Small team. Big ambitions.

The role

This is a hands-on role. You’ll work directly with the CEO, write and publish the content, run our channels and get to know the filmmakers and production teams we’re building for.

What you’ll do

• Run our marketing. Set priorities across the website, social, launches and events. Keep the work consistent with our voice and visual identity.
• Manage company and founder channels. Plan the calendar, write the copy and publish. Find things worth saying, backed by work we can show.
• Build the community. Recruit and support beta users, keep conversations going and bring their feedback into the company. Help develop fellowships and hackathons with industry and education partners.
• Make content from real production work. Turn collaborations into case studies, films and founder pieces. Brief creative collaborators and take the work through to publication.
• Track what’s working. Know which content and activities bring relevant people into conversations, trials and ongoing participation.

Who we’re looking for

You’ve run marketing or community in a small team and done much of the work yourself. You write well, have good visual judgment and can take something from an idea to publication.

You’re outgoing, positive and a self-starter. You enjoy meeting people, spot what needs doing and get it moving. You’re comfortable reaching out to a head of production, welcoming a new community member and following up when a conversation goes quiet.

You’re interested in how films and television actually get made: the creative decisions, budgets, deadlines and people involved. Experience in film, TV, media or a related creative field would help.

You use AI tools in your work and check what they produce. You’ll work with our agent-assisted content pipeline; you don’t need to be a developer, but you should be comfortable learning new tools.

How we work

We care about doing good work and being good people to work with. That means sharing ideas openly, taking initiative and helping each other when something gets difficult. We value a positive attitude and honest conversations, including when things haven’t gone to plan.

That culture should extend into the community you build. From someone’s first conversation with us to their ongoing involvement, we want people to feel comfortable asking questions, sharing unfinished work and giving honest feedback. How you welcome people, follow through and stay involved matters as much as the events or content you create.

What you’ll get

Direct access to the founders and room to shape how Gengis reaches the industry. There’s an established brand to work with, a product developed inside real productions and plenty still to build.

The role is based in Singapore, with community and partnership work across Southeast Asia and internationally.

To apply

Email karen@gengis.ai with "Marketing & Community Manager" in the subject. Send examples of your work and a short paragraph on what you would focus on in your first month. Show us an audience or community you helped build, explain your part in it and tell us what worked. No cover letter needed.

The role

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.

What you'll do

• 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

Tools you'll use

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'll thrive here if

• 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

Bonus

• 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

Logistics

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

How to apply

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.

About Gengis

Gengis builds AI for film and TV production. Our platform, YAM, connects work from script to screen, carrying production context, creative decisions and approvals through the process.

Our founders also built Refinery Media and X3D Studio. YAM grew out of work on real productions, and you’ll work directly with the filmmakers and engineers building it.

The role

You’ll own how our product agents work and how we know they’re good.

That means designing their behaviour, choosing their models and building the evaluations behind every decision. Did a prompt change improve script scoring? Is a model worth three times the cost for this task? Does an agent need the whole script or just three scenes? You’ll answer with experiments the team can reproduce.

This is a hands-on engineering role. You’ll work in our repository, ship pull requests and turn findings into changes to prompts, skills, tools and model configurations. You’ll also write clear recommendations when the right next step needs a team decision.

Your first priorities will be script scoring and story breakdown quality, followed by the next generation of our Writer’s Room agents.

What you’ll own

• Rubrics and quality standards. Work with film practitioners to define what good output looks like. Turn their judgment into versioned rubrics, labelled examples and acceptance thresholds. Practitioners own creative judgment; you make it measurable and understand where the measures fall short.
• Agent design. Define each agent’s goal, prompts, skills, tools, accessible context and output contract. Set its triggers, limits on steps, tokens and cost, and behaviour when a run fails.
• Model selection. Compare models across providers, including OpenRouter and AWS Bedrock, on quality, latency and cost. Maintain evidence for our model catalog and establish when a more efficient model is sufficient or additional reasoning effort is justified.
• Evaluation infrastructure. Build golden fixtures, held-out datasets, experiment runners and regression suites. Prevent leakage between tuning and evaluation, and measure changes before they ship.
• Production quality. Investigate failed or partial runs, quality regressions and cost drift. Turn findings into fixes and regression tests, working with engineering on tool and contract changes.

What you’ll work on first

Start by calibrating script scoring against practitioner judgments across short- and long-form web series. Establish a breakdown benchmark with labelled fixtures, a practitioner-approved rubric and comparisons of prompt, skill and model approaches.

Then apply that foundation to Writer’s Room agents: scene drafting from outlines, side-by-side writing proposals from multiple models, audience reactions, character interviews that respect what each character knows, and shot lists generated from reviewed scripts.

For each agent, establish a quality baseline and measure the cost and latency of an accepted output.

What you’ll bring

• Substantial experience shipping LLM agents in production, including tool use, agent loops, structured outputs, context and retrieval design, retries and partial results.
• A strong record in LLM evaluation: rubric design, golden sets, LLM-as-judge calibrated against humans, inter-rater agreement and recognising misleading metrics.
• Practical judgment on model selection, supported by benchmarks on your own tasks and an understanding of token costs, reasoning effort, latency and provider differences.
• Strong software engineering in TypeScript or Python. You can navigate an unfamiliar codebase, write useful tests and land clean pull requests.
• Clear written communication. You can explain to a non-engineer why a result is trustworthy, where it is uncertain and what should happen next.
• Experience working with domain experts and translating their judgment into measurable criteria.

Useful additional experience

• Agent skills, tool contracts, MCP, progressive tool discovery or bounded graph retrieval.
• Knowledge graphs or information extraction, particularly entity resolution, temporal reasoning and evidence-backed claims.
• Human labelling workflows and annotation quality.
• Screenwriting, film, TV, short-form drama or evaluation of creative writing.

Our stack includes a TypeScript monorepo, TanStack Start, oRPC, PostgreSQL with Drizzle, Inngest, Electric sync and Vercel’s AI SDK. Familiarity helps; experience with every component isn’t required.

How you’ll work

You’ll run short experiments with at least weekly checkpoints. Each experiment should preserve the versions, settings, inputs, outputs, cost and latency needed to reproduce it. Expected answers and held-out material stay out of tuning.

A well-documented negative result counts as delivery. You’ll share failures alongside successes, flag risks early and have autonomy over methods, tools and what to measure, including the freedom to challenge the framing of a problem.

The initial focus is evaluating and configuring agent systems. Foundation-model training is outside this role’s scope.

What success looks like after six months

• Every shipped agent has a reproducible answer to “Did this change make it better?”
• Agent designs are documented, with evidence behind model, prompt, tool and context choices.
• Rubrics are practitioner-approved and calibrated, with known agreement levels.
• Cost per accepted output is measured and improving without quality regressions.

Apply

Email karen@gengis.ai with "Senior Applied AI Engineer" in the subject. Send your CV or profile and a short example of an agent system you shipped or an evaluation that changed a product decision. Tell us what you owned, how you measured the result and what you learned. A written summary is welcome if the work is confidential.

The role

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.

What you'll do

• 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

What we're looking for

• 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

Nice to have

• 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'll thrive here if

• 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

Compensation

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

How to apply

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