Inference Specialist, Creative Technology - InterPositive

The Inference Specialist, Creative Technology will report to the Sr. Director, Creative Technology and support the Production, Research, and Engineering teams working at the frontier of storytelling innovation. This role owns the practical execution of model inference workflows, translating creative needs into reproducible runs, debugging complex generation issues, and helping build reliable pipelines by turning rapidly evolving research code into reliable creative production workflows.

• Operate and support custom generative AI inference workflows across a wide variety of film and series projects
• Run, monitor, and troubleshoot GPU‑based inference jobs across local workstations, cloud infrastructure, and/or cluster environments, including distributed multi‑GPU runs
• Prepare and validate inputs for model inference, including video, image, audio, masks, conditioning assets, prompts, metadata, and configuration files
• Tune inference parameters in collaboration with Creative Technology leadership, artists, researchers, and engineers to achieve production‑quality results
• Debug failed or degraded runs by inspecting logs, outputs, configs, model checkpoints, data shapes, masks, frame ranges, codecs, GPU utilization, and environment issues
• Maintain clean, repeatable inference launch workflows, including scripts, config templates, run manifests, output naming conventions, and result tracking
• Partner with researchers and engineers to test new models, checkpoints, samplers, conditioning methods, and pipeline changes in real production scenarios
• Translate experimental model capabilities into usable production practices
• Identify friction in inference workflows and drive improvements through tooling, automation, documentation, and better defaults
• Support rapid iteration with artists and creative stakeholders by preparing outputs for review, comparing variations, tracking parameters, and surfacing clear recommendations
• Own quality control for generated outputs
• Help bridge communication between creative, production, research, and engineering teams by explaining technical constraints and creative tradeoffs clearly
• Maintain awareness of GPU capacity, queue status, runtime expectations
• Contribute to a culture of practical experimentation: move quickly, test carefully, document learnings, and turn one‑off fixes into repeatable workflows

• 4+ years of relevant experience in machine learning production, VFX technology, post‑production engineering, creative technology, technical direction, or a closely related technical production role
• Hands‑on experience running GPU‑based model inference for image, video, audio, or multimodal generative AI systems
• Experience working with Python‑based ML codebases and command‑line workflows in Linux environments
• Experience debugging production runs using logs, stack traces, configuration files, model inputs, and generated outputs
• Working knowledge of deep learning inference concepts, including checkpoints, schedulers or samplers, seeds, precision, batching, conditioning, and GPU memory constraints
• Experience with video and image production formats, including frame sequences, Pro Res, H.264/H.265, EXR, PNG, MP4/MOV containers, resolution handling, frame rates, and color space considerations
• Experience coordinating technical work across creative, production, research, and engineering stakeholders
• Demonstrated ability to operate effectively in a fast‑moving R&D environment where tools, models, and workflows change frequently

• Strong practical understanding of generative AI inference workflows, especially for video, image, audio, or multimodal models
• Comfort working in Linux shells, Python environments, Git repos, config files, logs, and GPU infrastructure
• Strong debugging instincts: able to isolate whether a problem is data, model, environment, code, infrastructure, or user configuration
• Ability to reason about video and tensor fundamentals, including frame counts, aspect ratios, spatial resolution, temporal alignment, masks, channels, and batch dimensions
• Experience with tools and libraries commonly used in production ML workflows, such as PyTorch, CUDA, ffmpeg, OpenCV, Num Py,…