Employer profile
Stability AI
5 open roles indexed with location, benefit, and apply-link signals where available.
Open roles
Showing the most recent indexed roles for this employer.
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Senior Product Engineer, Growth & Lifecycle Infrastructure - Music & Audio
Los Angeles, CA or Remote (United States)
remote Salary not disclosedSenior Product Engineer, Growth & Lifecycle Infrastructure - Music & Audio Los Angeles, CA or Remote (United States) Stability AI is a community and mission-driven, open artificial intelligence company that cares deeply about real-world implications and applications. Our most considerable advances grow from our diversity in working across multiple teams and disciplines. We are unafraid to go against established norms and explore creativity. We are motivated to generate breakthrough ideas and convert them into tangible solutions. Our vibrant communities consist of experts, leaders, and partners across the globe who are developing cutting-edge open AI models for Image, Audio, Video, and 3D. As the Senior Product Engineer, Growth & Lifecycle Infrastructure , you will own the ground-up build of PLG growth and lifecycle infrastructure for our music and audio products - greenfield across web, desktop, plugin, and mobile. You'll choose the stack, design the event model, wire the integrations, and run the system. The output is a measurable, attributable funnel from anonymous visitor to paying customer to expanded account. This role sits at the intersection of growth marketing, product lifecycle, and community-driven PLG - the systems, how they live inside the product, and how they drive real conversion. What you'll do… - Select and own the stack for the Music & Audio vertical: CDP, product analytics, lifecycle/messaging, experimentation, warehouse, reverse-ETL, and entitlements - including writing defensible recommendations with cost model and tradeoffs - Design event taxonomy with consistent schema and identity resolution across anonymous → free → paid → multi-device -
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Multimodal Generative AI Researcher
Remote
remote Salary not disclosedMultimodal Generative AI Researcher Remote Multimodal Generative AI Researcher Location: Remote About the Role We're looking for a Research Scientist with deep expertise in training and fine-tuning large Vision-Language and Language Models (VLMs / LLMs) for downstream multimodal tasks. You'll help push the next frontier of models that reason across vision, language, and 3D , bridging research breakthroughs with scalable engineering. What You'll Do - Design and fine-tune large-scale VLMs / LLMs - and hybrid architectures - for tasks such as visual reasoning, retrieval, 3D understanding, and embodied interaction. - Build robust, efficient training and evaluation pipelines (data curation, distributed training, mixed precision, scalable fine-tuning). - Conduct in-depth analysis of model performance: ablations, bias / robustness checks, and generalisation studies. - Collaborate across research, engineering, and 3D / graphics teams to bring models from prototype to production. - Publish impactful research and help establish best practices for multimodal model adaptation. What You Bring - PhD (or equivalent experience) in Machine Learning, Computer Vision, NLP, Robotics, or Computer Graphics. - Proven track record in fine-tuning or training large-scale VLMs / LLMs for real-world downstream tasks. - Strong engineering mindset - you can design, debug, and scale training systems end-to-end. - Deep understanding of multimodal alignment and representation learning (vision-language fusion, CLIP-style pre-training, retrieval-augmented generation). - Familiarity with recent trends, including video-language and long-context VLMs , spatio-temporal grounding , agentic multimodal reasoning , and Mixture-of-Experts (MoE) fine-tuning. - Awareness of 3D-aware multimodal models - using NeRFs, Gaussian splatting, or differentiable renderers for
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Global Director of Partnerships
United States
unspecified $350K-$350KGlobal Director of Partnerships United States We are seeking a highly experienced and strategic Global Director of Partnerships to build, lead, and manage our worldwide ecosystem of technology alliances, strategic, and channel partners to deliver on Stability's vision. This role requires a unique blend of deep commercial acumen, a strong understanding of AI/ML technologies, and proven success in growing revenue through in-direct channels. What you'll do… - Define global partner strategy : Develop and execute a partnership strategy aligned with Stability's product roadmap, sales targets, and market expansion goals. Own the annual channel revenue and contribution target. - Identify, develop, and manage partnerships : Identify, develop, and manage strategic partners across various segments (e.g., Cloud, Agency, SI, Technology, Resellers) . - Negotiation and contracting : Lead complex partnership negotiations, working closely with legal and finance teams to establish mutually beneficial agreements and partnership frameworks. - Joint GTM : Design, implement, and manage joint GTM plans with strategic partners to drive pipeline generation and increase closed-won revenue. - Enablement : Oversee the development of partner enablement programs, ensuring partners are technically proficient and commercially motivated to sell and deploy our solutions. - Performance Tracking : Establish KPIs and metrics to monitor the health, productivity, and ROI of all global partnerships. - Collaboration : Work closely with Sales, Marketing, Product, and Engineering teams to ensure partnership activities are integrated into the company's overall operational structure and product development priorities. - Partner Advocacy: Serve as the internal voice of our partners, ensuring their
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Generative AI Inference Engineer
United States
unspecified Salary not disclosedGenerative AI Inference Engineer United States Generative AI Inference Engineer About the role: We are seeking passionate Machine Learning Engineers to join our Inference team, focusing on the creative applications of generative AI models. The ideal candidate will have substantial experience developing and running inference for multi-modal models. A deep understanding of diffusion model architectures and familiarity with workflow tools like ComfyUI are a big plus. You will be expected to leverage and push the boundaries of state-of-the-art inference optimization techniques for multi-modal generative models. This role offers the opportunity to work alongside top researchers and engineers, utilizing cutting-edge high-performance computing resources to make a significant impact in the rapidly evolving field of generative AI. Responsibilities: - Lead efforts to drive the design, development of customer-facing multi modal ML inference systems. - Work with the Platform and Inference teams on building inference systems for the next generation of models, where you will work on areas such as optimization, model tuning and deployment. - Partner with leading cloud providers to deliver hosted Stability AI inference solutions. - Be a strategic thought partner for leaders across the organization on driving business impact through machine learning - Be part of the team to bring new Stability models and pipelines into existence - Prototype and productionize inference platform improvements and new features Qualifications: - 7+ years working on productionizing machine learning systems, including inference pipeline development - Expert level knowledge on writing and running python services at scale - 5+ years working on python
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Research Scientist – Controlled 3D Generation
Remote
remote Salary not disclosedResearch Scientist – Controlled 3D Generation Remote Research Scientist - Controlled 3D Generation Location: Remote About the Role We're seeking a Research Scientist passionate about 3D generation, flow matching, and diffusion models . You'll help advance the frontier of controllable 3D content creation-building models that generate consistent, editable, and physically grounded 3D assets and scenes. What You'll Do - Conduct cutting-edge research on flow-matching, diffusion, and score-based methods for 3D generation and reconstruction. - Design and implement scalable training pipelines for controllable 3D generation (meshes, Gaussians, NeRFs, voxels, implicit fields). - Develop techniques for conditioning and control (text, sketch, pose, camera, physics) and multi-view consistency. - Analyse model behaviour through ablations, visualisations, and quantitative metrics. - Collaborate with cross-disciplinary research, graphics, and infrastructure teams to translate research into production-ready systems. - Publish results at top-tier venues and work with interns. What You Bring - PhD (or equivalent experience) in Machine Learning, Computer Vision, or Computer Graphics. - Published work on diffusion, flow-matching, or score-based generative models (2D or 3D). - Strong engineering and problem-solving abilities: experience with PyTorch, JAX, or CUDA-level optimisation . - Understanding of 3D representations (meshes, Gaussians, signed-distance fields, volumetric grids, implicit networks). - Solid grasp of geometry processing, multi-view consistency, and differentiable rendering . - Ability to scale experiments efficiently and communicate complex results clearly. Bonus / Preferred - Experience generating coherent 3D scenes with multiple interacting objects, lighting, and spatial layout. - Familiarity with scene-level control (object placement, camera path, simulation, or text-to-scene composition). -