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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in machine learning and deep learning, with a strong focus on training and fine-tuning large models for deployment in production environments. Capable of leading technical projects, mentoring engineers, and effectively communicating complex concepts to diverse audiences.
Highest-signal resume keywords
Machine Learning ExpertiseLarge Model TrainingDistributed Training ExperiencePython ProficiencyMSc or Equivalent Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep LearningModel TrainingData EngineeringPerformance OptimizationQuantisationPruningDistillationMLOpsModel Evaluation
Soft Skills
Strong CommunicationInfluencing Without Authority
Tools & Technologies
PyTorchKubernetesDockerAWSGCPApache SparkMLflowDatabricksNvidia JetsonNvidia Triton Inference Server
Certifications & Qualifications
Eligibility for Developed Vetting (DV) Clearance
Industry Keywords
Large-Scale Model EvaluationEdge Hardware DeploymentAgent-Based SystemsRetrieval/RAGTool OrchestrationPrompt OptimisationTechnical Readiness Level (TRL)Multi-GPU TrainingCI/CDObservability
Tech Stack
Tools & technologiesApacheAWSDockerGoogle Cloud PlatformKubernetesNode.jsPythonPyTorchRustSparkSQLC++
About the role
Key responsibilities & impact- Take technical ownership of the company’s most demanding machine-learning projects and make key technical decisions
- Design, build, train and debug machine-learning systems hands-on
- Lead large-model work, including pre-training, fine-tuning, distributed training and evaluation at scale
- Contribute to projects involving LLMs and agentic systems, considering latency, cost and reliability in constrained settings
- Ensure solutions work end-to-end from data pipelines and training infrastructure through deployment on edge hardware
- Evaluate model behaviour and system performance
- Lead Rowden’s technical presence at exercises, trials and customer engagements
- Contribute technical expertise to bid writing
- Represent Rowden’s ML capability with customers and partners, including international exercises a few times a year
- Set technical standards with craft and technical leads
- Mentor engineers through reviews, pairing and example
- Work closely with software, hardware, systems, RF, ML craft and technical teams
- Report to the Head of ML
Requirements
What you’ll need- Proven delivery of technical work into production or to a high technology readiness level (TRL) ready for deployment
- Mastery of modern machine learning and deep learning, with strong mathematical grounding and ability to reason about production trade-offs
- Deep hands-on experience training and fine-tuning large models
- Experience with distributed training across multi-GPU or multi-node environments
- Experience writing highly performant model training code
- Practical experience with LLM and agent-based systems, including retrieval/RAG, tool orchestration, prompt optimisation and evaluation
- Practical data engineering experience and experience deploying to edge or embedded hardware
- Excellent Python skills
- Experience with low-level languages such as Rust or C++ is desirable
- Strong communication and ability to influence without authority and bridge technical and non-technical audiences
- MSc or equivalent experience required
- Eligibility for Developed Vetting (DV) clearance
- Willingness and ability to travel to customer sites and internationally a few times a year
- Desirable: quantisation, pruning, distillation, compilation and runtime optimisation for constrained hardware
- Desirable: experience at trials, exercises or demonstrations, or working directly with users in operational or other high-stakes settings
- Desirable: MLOps, including reproducible pipelines, model versioning, CI/CD, observability and automated evaluation
- Beneficial knowledge of PyTorch DDP and FSDP, Kubeflow, Slurm, Kubernetes, DeepSpeed, Megatron, TorchTitan, evaluation harnesses, agent frameworks, TensorRT, ONNX Runtime, vLLM, Nvidia Triton Inference Server, KServe, Nvidia Jetson, Databricks, Apache Spark, MLflow, SQL, AWS, GCP and Docker
Benefits
Comp & perks- Flexible, hybrid-working model, typically averaging 3 days in the office each week (dependent on the role)
- Flexible working arrangements, including part-time working requirements and workplace adjustments
- Disability Confident Committed company supporting applicants with disabilities and health conditions
- Opportunity to discuss workplace adjustments to support the recruitment process