Modal

4 months ago

Forward Deployed Engineer - ML

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 14 hours agoStatus: Live

Job Summary

Forward Deployed ML Engineer who partners with leading AI companies to optimize production AI workloads (LLM serving, model training, audio pipelines, scientific computing) and work directly with customers. You will collaborate with product/sales teams, contribute to open-source, and demonstrate Modal’s platform capabilities through hands-on engineering and technical demos.

Location: Stockholm

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Work hands-on with customers to architect and optimize production AI workloads on Modal
•Contribute to open-source projects and publish technical content showcasing Modal's capabilities across the AI stack
•Collaborate with Modal's product and sales teams, contributing as both an engineer and a product stakeholder
•Build trusted relationships with technical leaders at companies doing frontier AI work
•Conduct technical demos, experiments, and proofs-of-concept that illustrate Modal's performance advantages

Key Requirements

•2+ years of professional ML engineering experience, ideally with hands-on work in inference optimization, model training, GPU programming, or ML infrastructure
•Familiarity with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains. You don't need all of these, but you should be able to go deep on at least one
•Strong communicator who can go deep on technical architecture with an engineering team and clearly articulate tradeoffs to technical leadership
•Genuine interest in working directly with customers — you find it energizing to understand someone else’s problem and help them solve it
•Bonus: side projects, open-source contributions, or published work you're proud of in ML or systems performance

Experience:2+ yearsAIML infrastructureInference optimizationGPU programming

Skills:CommunicationProblem-solvingTeam collaboration

Tech Stack:VLLMSGLangGPU programmingML infrastructureInference optimizationSFTRLHFSeabornLuigi

Company Brief

Modal

Provides a serverless, high-performance cloud platform for AI, ML, and data workloads — offering instant autoscaling, elastic GPU access, and developer-first tooling to run inference, training, and batch jobs at scale.

Industry: Cloud Computing

Company Size: Small (11 to 50 employees)

Growth: Growth Stage Startup

Valuation: Unicorn (USD 1B+)

Funding: Series B

Headquarters: New York City, United States

Founded: 2021

Website LinkedIn

Loading job details...

Preparing the role view and application actions.

Modal

4 months ago

Forward Deployed Engineer - ML

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 14 hours agoStatus: Live

Job Summary

Location: Stockholm

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Work hands-on with customers to architect and optimize production AI workloads on Modal
•Contribute to open-source projects and publish technical content showcasing Modal's capabilities across the AI stack
•Collaborate with Modal's product and sales teams, contributing as both an engineer and a product stakeholder
•Build trusted relationships with technical leaders at companies doing frontier AI work
•Conduct technical demos, experiments, and proofs-of-concept that illustrate Modal's performance advantages

Key Requirements

•2+ years of professional ML engineering experience, ideally with hands-on work in inference optimization, model training, GPU programming, or ML infrastructure
•Familiarity with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains. You don't need all of these, but you should be able to go deep on at least one
•Strong communicator who can go deep on technical architecture with an engineering team and clearly articulate tradeoffs to technical leadership
•Genuine interest in working directly with customers — you find it energizing to understand someone else’s problem and help them solve it
•Bonus: side projects, open-source contributions, or published work you're proud of in ML or systems performance

Experience:2+ yearsAIML infrastructureInference optimizationGPU programming

Skills:CommunicationProblem-solvingTeam collaboration

Tech Stack:VLLMSGLangGPU programmingML infrastructureInference optimizationSFTRLHFSeabornLuigi

Company Brief

Modal

Industry: Cloud Computing

Company Size: Small (11 to 50 employees)

Growth: Growth Stage Startup

Valuation: Unicorn (USD 1B+)

Funding: Series B

Headquarters: New York City, United States

Founded: 2021

Website LinkedIn