Futureheads Recruitment | B Corp™
Staff Machine Learning Enginer
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Technical Lead, Machine Learning Engineer
Highlights
- UK-based startup backed by a £100m investment
- Building an AI-first product and engineering organisation from the ground up
- Founding-level opportunity with significant influence on technical direction and strategy
- Highly competitive compensation, with current conversations ranging from £120k-£400k+, plus equity. We are looking for the very best candidates to work with some exceptional talent
- 100% remote, with an option to go to a London office if wanted
- Multiple live openings across AI, Engineering, Research, Platform, and Leadership
About the Company
The company is building an AI-native smart assistant designed to help everyday users manage conversations, tasks, organisation, and workflows with minimal prompting. The product is focused on delivering reliable AI systems capable of long-running workflows, persistent context, multi-step reasoning, and real-world task completion. The goal is to help users complete everyday tasks significantly faster through intelligent automation.
The Role
As a Staff Machine Learning Engineer (Technical Lead, Machine Learning), you will own the execution layer of the company's AI platform. Working at the intersection of research, infrastructure, and product, you'll be responsible for turning research direction into reliable, scalable, production-grade machine learning systems. You'll ensure models are trainable, deployable, observable, and performant in real-world environments.
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Key Responsibilities
- Own end-to-end ML system execution across data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
- Fine-tune and adapt models using advanced techniques such as LoRA, QLoRA, SFT, DPO, and distillation.
- Architect and operate scalable inference systems while balancing latency, cost, and reliability.
- Design and maintain data systems supporting both synthetic and real-world training data.
- Build evaluation pipelines covering performance, safety, robustness, and bias.
- Own production deployment, including GPU optimisation, memory efficiency, latency reduction, and scaling strategies.
- Collaborate closely with application engineering teams to integrate ML systems into backend, mobile, and desktop products.
- Make pragmatic trade-offs and deliver improvements rapidly based on real-world usage.
- Work within production constraints including reliability, cost, latency, and safety.
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Technology Stack
- Python
- PyTorch
- JAX
- GPU-based training and inference systems
Technical Skills
- Experience building and deploying real machine learning systems used by customers.
- Strong understanding of large-scale machine learning models and their failure modes.
- Ability to write robust, production-grade code.
- Experience architecting scalable ML infrastructure and production systems.
Leadership & Personal Attributes
- Technical leadership experience within ML teams.
- Strong ownership mindset and accountability for outcomes.
- Self-directed, pragmatic, and highly execution-focused.
- Excellent communication and collaboration skills.
- Comfortable operating in fast-moving, high-trust environments.
Working Environment
The company believes exceptional products are built by small, world-class teams with high talent density. The culture values ownership, speed, collaboration, and continuous learning. Team members are expected to exercise judgement, execute independently, and contribute to building AI products capable of delivering meaningful impact at global scale.
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