Machine Learning Engineer

Experience:

3+ years of experience in ML engineering or related roles.

Solenya is building the AI infrastructure layer for modern e-commerce. We help merchants better match products to customers so shoppers can discover the right products faster. This drives higher revenue, stronger engagement and better customer lifetime value.

We’re an early-stage team working closely with leading retailers and marketplaces to shape the future of AI-driven commerce.

We're looking for another ML engineer to join our small team of generalists. Between them, they look after everything that makes the product work: model serving, data pipelines, the API merchants integrate with, and the infrastructure underneath it all. Together, that handles millions of requests a day.

The focus changes from week to week. One week you might be deploying a new model from our researchers or speeding up inference, the next adding an API feature for a customer or fixing a pipeline. You'll work closely with the leadership team and own the parts of the system you build.

• Help take new models from research to production, and keep GPU inference fast and cost-effective
• Build and maintain the pipelines that ingest and embed merchant catalogues
• Build features in our GraphQL API, which merchants and developers integrate with
• Look after our Kubernetes infrastructure on GCP, including deployments and observability
• Keep training and evaluation pipelines reproducible
• Self-host and run LLMs for judging, annotation and evaluation

• Strong Python and software engineering fundamentals: testing, CI/CD and code others can maintain
• Experience deploying and running ML models in production
• Solid PyTorch and deep learning knowledge, enough to work alongside researchers
• Comfortable with backend services, databases and cloud infrastructure
• A generalist who’s happy switching between ML, backend and infra work
• High EQ, low ego, and happy in an early-stage environment

Nice to have: Ray, Kubernetes, GPU performance work, vector databases, GraphQL, Kafka or LLM serving.

• Backend: FastAPI, GraphQL (Strawberry), Prefect, Kafka
• Infra: GKE with GPUs, Helm, Skaffold, Terraform, Nix, uv

In your first 3–6 months, you'll:

• Ship changes across the stack, from model serving and pipelines to the API and infrastructure
• Measurably improve inference latency, cost or reliability
  
  Own at least one part of the system that the rest of the team relies on

• Work at real scale from day one
• Work closely with the leadership team
• Broad exposure across ML, backend and infrastructure