Founding AI/ML Engineer

3 weeks ago

Capital District, Philippines Edward Jacobs Full time

Add expected salary to your profile for insights We’re building an AI-first CRM used by recruiters all day. Think: rich records (companies/contacts), pipeline/Kanban , notes & tasks , search & saved views , and CSV import/export —with AI woven into the workflow (summaries, retrieval-based answers, safe automations). You’ll turn messy, real-world recruiting data into reliable AI features that meet strict latency , cost , and safety constraints. (We’ll share deeper internals later in process.)What makes this complex: Chat + retrieval must be grounded in our data (no hallucinations) and evaluated continuously—your work sets up eval hooks and dashboards to track faithfulness, relevance, and answer quality. We ship secure LLM features by default (prompt-injection defenses, data-exfil controls, PII handling) aligned to the OWASP LLM Top-10. The CRM has multi-step flows; some AI behaviors are agentic (tool use, routing, hand-offs). We orchestrate these explicitly (graph/guardrails) so they’re observable and debuggable. What you’ll lead (high-level, IP-safe): Production retrieval + chat : chunking/embedding strategies, re-ranking where needed, and prompted answer synthesis wired to our sources—with traces and test sets. Prompt & chatbot engineering : instruction design, system/task decomposition, multi-turn memory, tool-calling boundaries, and latency budgets; you’ll apply modern prompting patterns and iterate with experiments. Evaluation & observability : reference-free RAG evals (e.g., faithfulness/relevance), regression tests, and per-feature dashboards. Safety & privacy : align to OWASP LLM risks; design redaction/guardrails that preserve UX without exposing internals. Agentic orchestration (later) : structured, node-based flows for tool use and human-in-the-loop steps (graph-style orchestration). Must-have capabilities: Shipped LLM/NLP or search features in production (grounded answers, not just demos). Strong prompting instincts (system vs. user prompts, few-shot, constrained output formats), multi-turn state, and tool calling . Comfortable building RAG pipelines with measurable quality (faithfulness, answer relevance) and cost/latency SLOs. Security/privacy mindset aligned to OWASP LLM Top-10 (prompt injection, data exfiltration, etc.). Communicate clearly; ship in small, observable increments. Nice-to-have: Experience orchestrating agent workflows (graphs, hand-offs, tool routing) and keeping them debuggable.How we work: Remote-first, senior, high-trust. Short cycles, preview-before-merge. We measure AI by quality, safety, and cost , not just speed. Compensation & equity: Competitive salary plus founding equity (discussed during process). (We’ll request portfolios/demos later in the process.) Unlock job insights Salary match Number of applicants Skills match To help fast track investigation, please include here any other relevant details that prompted you to report this job ad as fraudulent / misleading / discriminatory / salary below minimum wage. What can I earn as a Machine Learning Engineer #J-18808-Ljbffr

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