Forward Deployed Engineer - ML/MLOps

Company: Modal (https://modal.com/)

Location: Stockholm

Type: On-site role, Full-time

About the company:

Modal provides the infrastructure foundation for AI teams. With instant GPU access, sub-second container startups, and native storage, Modal makes it simple to train models, run batch jobs, and serve low-latency inference. Companies like Suno, Lovable, and Substack rely on Modal to move from prototype to production without the burden of managing infrastructure.

They're a fast-growing team based out of Stockholm, NYC, and SF.  They've hit high 8-figure ARR and recently raised a Series B at a $1.1B valuation. They have thousands of customers who rely on them for production AI workloads, including Lovable, Scale AI, Substack, and Suno.

Working at Modal means joining one of the fastest-growing AI infrastructure organisations at an early stage, with many opportunities to grow within the company. Their team includes creators of popular open-source projects (e.g. Seaborn, Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.

The role

This role sits at the intersection of engineering, product, and customers. You will work closely with Account Executives and the broader team, but your primary focus is technical: understanding complex infrastructure, designing robust solutions, and helping teams migrate and build on Modal. You will:

• Work directly with customers (such as Lovable, Suno, and Mistral AI) to understand their existing infrastructure, constraints, and goals

• Lead technical discovery sessions and translate real-world requirements into concrete architectures on Modal

• Design and present compelling technical solutions that demonstrate how Modal addresses customer needs

• Architect migration paths from existing cloud infrastructure (AWS, GCP, Azure) to Modal's serverless platform

• Conduct technical demos, experiments, and proof-of-concepts that showcase Modal's capabilities

• Navigate complex technical evaluations and address security, compliance, and integration concerns

• Build trusted advisor relationships with technical decision-makers including CTOs, VPs of Engineering, and ML Engineering leads

• Collaborate with product and engineering teams to communicate customer feedback and influence product roadmap

• Support contract negotiations by providing technical expertise on implementation timelines, resource requirements, and success me

We are looking for somebody who has

• Several years of professional experience in ML engineering, MLOps, or infrastructure-focused roles

• Hands-on experience with parts of the ML lifecycle such as model training, inference, infrastructure, or performance optimization (experience with GPUs is a plus)

• Familiarity with modern ML training or serving ecosystems (for example tools like vLLM, SGLang, TRL, or similar). You don’t need to know all of these, but you should feel comfortable diving deep into at least one area

• Excellent communication skills — able to dive into technical architecture with engineers and clearly explain tradeoffs to leadership

• Genuine interest in working directly with customers — you enjoy understanding their problems and helping solve them

Bonus points for

• Side projects, open-source contributions, or published work in ML or systems performance

Why join Modal?

• Work on cutting-edge AI/ML infrastructure used by innovative startups and enterprise clients

• Be part of a highly technical, ambitious, and supportive team

• Influence product development and customer success at scale

• Great learning opportunities and potential for career growth in a fast-growing company

Sound like a fit? We’d love to hear from you!

For more information or questions, contact zoe.lepluart@ninox-limited.com.