Senior Machine Learning Engineer

Role Description

Join Runware as a Senior Machine Learning Engineer and be at the forefront of developing innovative AI solutions across various media modalities including text, image, video, 3D, and audio. We're building a powerful AI media creation platform designed to revolutionize how content is generated.

As a Senior Machine Learning Engineer, you’ll take the lead on critical projects, guiding the end-to-end lifecycle from research and experimentation to production deployment and performance monitoring. Your work will help shape the capabilities of our platform and enhance the experiences of users who rely on our cutting-edge AI technologies.

What You'll Be Doing

• Integrate open-source and third-party models into our inference platform
• Lead fine-tuning initiatives (LoRA, adapters, PEFT, domain adaptation)
• Optimise inference workloads for latency, batching, memory efficiency, and throughput
• Benchmark model quality vs cost vs performance across modalities
• Improve inference startup times and stability under high load
• Build evaluation frameworks and internal tooling for model validation
• Work closely with Infrastructure and Backend teams on scalable serving systems
• Monitor production performance and drive continuous optimisation
• Mentor engineers and help raise the ML engineering bar across the team

Qualifications

• Proven experience delivering ML systems to production environments
• Strong, low-level Python skills and deep hands-on experience with PyTorch
• Experience working with diffusion models, LLMs, or multimodal architectures
• Practical experience fine-tuning large models (LoRA, PEFT, adapters, etc.)
• Experience optimizing inference workloads in GPU environments
• Strong understanding of model evaluation, experimentation, and monitoring
• Ability to debug performance, memory, and reliability issues in production
• Strong systems thinking understanding how ML decisions impact infrastructure
• High ownership and comfort operating in a fast-paced startup environment

Requirements

• Experience with vLLM or custom inference servers
• Experience with Kubernetes or containerised ML workloads
• Experience working in high-throughput distributed systems
• Background in AI media generation (image, video, audio)
• Experience building internal ML tooling or developer-facing APIs
• Experience with kernels in CUDA/C++

Benefits

• Generous paid time off – vacation, sick days, public holidays
• Meaningful stock options – share in the upside you create
• Remote-first setup – work from home anywhere we can employ you
• Flexible hours – own your schedule outside core collaboration blocks
• Family leave – paid maternity, paternity, and caregiver time
• Company retreats – twice-yearly gatherings in inspiring locations