Senior Machine Learning Engineer

Empresaria Group plc is partnering with an innovative technology company focused on AI-native productivity solutions. They are seeking a Senior Machine Learning Engineer to drive the development and optimization of production-grade machine learning systems, providing technical leadership and collaborating with various teams to enhance AI capabilities.

Responsibilities:

• Lead the design, development, deployment, and optimisation of production machine learning systems
• Own end-to-end ML infrastructure, including data pipelines, training workflows, evaluation frameworks, inference systems, and deployment processes
• Fine-tune and optimise models using modern techniques such as:
• LoRA
• QLoRA
• Supervised Fine-Tuning (SFT)
• Direct Preference Optimisation (DPO)
• Model Distillation
• Design and operate scalable inference infrastructure focused on performance, reliability, and cost efficiency
• Develop and maintain data pipelines supporting both synthetic and real-world training datasets
• Build evaluation frameworks covering model quality, robustness, safety, bias, and operational performance
• Optimise GPU utilisation, memory consumption, inference latency, and system scalability
• Collaborate closely with software engineering teams to integrate machine learning capabilities across web, mobile, and desktop applications
• Monitor production systems, identify issues, and drive continuous improvement
• Provide technical leadership and mentorship within a high-performing engineering environment

Requirements:

• Strong commercial experience building and deploying production machine learning systems
• Expertise in developing scalable ML infrastructure and machine learning platforms
• Hands-on experience with: Python, PyTorch and/or JAX, Large Language Models (LLMs), Model Fine-Tuning, Distributed Training, GPU-based Training and Inference
• Experience designing and maintaining machine learning pipelines from data ingestion through deployment
• Strong software engineering principles and experience writing production-quality code
• Understanding of model evaluation, observability, monitoring, and performance optimisation
• Experience balancing technical trade-offs between latency, cost, scalability, reliability, and model quality
• Excellent communication and stakeholder management skills
• Experience working with large-scale AI products in production environments
• Experience building AI-powered consumer or productivity applications
• Exposure to agentic AI systems and multi-step reasoning architectures
• Experience with reinforcement learning, preference tuning, or post-training optimisation techniques
• Familiarity with MLOps tooling, experiment tracking, and deployment automation
• Previous experience leading or mentoring machine learning engineers