--- title: 'AI Engineer - Core at Hilbert''s AI' canonical: 'https://feeny.ai/job/ai-engineer-core-hilbert-s-ai-turkiye-rmnpqy3tfg68' type: 'job' last_seen: '2026-09-20' --- # AI Engineer - Core at Hilbert's AI - **Company:** Hilbert's AI - **Location:** Türkiye - **Employment:** full-time - **Work type:** remote - **Posted:** 2026-09-14 - **Last confirmed live:** 2026-09-20 - **Apply:** https://jobs.ashbyhq.com/hilberts/133e25a8-f895-4c44-bfbb-d7b64db4db7b ## Job description Hilbert is building a reasoning engine that must navigate non-deterministic user behavior across data silos — turning months-long decision cycles into minutes. Fully agentic by design, our demand intelligence platform doesn't just call APIs; it solves the hard problem of orchestrating multi-step inference over messy, high-stakes enterprise data where deterministic answers don't exist. From Fortune 500 enterprises to beloved brands like FreshDirect, Blank Street, and Levain Bakery, operators run their growth on Hilbert. We're also co-building alongside leading AI companies. We're looking for an AI Engineer who can build production-grade AI systems end-to-end — from prototype to pipeline to product — with the ownership and urgency of a startup culture. This is not a "wire up a prompt chain and move on" role. You'll own core pieces of the AI stack that power Hilbert's demand intelligence platform — designing agent architectures, building evaluation systems, and making hard tradeoffs between accuracy, latency, and cost in production. You'll ship fast in conditions where the spec is evolving, and communicate what you're building (and why) with clarity to the rest of the team. If you think in systems, have opinions about how agentic workflows should actually work, and want to build AI products that drive real enterprise outcomes, we want to meet you. What you’ll own first: Evaluation and testing for our agents. Hilbert's agents are in production with enterprise customers today. Before we expand what they do, we need to know, reproducibly, when a change makes them better or worse. You'll own designing the eval harness, defining what "correct" means for a multi-step agent trajectory, building the regression gates that run before anything ships, and turning production failures into test cases. From there, the scope widens into retrieval, orchestration, and execution across the AI stack. What you'll do: - Own the evaluation layer for our agents — harnesses, metrics, golden datasets, regression gates, and human-in-the-loop review. - Architect and implement agent workflows using LangChain, LangGraph, or equivalent; state memory, routing, tools registries and recovery paths. - Own systems from experimentation through production and operate in production: tracing, monitoring, latency, cost-per-task budgeting, on-call for what you build. - Diagnose real production failures: hallucination, tool misuse, retrieval misses, silent degradation and turn each into a durable fix and a test. - Set the technical standard for agent work at Hilbert: review designs, define patterns others build on, and raise the bar across the team. - Collaborate closely with the founding team and cross-functional partners — communicating tradeoffs, progress, and technical decisions with clarity. - Make pragmatic engineering decisions under ambiguity—ship, learn, iterate. Our Current Hurdles These are the kinds of problems you'll walk into on day one: - Intelligent retrieval across heterogeneous approaches — our agents need the right information at exactly the right moment. The challenge isn't picking one retrieval method; it's combining RAG, graph-based retrieval, and other approaches into a unified strategy that fetches the most relevant content precisely when the agent needs it — no more, no less. - Agentic workflows that solve real-world problems — it's building workflows robust enough to handle the unexpected. When an agent hits an edge case, missing data, or a situation it wasn't explicitly designed for, it needs to reason through it — leveraging available context, escalating to a human when it can't, and never silently failing. - Evaluation beyond vibes — we need systematic, reproducible evals that actually predict real-world performance. If you've built custom evaluators for RAG or agent workflows, we want to talk. - Execution and real-world integration — an agent that only surfaces insights isn't enough. We're building systems where agents take action — integrating with external platforms, executing workflows, and doing real work with the information they have, combined with human-in-the-loop checkpoints that keep enterprise trust intact. ## WHO THRIVES IN THIS ROLE We care about what you've shipped and how you think, but as an AI Engineer, we expect you to demonstrate 4+ years of building production software, with at least 2 of those on LLM or agentic systems that real users depend on. Must-haves - 4+ years of production software engineering: APIs, services, data infrastructure. You've owned code that other people depended on, with tests, CI/CD, and on-call attached. - 2+ years building LLM or agent systems that shipped to productions watching real users adopt. Not internal demos, not prototypes. Be ready to talk about what broke in production and what you did about it. - Hands on with LangChain, LangGraph, or equivalent agent/orchestration frameworks. You've built with them, hit their limits, and worked around them. You operate beyond just following tutorials. - You communicate with clarity and conviction. You can explain a technical decision to a non-technical founder and debate architecture tradeoffs with a senior engineer. Communication is not a nice-to-have here — it's core to the role. - You take ownership. You don't wait for tickets. You see what needs to be built, raise your hand, and ship it. - You thrive in ambiguity. AI products evolve fast. Requirements change. You're energized by figuring it out. - You move at startup speed. Without waiting for permission, and you know which decisions deserve a day of thought and which deserve an hour. Strong pluses: - Retrieval-augmented generation (RAG) at depth: Hybrid and graph retrieval, chunking and embedding strategy, ranking, grounding - Observability for LLM systems (Langfuse, OpenTelemetry, or equivalent) and cost/latency optimization - MCP, tool-calling frameworks, structured output and constrained decoding - Experience at early-stage startups or high-growth environments where you wore multiple hats You might be: A backend engineer who went deep on LLMs and never looked back. An ML engineer who realized they love building products, not just models. A startup CTO who wants to go deep on AI at a company where the stack is the product. Someone who's been hacking on agents and pipelines nights and weekends and wants to do it full-time with real enterprise stakes. What matters: you ship, you own it, and you communicate like a teammate — not a silo. Location Turkiye At least 5 hours overlap with PST timezone (7am-5pm) ## Compensation Competitive salary + equity package, commensurate with experience. Performance-based bonuses tied to project milestones and customer impact. The Hiring Journey Short form → Intro call → Technical working session → Team conversations → Offer ## Why join us At Hilbert, we move fast, work collaboratively, and give people real ownership over their impact. As a fast-growing company, there's no shortage of room to grow — you'll take on new challenges quickly and shape the path as you go. Hilbert is an Equal Opportunity Employer. We are committed to building a diverse team and an inclusive culture, and we welcome applicants of all backgrounds. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, disability, veteran status, or any other status protected by applicable law. ## About Hilbert's AI ## Company Overview - **One-liner**: Hilbert provides an AI-native growth infrastructure that unifies fragmented data into actionable insights and automated actions for B2C companies, replacing traditional BI tools. - **Entity Type**: Private (Series A) - **Headquarters**: San Francisco, California, United States - **Founded**: 2024 (pre-seed closed June 2025) - **Founders**: Nazli Tan (CEO), Ozgur Akaoglu (Co-founder, AI/ML), Cenk Batman (Co-founder, Engineering) ## Core Business - **Primary industry**: AI-Powered Analytics / Growth Intelligence - **Target customers**: B2C companies (consumer brands, e-commerce, subscription services, retail) – serving growth marketing teams, CFOs, and executives - **Mission**: To replace fragmented dashboards and manual analysis with a single AI-native system that observes, reasons, acts, and evolves automatically – enabling companies to see clearly, decide fast, and grow with precision. ## Products & Services - **Hilbert Growth Infra (SaaS)**: A multi-layer AI platform that ingests customer data, detects anomalies and trends, explains root causes, and executes autonomous growth actions (e.g., CRM triggers, budget reallocation, retention plays). Accessible through natural language with no code required. Integrates with tools like Segment, Fivetran, Mixpanel, and Snowflake. ## Market Standing - **Valuation**: Not disclosed - **Key Metric**: $28M Series A (led by Andreessen Horowitz, April 2026) - **Total Funding**: $28M+ (Series A) plus an undisclosed pre-seed round led by Asylum Ventures (June 2025) - **Notable Investors/Partners**: Andreessen Horowitz (lead), Asylum Ventures; customers include brands like Blank Street (mentioned in social posts) - **Growth Signals**: - Headcount grew 400% YoY (from ~4 to 20 employees) - 3,333 LinkedIn followers (+215% yearly) - Hiring key roles: Head of Retail (QSR & Grocery), ML Engineers, Software Engineers - Rapid traffic growth (monthly visits +108%) and low bounce rate (0.6%) - Podcast launch (“Hilbert Space”) with Walmart EVP Daniel Danker ## Competitive Advantages - **AI-Native & Agentic**: The platform doesn’t just report – it detects shifts, explains “why,” and executes actions automatically, collapsing months-long decision cycles to minutes. - **Unified Infrastructure**: Replaces multiple fragmented tools (BI, analytics, CRM) with one system that connects insight, decision, and action. - **No-Code Natural Language Interface**: Users can ask questions in plain English without relying on data teams. - **SOC 2 Compliant**: Enterprise-grade security and encryption by design. - **Team Expertise**: Founders and engineers bring experience from Google, FreshDirect, Getir, Nillion Labs, and top academic institutions (MIT, NYU, Georgetown, etc.), with deep B2C growth domain knowledge. ## Strategic Focus - **Current Priorities**: - Scaling the platform across verticals – especially retail (quick-service restaurants, grocery). - Expanding the go-to-market team with enterprise revenue leaders. - Continuing to invest in AI/ML capabilities (root cause analysis, predictive modeling, autonomous actions). - Building the “a16z Growth Engineer Fellowship” to attract top technical talent. - **Direction for Growth**: Becoming the essential growth infrastructure for all B2C companies, replacing manual analytics and enabling real-time, automated optimization of acquisition, retention, and monetization. ## Why Work Here - **Culture & Work Environment**: Hybrid model with a strong in-office presence in San Francisco (HQ). Small, fast-growing team (20 people) with a collaborative, high-impact atmosphere. - **Remote/Hybrid Policy**: Roles are hybrid (expected time onsite); some positions are fully in-office. Offices in SF and likely Istanbul (given high Turkey headcount). - **Notable Perks**: Not publicly detailed, but the company offers the chance to work on cutting-edge AI/ML problems with a team of top-tier engineers and operators backed by a16z. Rapid growth provides early-career acceleration and ownership. - **Engineering Culture**: Emphasis on mathematical rigor, AI/ML innovation, and building production-grade infrastructure. Tech stack includes Python, ClickHouse, dbt, Fivetran, SQL, and modern data tools. ## Sources 1. [hilberts.ai](https://hilberts.ai/) 2. [hilberts.ai/about-us](https://hilberts.ai/about-us) 3. [linkedin.com/company/hilbertsai](https://www.linkedin.com/company/hilbertsai) 4. [builtin.com/company/hilbert-s-ai](https://builtin.com/company/hilbert-s-ai) 5. [jobs.ashbyhq.com/hilberts](https://jobs.ashbyhq.com/hilberts) ## Other roles at Hilbert's AI - [Product Manager - LATAM](https://feeny.ai/job/product-manager-latam-hilbert-s-ai-latin-america-fcra8p2whj2n) — Latin America - [Forward Deployed Data Engineer - LATAM](https://feeny.ai/job/forward-deployed-data-engineer-latam-hilbert-s-ai-latin-america-tekm9prfhr4e) — Latin America - [Talent Specialist - LATAM](https://feeny.ai/job/talent-specialist-latam-hilbert-s-ai-mexico-city-4h3bjrydyzh1) — Mexico City, Mexico - [Senior AI Engineer - Core](https://feeny.ai/job/senior-ai-engineer-core-hilbert-s-ai-san-francisco-ak0w5zeq5zgb) — San Francisco, CA - [AI Engineer - Core](https://feeny.ai/job/ai-engineer-core-hilbert-s-ai-san-francisco-2rc16pvkp22h) — San Francisco, CA - [General Application](https://feeny.ai/job/general-application-hilbert-s-ai-world-wide-9z2jhwrp6hj4) — World Wide - [Enterprise Account Executive](https://feeny.ai/job/enterprise-account-executive-hilbert-s-ai-new-york-c8h9q1dev372) — New York, NY - [Senior Marketing Manager](https://feeny.ai/job/senior-marketing-manager-hilbert-s-ai-san-francisco-qxbrwv6k9drp) — San Francisco, CA - [Privacy & Product Counsel](https://feeny.ai/job/privacy-product-counsel-hilbert-s-ai-london-ha0g6ps4mnkd) — London, United Kingdom - [Product Marketing Lead](https://feeny.ai/job/product-marketing-lead-hilbert-s-ai-san-francisco-a1q3fhnymenp) — San Francisco, CA