Member of Technical Staff, Engineering

Keenable.ai; Polska; Híbrido; PLN 180.000 - 280.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Frontier AI infrastructure work with深技

High autonomy and real production work

Meaningful equity at an early stage

Competitive compensation

Healthcare coverage

401(k) retirement plan

Flexible time off

Descripción de la vacante

Keenable.ai in Poland is seeking an MTS to own and ship AI infrastructure for web-scale knowledge access. You will build retrieval systems spanning crawling, indexing, and model-facing knowledge layers.

You will own projects end to end, iterate rapidly, and apply Rust-based systems to production. The role emphasizes ownership, speed, and turning ambiguity into working systems.

Formación

• Strong systems instincts and performance awareness.
• Ability to turn ambiguity into shipped systems.
• Fast iteration with sound trade-off thinking.
• Experience with concurrency and distributed systems.
• Interest in web-scale retrieval, crawling, indexing, and search infrastructure.

Responsabilidades

• Build and ship retrieval systems for AI access to web-scale information.
• Collaborate across crawling, indexing, query execution, and model-facing infra.
• Own projects end to end, from prototype to production.
• Leverage AI tools and automation to speed iteration.

Conocimientos

Systems thinking

Ambiguity tolerance

Technical judgment

Concurrency

Distributed systems

Web-scale retrieval

Herramientas

Rust

Descripción del empleo

Keenable builds AI infrastructure for web search and knowledge access at web scale. We are building systems and new retrieval primitives that make the world's constantly growing knowledge accessible to models.

If you do not match every requirement, you should still apply. We care much more about slope than polish. We hire for agency, speed, ownership, and the ability to turn ambiguity into working systems. This is an MTS role, which means broad technical ownership, strong judgment, and the ability to ship through ambiguity.

Responsibilities

• Build and ship retrieval systems that make web-scale information accessible to AI systems.
• Work across crawling, indexing, query execution, and model-facing knowledge infrastructure.
• Own projects end to end, from prototype to production.
• Use AI tools, agents, and automation aggressively to increase iteration speed.

Requirements

• Strong systems instincts. You can reason about performance, scaling limits, bottlenecks, and failure modes.
• Comfortable with ambiguity, ownership, and turning open-ended problems into shipped systems.
• Fast iterator with strong technical judgment and clear trade-off thinking.
• Comfortable with concurrency, distributed systems, and production infrastructure.
• Excited by web-scale retrieval, crawling, indexing, search infrastructure, and model-facing knowledge systems.

Perks

• Frontier AI infrastructure work with real technical depth and real scale.
• High autonomy, low process, and real production problems.
• Access to meaningful compute and infrastructure for experimentation.
• Competitive compensation with meaningful early-stage equity.
• A small, high-talent team with direct exposure to frontier customers.
• Premium medical, dental & vision coverage, Health Savings Account (HSA).
• 401(k) retirement plan.
• Flexible time off — we trust you to manage your time and recharge when needed.

Things you should know

• This is startup work. It is intense, ambiguous, and results-oriented.
• Our interview process reflects real work. We evaluate how you operate, not trivia.
• We move quickly when there is mutual excitement.
• We're a Rust-first engineering team — our core systems are built in Rust.

Sample problems you might work on

• Build a polite, scalable crawler dispatch queue with per-domain scheduling, robots.txt compliance, dynamic updates, fairness, concurrency safety, crash recovery, and distributed coordination.
• Design a synthetic internet environment that stress-tests crawler behavior across robots.txt, redirects, failures, throttling, and scale-related edge cases.