment platform that lets companies generate powerful, custom datasets at scale. Our customers—recruitment firms, data labeling companies, and growth teams—use our platform to source, qualify, and enrich prospect and candidate data that powers their pipelines. To close deals, we show prospects the quality of datasets they can build with us by creating tailored samples—and that’s where this role comes in. Our customers come to us because they want high-quality, enriched datasets—and they need help getting there. You’ll be the person who makes that happen: proactively using our platform to show customers what’s possible, ensuring they can use it to get the results they need, and responding to requests in Slack and email. Creating data samples isn’t a separate function from support—it is the support. You’re guiding customers to success before they even have to ask. This is an in-person role embedded with our sales and growth team. What You’ll Do Proactively use the SixtyFour platform to curate data samples that show customers what’s possible—ensuring they understand how to get the results they need from the platform themselves. Review every sample for accuracy, completeness, and deduplication before it reaches the customer. Zero errors. Manage 7–10+ concurrent requests from prospects and customers, staying organized across Slack channels, email, and CRM. Be the first responder in shared Slack channels—answering questions, triaging issues, fulfilling data requests, and proactively surfacing ways customers can get better results. Track customer engagement and flag accounts that need attention or are at risk of churn. Coordinate with engineering on bugs and product issues, translating customer feedback into clear, actionable context. Join daily standups and CRM syncs to keep the team aligned on customer status and request pipelines. Who You Are Self-starter with a growth-oriented mindset—you take initiative, move fast, and thrive in ambiguous, rapidly changing environments. Timely and put together—you show up prepared, meet deadlines, and hold yourself to a high professional standard. Detail-oriented and comfortable working with spreadsheets, CSVs, CRMs, and data tools. Strong written communicator who can manage multiple Slack and email threads simultaneously.

About Sixtyfour Sixtyfour is a data orchestration engine for company and people intelligence. We build AI systems that gather, verify, enrich, and structure complex information about individuals and organizations across public and proprietary sources. Our platform and API enable enterprises to design custom data pipelines that find, qualify, and act on mission-critical intelligence about people and companies. Our Mission To power how enterprises understand and act on people and companies with world-class intelligence. The Role We are hiring a Senior AI Software Engineer to design and ship production-grade AI agents that reason over complex human and organizational data. This is not a generic LLM application role. You will build agentic systems that operate across the open web, APIs, and OSINT sources to solve high-stakes problems such as identity verification, entity resolution, and internet presence analysis for enterprise intelligence use cases. You must have shipped real AI-first products in production. We are looking for engineers who understand how to turn large language models into reliable systems, not just prototypes. What You’ll Actually Do • Build agentic systems with tool calling, structured outputs, and multi-step reasoning loops • Turn large language models into reliable production systems, not prototypes • Engineer web intelligence pipelines that survive anti-bot systems, CAPTCHAs, rotating proxies, and dynamic sites • Design OSINT workflows that extract signal from noisy, fragmented data • Ship high-throughput backend systems in Python + FastAPI • Scale workloads on AWS with Docker, queues, batch workers, and autoscaling • Parallelize API calls safely with rate limits, retries, idempotency, and circuit breakers • Normalize, reconcile, and deduplicate messy people and company data • Own problems end to end, from ingestion to customer-facing impact You will operate across AI systems, scraping infrastructure, backend architecture, and data engineering. Who You Are • 1–2+ years at an AI-first startup or company • You have shipped real AI products into production • You understand agent architectures, tool calling, structured outputs, and evaluation • You’ve built resilient scraping systems and handled adversarial web environments • You’re comfortable with AWS + Docker in production • You’ve built high-concurrency IO pipelines • You take ownership and debug systematically You don’t just prompt LLMs. You design systems around them. Strong Edge • OSINT experience (intelligence, investigations, large-scale open web analysis) • Entity resolution and probabilistic matching at scale • Search systems, embeddings, retrieval pipelines • Stealth browser automation and anti-bot frameworks • Observability tooling for AI systems This role is not suitable for new graduates.

About Sixtyfour Sixtyfour is a data orchestration engine for company and people data. Our AI agents specialize in enriching detailed datapoints about individuals and organizations, gathering information from both public and proprietary sources. We enable enterprise customers to build custom data pipelines that help them find, enrich, qualify, and act on company-specific requirements for people and business data. Our Mission To power how enterprises understand and act on people and companies with world-class intelligence. What you’ll do Build polished, fast product experiences in Next.js + TypeScript (design-quality UI, accessible components, responsive layouts). Design clean backend contracts and APIs (FastAPI/Python) for workflows, jobs, billing, and auth. Evolve our workflow engine (DAG/state machine): scheduling, retries/idempotency, rate limits, progress tracking, logs, and user-visible run history. Ship data pipelines and background workers (Celery/queues), with strong observability and alerting. Deploy the stack on AWS + Docker (ECS/EKS or similar), with caching (Redis), pagination, backpressure, and cost controls. Implement product features end-to-end: DB schema → API → UI, including tests and telemetry. Maintain a high bar for reliability, performance, and UX; instrument everything. Be very customer-focused on how certain product decisions impact end customers. Minimum requirements Strong backend skills in Python (FastAPI preferred): API design, testing, error handling, schema/versioning. Good design taste is a must. Experience deploying/operating with Docker and a major cloud (AWS preferred: ECS/EKS/Lambda, SQS, CloudWatch). Comfortable with Postgres (or MySQL) schema design and Redis (caching, rate limiting, locks). Performance mindset: RSC/Server Actions, data-fetching patterns, bundle/TTFB tuning, DB query perf. Production observability: logs, traces, metrics (e.g., Langfuse, OpenTelemetry, Sentry), and on-call pragmatism. Solid security hygiene (PII handling, secrets, JWT/session auth, CSRF/CORS, least-privilege access). Nice to have Next.js App Router, RSC, Server Actions, edge/runtime primitives (Vercel/AWS CloudFront). Prisma/Drizzle; tRPC or GraphQL; Zod/JSON schema; feature flags/experimentation. Realtime UX (WebSockets/SSE), optimistic updates, background revalidation. Celery/RQ, event sourcing, or state-machine libs (e.g., Temporal, Dagster, Prefect, XState). Testing: Playwright (e2e), Vitest/Jest, pytest; accessibility checks.