Senior Machine Learning Engineer

Key areas of responsibility

• 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.

Key skills, experience and behaviours

• 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. 

• 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. 

Working at Rowden