Machine Learning Engineer - Scalable ML Systems
Skills Alliance are supporting a venture-backed TechBio company that’s building a platform to make large-scale biological datasets computable using next-generation foundation models.
They’re now hiring a Machine Learning Engineer to help scale model training, inference and deployment across real-world workflows. This is a great fit for someone who enjoys working at the intersection of ML infrastructure, systems engineering and applied research.
What you’ll be working on
* Building and optimising large-scale training and inference pipelines for modern architectures (Transformers, SSMs, diffusion-style models etc.)
* Improving performance, throughput and latency across distributed environments
* Designing modular ML components that can be reused across teams
* Taking research-level code and turning it into reliable, production-ready systems
* Working closely with researchers and product engineers to ship fast in an environment that iterates constantly
What they’re looking for
* Strong Python engineering background
* Experience with frameworks like PyTorch, JAX or TensorFlow
* Demonstrated ability to scale ML workflows in real-world settings (cloud, GPU clusters, distributed training)
* Comfortable with MLOps tooling (W&B, Ray, Docker, etc.)
* Familiar with modern model architectures
* High ownership mindset ideal for people who enjoy low-ego, fast-moving engineering teams
* No prior biotech experience needed