Lead end-to-end ML initiatives: integrate and fine-tune models, optimize GPU inference for latency and throughput, build evaluation and monitoring tooling, collaborate on scalable serving systems, and mentor engineers.
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
RequirementsWhat We’re Looking For
- Deep, hands-on expertise in Python and PyTorch, comfortable working at a level well beyond standard framework usage, including the quirks and edge cases that come with pushing Python for ML workloads
- Demonstrated experience building ML applications and models yourself, not just running or deploying existing ones (e.g. serving a pre-built model via vLLM doesn't qualify on its own)
- Hands-on experience writing GPU kernels in CUDA/C++, or in Triton
- Low-level experience with model internals e.g. working directly with diffusion model architectures (diffusers or equivalent), not just calling high-level APIs
- Experience with the PyTorch compiler (torch.compile) or comparable low-level PyTorch tooling
- Practical experience fine-tuning large models as a repeatable service (LoRA, PEFT, adapters), built for speed and reuse across many models, not a single one-off training run
- Real, verifiable project history e.g. GitHub repos, demonstrating what you have personally wrote
- High ownership and comfort operating in a fast-paced startup environment
- Experience optimizing inference workloads in GPU environments
- Experience with vLLM or custom inference servers
- Experience with diffusion models, LLMs, or multimodal architectures more broadly
- Experience with Kubernetes, Docker, or containerised ML workloads
- Experience building internal ML tooling or developer-facing APIs
- Experience working in high-throughput distributed systems
- Background in AI media generation (image, video, audio)
Benefits
We’re a remote-first team that comes together in person twice a year to plan, collaborate, and celebrate wins. Day to day we keep a few core hours for teamwork, but outside of that you set the schedule that helps you do your best work.
Our environment is fast-moving and ambitious. Big pushes are part of building category-defining products, but we balance that with flexible working, generous time off, and regular retreats so the team can stay sharp and motivated.
- 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
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