Specialist, Creative Technology - InterPositive

Netflix•Los Angeles, CA

124d

About The Position

The 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.

Requirements

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, ProRes, H.264/H.265, EXR, PNG, MP4/MOV containers, resolution handling, frame rates, and colorspace 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

Nice To Haves

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, NumPy, safetensors, and distributed launch tools
Comfort with job schedulers, cloud GPU environments, or cluster workflows; Slurm experience is a strong plus
Careful eye for generated output quality, including temporal artifacts, mask errors, motion issues, color shifts, compression problems, and sync problems
Able to balance creative iteration speed with technical rigor, reproducibility, and clear communication
Self-directed and ownership-minded; comfortable seeing a messy problem, creating a path through it, and pulling in help when needed
Collaborative and calm under pressure, especially when supporting time-sensitive creative reviews or production deadlines
Strong written communication, including the ability to document workflows, summarize test results, and explain technical findings to non-technical partners
Comfort with ambiguity, rapidly changing tools, and incomplete information
Genuine interest in tooling for filmmakers, with the curiosity to engage deeply with both the creative possibilities and the engineering realities of the work

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