A small number of founding engineers and designers.

Gengis.ai is building YAM, a production operating system for film and TV. We work alongside Refinery Media (production) and X3D Studio (virtual production, biggest LED volume in Southeast Asia), which means there's more interesting material walking around our sets in a week than most companies generate in a year, and almost none of it is being captured. We're looking for someone to change that.

• Run our founders' social presence: ghostwrite and edit Karen and Joel across X and LinkedIn. You'll get the tone right and ship daily without needing every post pre-approved.
• Embed with Refinery shoots and X3D volume work to capture behind-the-scenes footage as it happens, then turn it around the same day
• Build a constant output of short vertical content for TikTok, Reels, Shorts, and X video, using whichever tool gets it done fastest, AI or traditional
• Hold a publishing cadence we agree on together. Daily, not weekly.
• Watch the analytics and tell us what's working and what isn't

You don't need every one of these on day one, but you should be fluent in most of them and unafraid of the rest. We hire on velocity, not on which app you prefer.

• Editing and design — CapCut, Premiere or DaVinci Resolve, Photoshop, Figma or Canva
• AI for writing and ideation — ChatGPT, Claude
• AI for image and video — Midjourney or Flux, Runway, Kling, Veo
• AI for audio — ElevenLabs, Descript
• Long-to-short — Opus Clip, Klap, or your own workflow

If you've built a faster pipeline using something not on this list, tell us about it.

• You've grown a founder, brand, or your own account from nothing to something measurable, and can point to specific posts and numbers
• You write hooks people actually click on, and you also know when to step back and let the footage carry the post
• You can shoot and edit vertical video yourself, on a phone, fast, without waiting for a producer
• You're across what's working right now on X, TikTok, Instagram, LinkedIn, and YouTube Shorts, and can explain why
• You're comfortable being embedded with a production crew on a working set

• Film, TV, or production-industry background
• You've ghostwritten for a founder or executive before and have a system for it
• You've trained or fine-tuned an AI workflow that meaningfully sped up your output

Singapore-based, on the ground. This isn't remote, you need to be on Refinery and X3D sets regularly. Full-time.

Email karen@gengis.ai with "Social Content Lead" in the subject, links to three accounts you've grown or run (yours or someone else's), and one paragraph on the best piece of content you've ever shipped and why it worked.

YAM is a production operating system for film and TV: the relay that moves a project from script to delivery without things falling between the cracks, built by people who have actually shipped productions for the better part of two decades. The hard part isn't calling a model; it's the control loop, the evals, the routing, and the fine-tuning around it. That work is the company. This role builds it.

• Build the multi-step agent loop that turns creative input into production-ready output, with correction passes between steps
• Build the evals: define what "good" means for subjective creative output, and make quality a number we can move
• Design the model gateway: route between in-house and frontier models per task, with cost and provenance tracked
• Run the post-training loop (SFT, RL with rubric rewards) on the in-house components

• You've built agent loops or eval harnesses that ran in production: something that touched real users or real spend
• You're fluent in LLM/VLM plumbing: tool use, structured output, retries, evals, cost/latency tradeoffs
• You can read a paper, ignore 80% of it, and ship the 20% that matters this week
• You default to small models plus good harness over big models plus hope

• Fine-tuned open-source models (SFT, DPO, RL with rubric rewards)
• Worked on creative-tool evals where "good" is subjective
• Film, TV, or visual-production exposure

• Stack — Modern, decisions still open. Real input on what we build with.
• Location — Singapore preferred. SEA-remote considered for the right person.
• Comp — Full-time. Competitive cash for an early-stage company plus meaningful equity. Specifics in the first conversation.
• Interview — One paid working session on a real YAM problem. No whiteboards, no take-homes you'll never hear back on.

Email karen@gengis.ai with "Harness Engineer" in the subject and one paragraph on the most interesting harness or eval you've built, and what you learned was wrong about it.

Gengis AI is building applied AI for the operational layer of film, television, and media production, the hard work that happens after generation: turning creative material into structured workflows that production teams can review, refine, approve, and use. We are not building a foundation model. We are building a model-agnostic platform that makes AI useful inside real production environments. You will own the core systems, AI orchestration, evaluation workflows, and product infrastructure that make this possible. You will work directly with the founders and early production users to ship the first durable version of the platform. In the first phase, your focus will be a reliable workflow that helps production teams move from creative input to practical planning and visual development outputs. This is not a narrow backend role and not a pure ML research role; you should be able to reason across systems, product, applied AI, infrastructure, data, and user-facing execution.

• Core platform architecture and backend systems
• AI orchestration across text, image, and future video workflows
• Reliable async workflows for long-running AI and production tasks
• Evaluation systems that make output quality measurable, repeatable, and improvable
• Human-in-the-loop review flows where users can refine, approve, reject, and iterate
• Cost, reliability, and performance controls for AI-powered workflows
• Data handling, permissions, auditability, and security fundamentals
• Cloud deployment, CI/CD, observability, and operational discipline
• Engineering standards and early technical hiring as the team grows

• 7+ years building and shipping production software, with real ownership of systems taken from zero to live use
• Strong backend and systems architecture ability: APIs, databases, stateful services, async jobs, queues, and cloud infrastructure
• Experience building data-heavy, workflow-driven, or human-in-the-loop products
• Experience building with LLMs, generative AI APIs, diffusion/video models, or AI orchestration systems in production or near-production environments
• Practical judgment around model failure modes, evaluation, cost, latency, reliability, and user trust
• Comfortable designing evaluation workflows and quality metrics, not just shipping demo prompts
• Full-stack enough to ship end-to-end early, when the team is small
• Cloud and infra fluency: deployment, CI/CD, inference basics, observability, and security fundamentals
• Clear communication with product, creative, and non-technical users
• Ability to set technical direction, make tradeoffs, hire well, and lead without over-engineering

• Experience with AI evaluation, model routing, provider abstraction, or cost/performance benchmarking
• Experience with MLOps or data pipelines: versioning, reproducible evaluation, and dataset governance
• Experience with media, film, creative tooling, workflow platforms, or collaborative review tools
• Experience with Postgres, cloud GPU workflows, or event-driven infrastructure
• Early-stage startup experience where you built the first durable version of a product

• You want to own the technical layer that turns a strong vision into a working platform
• You build for professionals who need AI to be useful, controllable, reliable, and commercially practical, not just impressive in a demo
• You can ship the core system largely hands-on while setting architecture for the team that follows
• You make pragmatic tradeoffs between speed, quality, cost, and runway
• You want senior ownership from day one, with a path toward Head of Engineering as the company scales

Competitive senior-market salary plus meaningful founding-stage equity. Singapore-based onsite/hybrid preferred; relocation support may be available for an exceptional candidate.

Send your application to karen@gengis.ai and include:

• CV and links to shipped work or code
• A short note on one complex system you built or owned: what was hard, what you measured, what tradeoffs you made, and what you would do differently now

This is a high-ownership role for someone who wants to shape the product, architecture, engineering culture, and technical direction from the ground up. You will work directly with the founding team and production users to turn messy real-world workflows into reliable software. This is not a narrow frontend or backend role; you should be comfortable moving across product, frontend, backend, database, workflow integration, deployment, and technical decision-making.

• Build and ship core product features across the full stack
• Own frontend, backend, database, auth, and deployment decisions
• Integrate AI and LLM workflows into practical user-facing product flows
• Design secure handling of scripts, documents, and production IP
• Build internal tools, dashboards, and workflow automations
• Work directly with founders and users to turn ambiguity into shipped software
• Set engineering standards for future technical hires
• Make pragmatic architecture decisions that balance speed, quality, and runway

• 5+ years of software engineering experience
• Strong experience with React, Next.js, TypeScript, or equivalent frontend frameworks
• Strong backend experience with Node.js, Python, or similar
• Experience with Postgres, Supabase, Firebase, or similar platforms
• Experience shipping production web applications
• Good judgment around auth, data security, reliability, and maintainability
• Comfortable working in an early-stage environment with incomplete information
• Product-minded and able to think beyond tickets
• Clear communicator who can work closely with non-technical founders and creative teams

• Experience with LLM APIs, RAG, agents, document parsing, or AI workflow systems
• Experience with media, video, film, production, creative tooling, or workflow software
• Experience with background jobs, queues, structured AI outputs, or multimodal systems
• Experience leading small engineering teams or mentoring other engineers
• Startup or zero-to-one product experience

• You like building from zero to one
• You can move fast without creating chaos
• You are comfortable making decisions with imperfect information
• You care about users, not just architecture
• You want meaningful ownership over product and technical direction
• You are excited by the intersection of AI, filmmaking, and production workflows

Competitive salary based on experience. Equity or long-term incentive structure can be discussed for the right candidate.

Send your application to karen@gengis.ai and include:

• LinkedIn, GitHub, or portfolio
• One product or system you have shipped that you are proud of
• A short note on why this role interests you

This role is focused on turning scripts, creative briefs, visual references, moodboards, characters, locations, and production intent into useful visual outputs for directors, producers, and creative teams. It is not a pure research role, and it is not a generic chatbot role. We need someone who can experiment with generative image/video models, structure training and evaluation data, improve visual consistency, and help turn model workflows into practical product features. You will work closely with the founding team, product engineering, and production users to build AI systems that support real creative decision-making.

• Build and evaluate AI workflows for script-to-scene and script-to-shot previsualisation
• Work with generative image and video models, multimodal models, and LLMs
• Design datasets from scripts, briefs, visual references, storyboards, shot lists, moodboards, frames, and production metadata
• Experiment with fine-tuning, LoRA/adapters, prompting, retrieval, and reference-conditioning workflows
• Improve consistency across characters, locations, style, mood, framing, and scene continuity
• Explore workflows for generating visual options that are useful to directors, producers, and creative teams
• Evaluate generated outputs for creative usefulness, consistency, quality, latency, and cost
• Collaborate with full-stack engineers to integrate AI workflows into YAM
• Help decide when to use existing models, fine-tuning, custom datasets, third-party APIs, or open-source model pipelines
• Build repeatable evaluation methods so we can improve model outputs over time

• 3+ years of experience in machine learning, applied AI, computer vision, generative AI, or AI engineering
• Strong Python skills
• Experience with image generation, video generation, multimodal AI, or visual-language models
• Familiarity with model fine-tuning, LoRA, adapters, embeddings, or conditioning workflows
• Experience working with messy real-world data and turning it into usable training or evaluation datasets
• Ability to prototype quickly while thinking about production constraints
• Practical understanding of model limitations, hallucinations, consistency issues, latency, and cost
• Strong problem-solving ability and clear communication
• Interest in film, content production, creative tools, or visual storytelling

• Experience with Stable Diffusion, Flux, ComfyUI, Runway, Kling, V, Sora-style workflows, or similar generative tools
• Experience with ControlNet, IP-Adapter, reference conditioning, character consistency, style transfer, or visual continuity workflows
• Experience with image/video understanding, shot analysis, scene parsing, or visual metadata extraction
• Experience with LLMs, structured outputs, RAG, or agentic workflows
• Experience building AI products beyond notebooks and demos
• Experience in film, advertising, animation, storyboarding, previsualisation, creative tooling, or content production
• Experience deploying AI systems into production environments

• You are excited by the idea of AI helping creative teams visualise ideas earlier
• You care about whether generated outputs are actually useful, not just impressive
• You enjoy experimenting with models but can stay grounded in product needs
• You can work with ambiguity and turn rough creative workflows into structured systems
• You are pragmatic about using the best available tools instead of reinventing everything
• You want to work at the intersection of generative AI, storytelling, and production workflows

Competitive salary based on experience. Equity or long-term incentive structure can be discussed for the right candidate.

Send your application to karen@gengis.ai and include:

• LinkedIn, GitHub, portfolio, or relevant project links
• One AI/generative model project you have built or contributed to
• Any examples of image/video generation, fine-tuning, LoRA, visual consistency, multimodal AI, or model evaluation work
• A short note on why this role interests you