What you'll be doing:
Drive architecture, applied research, and hands-on development by defining and building AI-native software engineering solutions.
Design and build AI-powered development pipelines – from code generation and automated review to feedback loops and evaluation systems.
Evaluate and select ML approaches for specific problems: when to use LLM prompting vs. fine-tuning (QLoRA), classical ML (random forest, linear regression) vs. reinforcement learning, RAG vs. structured extraction.
Architect feedback and evaluation systems that measure and improve AI output quality over time.
Review and refine AI solution architectures – evaluate design decisions, identify weaknesses, propose alternatives with reasoning.
Lead proof-of-concept development to validate new AI/ML approaches for development tooling.
Collaborate with the core team to define risk-based development levels and calibrate AI review depth per level.
What we need to see:
Hold a M.Sc. or Ph.D. in Computer Science, Electrical or Computer Engineering from a leading university (or equivalent experience).
5+ years of industry experience (or equivalent) in software architecture, hands-on development, AI/ML, applied research, or related fields.
Strong background in software or solution architecture, applied AI/ML research, or hands-on development of production-grade AI systems.
Industry experience building and shipping AI-powered tools or ML pipelines (not just training models – end-to-end delivery).
Strong understanding of LLM capabilities and limitations – prompt engineering, fine-tuning, RAG, agent architectures.
Experience with at least two of: reinforcement learning, classical ML, NLP/information retrieval, evaluation framework design.
Strong understanding of AI development and evaluation pipelines.
Can reason about trade-offs: when to use which approach, with real reasoning backed by shipping experience.
Strong programming skills (Python required; familiarity with ML frameworks – PyTorch, HuggingFace, etc.).
Ability and flexibility to work and communicate effectively in a multi-national, multi-time-zone corporate environment.
Ways to stand out from the crowd:
Experience with LLM-based code generation, code review, or developer tooling.
Familiarity with eval frameworks and feedback loop design (online and offline evaluation).
Experience with AI agent orchestration (multi-agent systems, tool use, planning).
Shown research track record (publications, open-source contributions).
Knowledge of AI-assisted development tools and their underlying architectures.
