Inference Specialist, Creative Technology - InterPositive

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.

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.

The ideal candidate is deeply technical, operationally calm, and comfortable working in an R&D environment where models, infrastructure, datasets, and creative expectations change quickly.

Responsibilities:

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

Qualifications:

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

Skills:

Strong practical understanding of generative AI inference workflows, especially for video,…