November 2025
Senior Applied AI Engineer
- Job Title: Senior Applied AI Engineer
- Location: Barcelona, Spain (hybrid; options for Boston/US or remote in some cases)
- Company Type: AI/deep-tech startup (e.g., building reasoning AI systems), research-oriented firm, or multinational tech company
- Employment Type: Full-time.
About the Role
We are building next-generation AI systems that reason rigorously (e.g., with scientific-method-like precision) to solve complex problems in domains like hardware design, semiconductors, engineering, science, or enterprise workflows. As a Senior Applied AI Engineer, you will be the key bridge between AI research/experiments and production software. You own end-to-end delivery: turning promising models or techniques into robust, high-impact product features while ensuring reliability, scalability, and user value.
Key Responsibilities
- Partner with AI researchers/developers to productionize experiments and novel techniques (e.g., improving workflows, reasoning capabilities, or domain-specific applications).
- Design, implement, and iterate on LLM-powered features, including agents, tool-calling, prompting strategies, retrieval-augmented generation (RAG), multi-step reasoning, or interpretable AI systems.
- Build and integrate production-grade infrastructure: evaluations, training data pipelines, APIs, observability, and rapid-iteration frameworks (using tools like PydanticAI, LangChain, Hugging Face, or custom solutions).
- Own deployment and scaling of AI features in production environments (cloud-based or hybrid; focus on reliability, cost-efficiency, latency, and safety).
- Review code, mentor junior AI developers/engineers on best software practices and establish engineering standards for AI systems.
- Collaborate closely with product, users, and cross-functional teams to define requirements, validate impact, and drive real-world outcomes.
- Contribute insights back to frontier model development (e.g., feedback on limitations or needed improvements).
- Lead architecture and development of new AI products/features from 0 to 1, including experimentation, benchmarking, and risk assessment.
Required Qualifications
- 7+ years of strong software engineering experience with production ownership (Python strongly preferred; experience in other languages like Go, C++, or Java a plus for performance-critical components).
- Proven track record deploying and scaling AI/ML systems in production (experience with LLMs, generative AI, agents, or reasoning models highly valued).
- Deep familiarity with modern AI frameworks and tools (e.g., PyTorch, TensorFlow, Hugging Face, LangChain, or equivalent for LLM orchestration).
- Solid engineering fundamentals: design patterns, testing, CI/CD, cloud infrastructure (AWS/GCP/Azure), APIs, and observability/monitoring.
- Bachelor’s/Master’s/PhD in Computer Science, AI, Machine Learning, or related quantitative field (advanced degree often preferred).
- Strong problem-solving skills, ability to handle ambiguity, and passion for bridging research to impactful software.
Preferred Skills / Nice-to-Haves
- Experience with interpretable/reasoning AI, scientific/engineering applications (e.g., EDA, photonics, semiconductors, or hardware design).
- Expertise in evaluations, synthetic data generation, fine-tuning, or influencing frontier model development.
- Knowledge of MLOps for AI (e.g., MLflow, Vertex AI, or custom pipelines) and production challenges (cost, latency, safety/guardrails).
- Domain knowledge in emerging areas like AI acceleration, edge computing, or scientific computing.
- Open-source contributions, publications, or portfolio demonstrating production AI work.
What We Offer
- Opportunity to shape groundbreaking AI systems with significant real-world impact.
- Collaborative environment with top AI researchers, engineers, and hardware experts from leading institutions.
- Competitive compensation, equity (in startups), professional growth, flexible hybrid work, and exposure to cutting-edge tech/events.
How to Apply Submit your resume, cover letter, and relevant portfolio (e.g., GitHub, past projects) via the careers page or LinkedIn.
