Senior Machine Learning Engineer

Role Overview

• Own and ship ML in production: take ideas from R&D to robust, maintainable deployments, often onto edge or embedded hardware.
• End-to-end ownership: data collection/curation, feature engineering, model training, evaluation, deployment, monitoring, and iteration.
• Technical leadership: set direction, guide design, perform reviews, mentor teammates, and raise the engineering bar.
• MLOps/LLMOps: CI/CD for models, containerisation/orchestration, experiment tracking and registry, model evaluation pipelines, safety guardrails, canaries, and performance monitoring.
• Cross-team collaboration: partner with software, systems, and product colleagues; simplify complex topics for other disciplines and customers.
• Data foundations: establish pragmatic data pipelines (batch/stream) that make curation, provenance, and reproducibility first-class.

Requirements

• Proven delivery: multiple years leading technical work that delivered measurable impact in production, especially on edge, embedded, or mission-critical systems.
• ML & maths depth: strong grounding in ML/DL (optimisation, generalisation, probability, model architecture) and the ability to reason about these trade-offs in production.
• LLMs & agentic systems: practical experience with prompt optimisation, retrieval/RAG, evaluation, and tool orchestration; aware of latency, cost, and reliability trade-offs.
• MLOps excellence: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation.
• Data engineering: proficiency with Databricks, Apache Spark, Delta Lake, MLflow, and SQL; experience integrating datasets and maintaining data quality.
• Software development: Strong python skills, experience with low-level languages like Rust is desirable.
• Interpersonal skills: strong communicator who can mentor, influence, and bridge technical and non-technical audiences.
• Education: Strong foundation in computer science or related disciplines, gained through formal education or hands-on experience.
• Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity.
• Beneficial knowledge
• General tooling and platforms: Databricks, AWS, GitHub, Docker/Kubernetes, MLflow, Jira.
• Edge deployments: Nvidia Jetson (e.g. AGX Orin), Raspberry Pi, or other embedded accelerators.
• LLM/Agent tooling: DSPy, llama.cpp, vLLM, evaluation harnesses, prompt optimisation, agent frameworks.
• Operational practices: incident response, canary deployments, cost/performance optimisation across edge and cloud.

Tech Stack

• Apache
• AWS
• C++
• Cloud
• Docker
• Kubernetes
• Python
• Rust
• Spark
• SQL

Benefits

We are committed to building a flexible, inclusive, and enabling company. Our aim is to create a diverse team of talented people with unique skills, experience, and backgrounds, so please apply and come as you are!

We also recognise the importance of flexible working and support this wherever we can. We typically operate a flexible, hybrid-working model, with an average 3 days in the office each week (dependent on the role). We welcome the opportunity to discuss flexibility, part-time working requirements and/or workplace adjustments with all our applicants.

Rowden is a Disability Confident Committed company, and we actively encourage people with disabilities and health conditions to apply for our roles. Please let us know your requirements early on so that we can make sure you have everything you need up front to help make the recruitment process and experience as easy as possible.

Finally, if you feel that you don’t meet all the criteria included above but have transferable skills and relevant experience, we’d still love to hear from you!