About the job
About Us:
Modal is revolutionizing the AI infrastructure landscape. We provide seamless access to GPUs, rapid container startups, and integrated storage solutions, making it effortless for teams to train models, execute batch jobs, and deliver low-latency inference. Notable companies such as Suno, Lovable, and Substack trust Modal to transition from concepts to production while alleviating infrastructure management burdens.
Based in New York City, San Francisco, and Stockholm, we are a rapidly expanding team that has achieved a 9-figure ARR and recently secured a Series B funding round at a valuation of $1.1 billion. With thousands of customers relying on us for their production AI workloads, our clientele includes industry leaders like Lovable, Scale AI, Substack, and Suno.
Joining Modal means becoming part of one of the most dynamic AI infrastructure teams at an early stage, opening doors for personal and professional growth. Our team comprises creators of widely-used open-source projects (e.g., Seaborn, Luigi), distinguished academic researchers, international competition medalists, and seasoned engineering and product leaders with decades of experience.
The Role:
Modal is on the lookout for a skilled Forward Deployed Machine Learning Engineer (FDE) to collaborate with our sales team and spearhead technical sales initiatives. As an FDE, you will serve as the technical expert in our sales engagements, partnering with Account Executives to help enterprise customers grasp how Modal can transform their AI/ML infrastructure. Your responsibilities will include:
Collaborating with Account Executives to identify, evaluate, and finalize strategic enterprise opportunities
Leading technical discovery sessions with potential clients to assess their current infrastructure, pain points, and specific requirements
Crafting and presenting compelling technical solutions that illustrate how Modal meets client needs
Conducting technical demonstrations, experiments, and proof-of-concept projects that highlight Modal's capabilities
Navigating complex technical evaluations while addressing security, compliance, and integration concerns
Establishing trusted advisor relationships with technical decision-makers, including CTOs, VPs of Engineering, and ML Engineering leads
Working alongside product and engineering teams to relay customer feedback and influence the product roadmap

