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About This Role
Position Summary:
The VFX AI Pipeline Engineer is responsible for designing, developing, and maintaining the infrastructure that integrates artificial intelligence and machine learning into Wellcom's CGI and visual effects production pipeline. Working within HOME, Wellcom's AI\-enabled production ecosystem, this role bridges traditional VFX applications with AI\-native technologies to create scalable, artist\-friendly workflows that enhance efficiency without compromising creative quality.
Acting as the connection between software engineering, pipeline development, and production, the VFX AI Pipeline Engineer builds intelligent tools that automate repetitive processes, accelerate asset creation, and enable creative teams to leverage AI throughout the production lifecycle. This role collaborates closely with CGI Supervisors, Compositing Artists, Developers, Creative Technologists, and Production teams to continuously evolve HOME's AI capabilities.
Key Responsibilities:
*AI Pipeline Integration*
- Architect, develop, and maintain AI\-enhanced production solutions that integrate seamlessly into existing VFX and CGI pipelines.
- Build custom tools, APIs, and services that connect traditional DCC applications—including 3ds Max, Maya, Houdini, Nuke, and Fusion—with AI generation platforms and machine learning models.
- Ensure AI workflows are scalable, secure, reliable, and production\-ready within HOME.
- Collaborate with pipeline engineers and software developers to optimize performance and interoperability across production systems.
*Workflow Automation*
- Design, build, and maintain automated AI workflows using ComfyUI, Python, and proprietary workflow frameworks.
- Standardize AI\-assisted asset generation, image enhancement, look development, and production processes across creative teams.
- Develop reusable automation pipelines that improve production speed, consistency, and scalability.
- Monitor workflow performance and continuously optimize processing efficiency.
*Model Fine\-Tuning \& AI Development*
- Manage the training, fine\-tuning, and deployment of custom AI models including LoRAs, ControlNets, IP\-Adapters, and related machine learning assets.
- Maintain model libraries, checkpoints, version control, and documentation to support project consistency and governance.
- Optimize AI models for specific client aesthetics, character consistency, product visualization, and brand standards.
- Evaluate emerging AI technologies and recommend production\-ready implementations.
*Technical Artist Tools*
- Develop intuitive tools, interfaces, and scripts that enable artists to utilize AI capabilities without requiring advanced programming knowledge.
- Create integrations within Nuke, Houdini, 3ds Max, Maya, and other production applications that support AI\-powered workflows such as texture synthesis, depth\-map generation, image upscaling, rotoscoping, segmentation, and asset enhancement.
- Build artist\-focused workflows that balance automation with creative control.
- Improve usability through documentation, training, and ongoing technical support.
*Collaboration \& Innovation*
- Partner with CGI Supervisors, Compositing Artists, Creative Technologists, AI Developers, and Production teams to identify opportunities for AI\-enabled workflow improvements.
- Support the evolution of HOME by researching, testing, and implementing emerging AI technologies that enhance production capabilities.
- Establish best practices, technical documentation, and governance standards for AI\-assisted production.
- Champion responsible, ethical, and secure implementation of AI technologies throughout the production pipeline.
Required Qualifications:
- Bachelor's degree in Computer Science, Software Engineering, Visual Effects, Computer Graphics, Artificial Intelligence, or equivalent professional experience.
- 5\+ years of experience in VFX pipeline development, technical direction, software engineering, or AI workflow development.
- Strong understanding of VFX production pipelines and digital content creation workflows.
- Advanced programming experience with Python.
- Experience integrating software applications through APIs and automation frameworks.
- Excellent problem\-solving, communication, and cross\-functional collaboration skills.
Preferred Qualifications:
- Experience developing AI\-enabled production pipelines for CGI, VFX, or creative production environments.
- Experience with ComfyUI, Stable Diffusion, Flux, or other generative AI frameworks.
- Knowledge of machine learning concepts, inference optimization, and model fine\-tuning.
- Experience training and managing LoRAs, ControlNets, IP\-Adapters, or similar AI models.
- Familiarity with cloud computing, GPU infrastructure, and distributed AI processing.
- Experience working within commercial advertising, production, or animation studios.
Technical Skills:
*Programming \& Development*
- Python
- REST APIs
- Git
- Docker
- Workflow automation
- Pipeline development
*AI \& Machine Learning*
- ComfyUI
- Stable Diffusion
- Flux
- LoRA training
- ControlNet
- IP\-Adapters
- Model inference
- Prompt engineering
- AI workflow orchestration
*VFX \& DCC Applications*
- Autodesk Maya
- 3ds Max
- Houdini
- Nuke
- Fusion
*Pipeline \& Infrastructure*
- Asset management
- Pipeline architecture
- GPU rendering
- Cloud infrastructure
- Version control
- AI model management
Success in This Role:
A successful VFX AI Pipeline Engineer will:
- Build reliable, scalable AI infrastructure that seamlessly integrates with Wellcom's VFX and CGI production pipeline.
- Empower artists through intuitive AI tools that enhance creativity while reducing repetitive manual work.
- Optimize AI workflows to improve production efficiency, consistency, and quality.
- Collaborate effectively across engineering, creative, and production teams to deliver innovative solutions.
- Help shape the future of HOME by advancing AI\-enabled production capabilities while maintaining the highest standards of technical excellence, artistic integrity, and operational reliability.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At thelab, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
thelab AI Hiring
thelab has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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