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AI Software Engineer

Trabajo disponible en: 08001, Barcelona, Cataluna, España
Empresa: Pensero Inc.
Tiempo completo posición
Publicado en 2026-01-24
Especializaciones laborales:
  • TI/Tecnología
    Ingeniero de IA, Machine Learning
Rango Salarial o Referencia de la Industria: 50000 - 70000 EUR Anual EUR 50000.00 70000.00 YEAR
Descripción del trabajo

Barcelona HQ — Hybrid (Tues & Thurs in the office)

Help us reinvent how careers grow, powered by AI.

Our mission

Careers today move at the speed of bureaucracy with complex, slow, and shaped by outdated, biased, and incomplete information.

We’re here to change that. Our mission is to give leaders and employees the clarity, speed, and fairness they deserve, powered by AI at the core.

About us

We’re a tiny but mighty team of senior technologists and operators, building the product we always wished existed. At Pensero.ai
, AI isn’t a bolt-on, it’s the foundation. Every product decision, technical choice, and operational process is designed with AI in mind, which means we build faster, smarter, and very differently from traditional tech companies.

The opportunity

, every hire moves the needle and in AI, the needle is the product.

We’re looking for one exceptional AI Engineer to take ownership of our models, prompts, infrastructure, evaluation pipelines, and datasets.

You’ll work in tight, iterative cycles
, turning messy real-world problems into AI-powered features that are reliable, measurable, and ship fast
.

This isn’t a “research-only” role. You’ll move between prompt design, fine-tuning, model selection, eval framework building, and production deployment
, often in the same week. You’ll see your work in the hands of users within days, not months, and will shape how AI works inside Pensero for years to come.

What you’ll do
  • Own the AI pipeline end-to-end
    : from prompt design to model deployment, making sure every step is measurable, reproducible, and production-ready.
  • Select & integrate the right models
    : open-source models balancing speed, accuracy, cost, and maintainability.
  • Design and maintain evaluation frameworks so we know exactly when a model, prompt, or dataset change is an improvement.
  • Build and manage AI infrastructure
    : fine-tuning workflows, inference APIs, for scale and reliability.
  • Curate and generate datasets for training, testing, and benchmarking, including building the tooling to automate this.
  • Collaborate directly with product & engineering to decide not just how to implement AI features, but which ones we should build in the first place.
  • Ship, learn, repeat
    : deploy to production quickly, monitor, and iterate based on real-world feedback.
You’ll love it here if you…
  • Own your work: you thrive when you have full responsibility and clear trade-offs.
  • Ship, learn, repeat: you believe “good and out” beats “perfect and never shipped.”
  • Live to learn
    : AI excites you because it’s transformative, not just trendy.
  • Bring experience that counts: 5+ years in software engineering, ideally including time in a seed or Series‑A startup.
  • Build fast, think scale: you can prototype quickly and still design for the long term
  • Speak Python fluently: Django a big plus.
  • Care about impact: you want your code to solve real problems, not just tick tickets.
  • Play hard, no ego: you like to challenge and be challenged in a respectful, high‑intensity environment.
This role might not be your sweet spot if…
  • You like working from a fixed checklist more than charting your own path.
  • You’re happiest when things stay the same week to week.
  • You’d rather stay in your comfort zone than experiment and learn on the fly.
  • Picking up new skills or tools regularly feels more like a chore than an opportunity.
  • You struggle to let go of a project until it’s absolutely flawless.
  • Experience in AI beyond just LLMs — embeddings, retrieval, fine‑tuning, multimodal models.
  • Hands‑on work with LLMOps: prompting, RAG pipelines, evaluation, guardrails.
  • Knowledge of vector DBs (e.g., Weaviate, Milvus, Pinecone) and search/retrieval systems.
  • Building and running eval pipelines that mix automated metrics and human feedback.
  • Familiarity with hybrid model setups — mixing self‑hosted and provider‑based.
  • Contributions to open‑source AI projects.
  • Ideas from adjacent fields (search, NLP, distributed systems) that could sharpen our approach.
How we work
  • Hybrid rhythm: Tuesdays & Thursdays on‑site in our offices, other days from home. It’s the best of both worlds: deep focus time plus high‑value in‑person collaboration.
  • Small, senior, sharp: Fewer than 10 engineers, all senior. You’ll work…
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