Senior Generative Video ML Engineer, Post-Training & Fine-Tuning

We are hiring a senior, hands-on ML engineer for a focused 60-day generative-video model sprint. The work centers on open-weight video models, reproducible inference, controlled post-training or adaptation, and measurable improvement in character, scene, and temporal consistency. This is not a prompt-engineering role and not a general AI-app build. We need someone who has personally trained, adapted, evaluated, and debugged modern video-generation models and can show what changed, why it changed, and how the result was measured. What you will own: - Stand up a reproducible, versioned inference and experimentation environment for one or more open-weight video-generation models. - Establish frozen baseline results before adaptation begins. - Inspect data readiness, define train, validation, and sealed holdout splits, and prevent identity or scene leakage. - Design and run license-permitted post-training experiments using the lightest justified method, such as LoRA, adapter training, supervised tuning, preference optimization, or conditioning changes. - Build or integrate structured character and scene conditioning while preserving immutable run manifests. - Measure identity consistency, appearance continuity, scene adherence, action fidelity, temporal stability, latency, and cost. - Diagnose failures and compare controlled remediation strategies such as repair, retry, reroute, or rejection. - Package code, configs, run logs, checkpoints or adapters, evaluation results, architecture notes, and a complete technical handover. Environment and constraints: - All proprietary data and resulting artifacts remain inside a controlled private environment. - No data may be copied to personal storage or external inference services. - Training and model use must follow checkpoint-specific commercial-license, data-rights, consent, provenance, security, and territory approvals. - We will begin with a bounded evidence phase, not an unrestricted production training run. - Results will be judged against frozen baselines and held-out cases. We do not accept self-selected demos as proof. Required experience: - Deep hands-on experience with diffusion or flow-based video generation, transformer-based video models, or closely related multimodal generation systems. - Personally executed post-training or adaptation of an open-weight image or video model using PyTorch. - Strong GPU systems knowledge, including distributed training or inference, memory optimization, mixed precision, checkpointing, and experiment tracking. - Built evaluation pipelines for identity, visual consistency, temporal quality, prompt or scene adherence, or production usability. - Comfortable operating under strict data custody, reproducibility, and evidence requirements. - Able to lead the work personally. A small named support team is acceptable, but the core technical work cannot be delegated without approval. Strong pluses: - Direct experience with model families such as Wan, HunyuanVideo, CogVideoX, LTX Video, Mochi, or comparable systems. - Character-consistency techniques, reference conditioning, identity embeddings, temporal adapters, video inpainting, or localized repair. - Experience optimizing inference on H100 or H200-class infrastructure. - Experience designing blinded human review alongside automated evaluation. To apply, answer every question below. Applications that skip them will not be reviewed. 1. Describe the most relevant video-generation model you personally trained or adapted. Name the base model, method, data scale, compute, your exact contribution, and the measured before-and-after result. 2. How did you evaluate identity consistency and temporal quality on held-out examples? Include metrics, human review, and one failure your evaluation caught. 3. What is the largest multi-GPU training or inference job you personally operated, and what failed during the run? 4. Share one sanitized artifact you can walk through live: code, config, experiment report, evaluation dashboard, or architecture document. 5. Can you commit at least 30 hours per week for 60 days, and will you personally perform the core work?