7 months ago
Stockholm, SwedenMid Level
Responsibilities
- Architect and optimize production AI workloads on Modal for customers including Suno, Lovable, Cognition, and Meta.
- Work on workloads including LLM serving, model training, audio pipelines, and scientific computing.
- Conduct technical demos, experiments, and proofs of concept that demonstrate Modal’s performance advantages.
- Collaborate with Modal’s product and sales teams as an engineer and product stakeholder.
- Build trusted relationships with CTOs, VPs of Engineering, and ML leads at frontier AI companies.
- Contribute to open-source projects such as SGLang and publish technical content about Modal’s capabilities across the AI stack.
Requirements
- At least 2 years of professional ML engineering experience, ideally involving inference optimization, model training, GPU programming, or ML infrastructure.
- Familiarity with ML serving or training toolchains such as vLLM, SGLang, slime, verl, and TRL, with the ability to go deep on at least one.
- Strong communication skills for discussing technical architecture and tradeoffs with engineering teams and technical leadership.
- Genuine interest in working directly with customers to understand and solve their problems.
- Side projects, open-source contributions, or published ML or systems-performance work are a bonus.
- Willingness to work in person in Stockholm.
Benefits
- In-person work in Stockholm
- Opportunities to contribute to open-source projects and publish technical content
- Collaboration with leading AI companies and foundation model labs
Tech Stack
Seaborn
Categories
Forward DeployedML Engineering
About Modal
Modal builds a serverless compute platform for AI and data workloads, offering instant GPU access, sub-second container starts, and native storage to run inference, fine-tuning, and batch jobs. It sells a usage-based cloud service to developers and ML teams to deploy generative models and pipelines. Privately held and headquartered in New York City, its customers include companies like DoorDash and Ramp.