Applied AI Engineer
Primary stack
Nice to have's
Job description
About the Position
Applied AI Engineer
RkiveAI
Bridge research and production: build custom inference pipelines, optimise GPU workloads, and ship multimodal AI systems that run at scale.
Remote
Posted: 2026-02-24
Full-Time
Engineering
Responsibilities
- Model productionisation: Take research prototypes - temporal event models, multimodal fusion architectures - and build them into robust, low-latency production services.
- Custom inference pipelines: Design and optimise inference paths that integrate with our rendering engine, structured output schema, and model interface.
- GPU optimisation: Profile, optimise, and where necessary write custom CUDA code for training and inference workloads.
- Training infrastructure: Build and maintain distributed training pipelines for research models, including data ingestion from production signal.
- Benchmarking and evaluation: Instrument systems for rigorous performance measurement - latency, throughput, cost, output quality - across model variants running through the same execution environment.
- Cross-team integration: Work directly with the research scientist on architecture constraints and with the systems engineer on deployment and runtime requirements.
Requirements
- Systems-level ML engineer: You understand what happens between
model.forward()and the GPU executing a kernel. You have optimised at that level. - GPU-literate: You have worked with CUDA, or GPU-accelerated workloads at a level beyond just calling library functions. If you have written custom kernels, even better.
- Builder: You have shipped at least one non-trivial ML system to production - not a demo, not a notebook, a real system handling real traffic.
- Resourceful: When the problem is novel, you research, prototype, and solve. You do not wait for someone to tell you how.
- Quality-driven: You write code that others can maintain. Tests, documentation, clear interfaces.
- Research-adjacent: You can read a paper, understand the architecture, and implement it. You do not need the research scientist to hand you a script.
- Ambitious: You want to build systems that do not exist yet, not maintain systems that already do.
Nice to Have
- JAX
We Offer
- Fully Remote
- Competitive Compensation
- High Technical Bar
About the Company
Rkive is an AI lab focused on multimodal reasoning across time and complexity.
We develop novel architectures and products. We aim to create intelligent environments that work alongside you - proactively, reliably, and responsive to intent.
- Meaningful work: What we are building is genuinely unprecedented. The problems are hard and the opportunity is enormous.
- Autonomy: Fully remote. Manage your own time. Take time off when you need it.
- Zero politics: No bureaucracy, no posturing, no performative culture. Just the work.
- Mutual respect: We back our people, but we expect the same in return.
- Honest environment: Not a family, not a pressure cooker. A high-trust, high-performance team.
Application Process
Show us your work and your depth.
A strong CV gets you a look. Evidence of solving hard systems problems gets you an interview.
- CV: Focused on impact and technical depth - not tool lists.
- Portfolio: Repositories, deployed systems, performance benchmarks, GPU profiling results, custom pipelines - anything that shows you build real things.
- Recommendations: From engineers, researchers, or technical leads you have worked with.
- Cover letter: Tell us about the hardest production ML problem you have solved - and how.
Send your resume, portfolio, and GitHub links to [email protected]
Apply now
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