We're hiring
Hey, we're Aim. We monitor competitors, regulators, and markets for business teams, continuously, and surface what actually changed.
Small Prague team, $1M from Miton and Purple Ventures. Most of our codebase is written by AI, reviewed by humans who know how to think. We ship daily. Short loops, no PM handoffs.
We hire people who drive a product, not just close tickets.
Open roles
3 positionsOwn the ingestion and inference backbone: distributed pipelines, LLM orchestration, and the reliability underneath it.
What you'll do
- Design and operate distributed, event-driven systems serving production traffic
- Own cost, throughput, and reliability tradeoffs across LLM-heavy workloads
- Partner with AI engineers on retrieval, evals, and latency
- Build observability that makes async systems legible
About you
- Event-driven systems in production, at real traffic
- Strong Python (FastAPI, asyncio); TypeScript when needed
- Cost and throughput as first-class concerns
- Shipped at seed/Series A, no specs handed to you
- Claude Code daily; parallel agents is normal
Nice to have
- Production LLM orchestration
- Vector DBs
- Scrapers at scale
- GCP
Own the customer-facing AI surfaces: prompts, agent flows, and the evals that prove they work.
What you'll do
- Ship LLM features end-to-end, from prompt to UI
- Design for how models actually behave: streaming, uncertainty, agent steps, failure modes
- Build evals that separate 'demos well' from 'actually works'
- Drive product decisions, not just implementation
- Cross fluently between TypeScript and Python
About you
- LLM feature shipped to production users in the last 18 months
- Fluent in TS/Next.js and Python, no boundary handoffs
- Instincts for how models fail: streaming, uncertainty, fallbacks
- Built evals for own features; 'demos well' vs 'works'
- Startup shipping; drives product, not just implementation
- Claude Code daily
Nice to have
- AI SDK, LangChain, or Agents SDK in production
- Retrieval: embeddings, reranking, hybrid
- Agent or tool-use features in production
- Figma to shippable UI without a frontend specialist
Work on the LLM core: ranking, retrieval, prompting, evals, agents. Early-career role for a CS grad treating LLMs as infrastructure, not demos.
What you'll do
- Tune retrieval over large source collections
- Design and run evals that catch regressions before they ship
- Prototype agent workflows; ship the ones that work
- Benchmark against the frontier; close the gaps
- Read papers, implement what matters
About you
- Strong CS foundations; MSc/PhD in progress or recently done
- Built something real with LLMs (agent, RAG, benchmark, research) and can defend the design
- Solid Python; clean, testable code
- Reads papers and implements ideas from them
- Output: research, competitions, open source, or shipped projects
- Claude Code daily
Nice to have
- LangChain, OpenAI Agents SDK, or DSPy in production
- Evaluation frameworks (ragas, LLM-judge harnesses)
- Kaggle or ML competition placements
- Startup or research-lab internship
- Fine-tuning, GRPO, DSPy tricks