Empresaria Group plc
Senior Machine Learning Engineer
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Senior Machine Learning Engineer
Remote | UK & USA
Shape the Future of AI-Powered Productivity
We are partnering with an innovative, venture-backed technology company building the next generation of AI-native productivity solutions.
The organisation is focused on creating intelligent systems capable of understanding context, managing complex workflows, and helping users complete real-world tasks with minimal effort. Combining cutting-edge machine learning research with practical product development, the team is building AI experiences designed to deliver meaningful impact at scale.
As part of this growth, we are seeking a Senior/Lead Machine Learning to drive the development, deployment, and optimisation of production-grade machine learning systems.
The Opportunity
This role sits at the intersection of machine learning, infrastructure, and product engineering. As Technical Lead, Machine Learning, you will be responsible for translating research and experimentation into scalable, reliable production systems. You will own the technical execution of machine learning initiatives, ensuring models are trainable, deployable, observable, and performant in real-world environments.
Working closely with research, engineering, and product teams, you will play a critical role in shaping the company's AI capabilities while providing technical leadership across machine learning delivery.
Key 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.
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Required Experience
- 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.
Preferred Experience
- 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.
Technology Stack
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- Python
- PyTorch
- JAX
- GPU-Based Training & Inference
- Large Language Models (LLMs)
- Machine Learning Infrastructure
- Data Pipelines
- Distributed Systems
- Model Evaluation Frameworks
What Success Looks Like
In this role, you will:
- Deliver reliable production machine learning systems that directly impact users.
- Ensure models can be trained, evaluated, deployed, and monitored efficiently.
- Improve system performance across latency, scalability, and cost.
- Support rapid experimentation while maintaining production stability.
- Enable engineering teams to integrate AI capabilities seamlessly into customer-facing products.
- Drive measurable improvements in model performance and user experience.
What We're Looking For
The ideal candidate combines deep machine learning expertise with strong engineering fundamentals and a pragmatic delivery mindset. You will be comfortable operating in a fast-paced environment, making informed technical decisions, and taking ownership of outcomes from concept through production deployment. You should be equally passionate about building robust systems as you are about advancing machine learning capabilities.
Why Apply?
- Opportunity to work on cutting-edge AI and machine learning challenges.
- High-impact role with significant ownership and influence.
- Collaborate with experienced researchers, engineers, and product leaders.
- Fully remote working environment across the UK and USA.
- Competitive compensation package with long-term growth potential.
- Opportunity to help build AI products designed for global scale.
Interview Process
The interview process is designed to be efficient and transparent, typically consisting of three to four stages focused on technical capability, problem-solving, and collaboration.
If you're passionate about developing production-grade machine learning systems and want to contribute to the future of AI-powered products, we'd love to hear from you.
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