2 days ago
London, United KingdomMid Level
Responsibilities
- Architect and optimize production AI workloads on Modal for customers such as Suno, Cognition, and Meta.
- Work on LLM serving, model training including SFT and RLHF, audio pipelines, scientific computing, and other demanding AI workloads.
- Conduct technical demos, experiments, and proofs of concept demonstrating Modal’s performance advantages.
- Collaborate with product and sales teams as an engineer and product stakeholder.
- Build trusted relationships with CTOs, VPs of Engineering, and ML leads.
- Contribute to open-source projects and publish technical content about Modal’s capabilities.
Requirements
- At least 2 years of professional ML engineering experience.
- Hands-on experience ideally covering inference optimization, model training, GPU programming, or ML infrastructure.
- Familiarity with ML serving and training toolchains such as vLLM, SGLang, slime, verl, and TRL, with depth in at least one.
- Strong communication skills for discussing technical architecture and tradeoffs with engineering teams and technical leaders.
- Genuine interest in working directly with customers.
- Willingness to work in person in London.
- Preferred qualifications include ML or systems-performance side projects, open-source contributions, or published work.
Benefits
- The role is based in London and requires working in person.
- The position offers direct work with leading AI companies and foundation model labs, plus opportunities to contribute to open-source projects and publish technical content.
Tech Stack
Seaborn
Categories
Forward Deployed
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.