Lovable
Coding AI · AI app builder (vibe coding)
Currently tracking 7 active AI roles, down 16% versus the prior 4 weeks. Primary focus: Agent · Engineering.
Coding AI · AI app builder (vibe coding)
Lovable currently has 11 active AI-related job listings. The majority of these roles, 64%, are focused on agents. Engineering is the dominant function with 9 roles, and the primary hiring country is Sweden with 9 positions. The company is frequently seeking expertise in agent orchestration, model serving, and LLM observability.
Lovable currently has 11 active AI-related roles in our index. The most common open titles are: Engineering Manager, Forward Deployed Engineer, GTM Engineering Lead, Head of Customer Experience, Penetration Tester. Most positions are in Engineering and Product.
Lovable's active AI hiring is concentrated in: agents (64%), post-training (18%), application (9%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Lovable is hiring AI talent in: Sweden (9 roles), United States (1 role), United Kingdom (1 role).
Job postings at Lovable most frequently mention: Code Generation, AI Safety, System Design, Product Impact, CI/CD.
In the past 30 days, Lovable has posted 4 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Researcher, Post Training Lovable is seeking an engineer with experience in post-training large language models at scale to own their full post-training pipeline. This role involves translating research into production training recipes for code generation and agent workloads, with a focus on shipping improved models quickly. The position requires strong production code skills, familiarity with ML frameworks like PyTorch or JAX, and an understanding of preference optimization, reward modeling, and alignment techniques. The engineer will also build evaluation systems and operate production systems for training jobs. | Post-trainAgent | 9 |
| Research Engineering Lead Research Engineering Lead to optimize performance of frontier LLMs for AI software engineering products. Owns experiments from dataset design to model alignment and performance optimization. Focus on training pipelines, distillation, and synthetic data generation, partnering with infra and product engineers to scale and ship improvements. |
| Post-trainServe |
| 8 |