Loft
Space
HeadofAIEngineering
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“Head of AI Engineering at Loft. Skills: AI Engineering, ML/AI systems, production ML systems, ML infrastructure, model serving, deployment pipelines. owning the technical strategy and execution of the AI Marketplace and Loft Labs ML tooling. defining what gets built and how”
What You'll Achieve.
shipping production ML systems; AI Marketplace; Loft Labs ML tooling; AI services deployed on the platform; AI services engineers; end-to-end solutions on the marketplace; ML model deployment; inference pipelines; edge compute; third-party AI models into the platform; sandbox, testing, and certification pipeline for AI services; tasking interfaces; orchestration; regulatory compliance layer
Industry & Context.
What They're Looking For.
Must Have
7-15 years of hands-on engineering experience, meaningful time building ML/AI systems, Track record of shipping production ML systems, Experience building and managing engineering teams (3-10 people), Comfortable across the stack: model optimization, API design, infrastructure, and team leadership
Nice to Have
Experience with edge compute, embedded ML, or on-device inference (NVIDIA Jetson, TensorRT, ONNX), Background in space, defense, or geospatial systems, Familiarity with satellite operations, Earth observation data, or RF signal processing
What You'll Do.
owning the technical strategy and execution of the AI Marketplace and Loft Labs ML tooling
defining what gets built and how
bridging the gap between research and production
setting the technical standard for every AI service deployed on the platform
Define and own the technical roadmap for the AI Marketplace and all AI services
Bridge Loft Labs research outputs and production-grade service delivery
Hire and lead a team of AI services engineers
setting architecture standards for ML model deployment
and certification pipeline for AI services
How You'll Work.
Team & Collaboration
work closely with Loft’s broader engineering, Solutions, and Architecture (CTO platform) teams; ensure seamless integration of AI services with Cockpit, Loft’s satellite operations platform, and the partner ecosystem; establishing clear boundaries between marketplace and ML-specific capabilities owned by the AI team and those developed by existing platform teams; Collaborate closely with the rest of engineering; Work with partners; Collaborate with Cockpit and ground software teams
Communication Scope
Direct, low-ego communication; explain your reasoning; change your mind when evidence warrants it
Full Job Description
## Description Wanna join the adventure? AI for Space is Loft’s newest business unit, focused on building the Loft Mission Store - an open platform where AI companies publish models, satellite operators deploy them in orbit, and the system delivers structured intelligence rather than raw imagery. The team is lean, the ambition is significant, and the satellites are already operational. As Head of AI Engineering, you are the most senior technical leader within the AI for Space team, responsible for owning the technical strategy and execution of the AI Marketplace and Loft Labs ML tooling. You define what gets built and how, bridging the gap between research (Loft Labs) and production (implementation engineers), and setting the technical standard for every AI service deployed on the platform. Reporting to the General Manager, AI for Space, you work closely with Loft’s broader engineering, Solutions, and Architecture (CTO platform) teams to ensure seamless integration of AI services with Cockpit, Loft’s satellite operations platform, and the partner ecosystem. A key part of the role is establishing clear boundaries between marketplace and ML-specific capabilities owned by the AI team and those developed by existing platform teams. ## About this role Define and own the technical roadmap for the AI Marketplace and all AI services, working closely with the Head of Product Management. Bridge Loft Labs research outputs and production-grade service delivery. Hire and lead a team of AI services engineers (initially 2-3, growing to 5-7) responsible for the AI-specific services that partners and solution engineers need to build end-to-end solutions on the marketplace (data and model sandboxes, test environments, simulation, super resolution, multimodal orchestration). Collaborate closely with the rest of engineering to set architecture standards for ML model deployment, inference pipelines, and edge compute (Hub Compute). Work with partners (Helsing, Mistral, NVIDIA, Kayrros, a
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