Staff/Lead Machine Learning Engineer (Research)

Mimica
City of London
20 hours ago
Create job alert
What we are building

Mimica's mission is to empower enterprises, teams, and individuals to reclaim their most precious resource — time and work more efficiently, with greater purpose and impact.


Our AI-powered task mining observes employee actions across the desktop and categorizes them into detailed process maps. Mimica’s process intelligence highlights inefficiencies, prioritizes improvements based on ROI, recommends the optimal technology for automation (RPA, intelligent document processing, GenAI), and provides a blueprint for building new automations and transforming work.


Our approach to engineering

  • We prioritize customer needs first
  • We work in small, project-based teams
  • We have flexibility in terms of the problems we work on
  • We own the full lifecycle of our projects
  • We avoid silos and encourage taking up tasks in new areas
  • We balance quality and velocity
  • We have a shared responsibility for our production code
  • We each set our own routine to maximize our productivity

What will you own

In this role, you will be a member of the ML Chapter and work with the Mapper team. Mimica Mapper is one of our main products that creates intuitive flowcharts that map out user and team workflows. Its architecture includes components designed to automatically detect task similarities.


For the first 3 to 6 months, you will own projects to improve the task similarity algorithm and the use of the Mapper.


Part of your day-to-day

  • Design and run experiments to improve our task-similarity algorithms, using a mix of classic and deep learning techniques.
  • Write clear technical reports that document experiments and their results.
  • Write clean, readable, and maintainable Python code, assuring best practices.
  • Interface with our internal Process Analyst team to discover opportunities on which parts of the product can be automated, find out pain points and explore automation solutions by leveraging ML
  • Support productionization (although we have a dedicated MLOps Engineer for that!)
  • Actively collaborate and engage in technical discussions with the other Engineers, Product Managers in the team and ML Chapter, to drive the development of the product.
  • Contribute to knowledge sharing and the improvement of our processes.

Requirements

  • A researcher mindset, with curiosity and rigour in exploring and solving complex problems.
  • Experience with deep learning methods and techniques
  • Experience with transformer and embedding architecture
  • Strong technical skills in designing, setting up, running, and evaluating experiments.
  • Proficiency in supervised and unsupervised learning techniques.
  • Excellent written communication skills, including the ability to produce clear and concise reports.
  • Strong Python programming skills, emphasising clear, readable code; while productionization support may be involved, it is not the primary focus.
  • A drive to continually develop your skills, improve team processes, and reduce technical debt
  • Fluency in English, with the ability to effectively communicate abstract ideas, complex concepts, and trade-offs

Bonus

  • Graph ML knowledge
  • Experience working in a startup/scale-up environment

What we offer

💰 Generous compensation + stock options - aligned with our internal framework, market data, and individual skills.


🏢 Distributed work: Work from anywhere - fully remote, in our hubs, or a mix.


💻 Company-issued laptop*, remote setup stipend, and co-working budget


📍 Flexible schedules and location


☀️ Ample paid time off, in addition to local public holidays


🍼 Enhanced parental leave


🧘♀️ Health & retirement benefits


📖 Annual learning & development budget - up to £500 / €600 / $650 per year


🌴 Annual workaways and regular virtual & in-person socials


🌍 Opportunity to contribute to groundbreaking projects that shape the future of work


Note: Some benefits may vary depending on location and role
*On company equipment: Company-issued equipment (e.g. laptops) is provided for work use and must be returned upon departure, unless otherwise agreed.


#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer (Databricks)

Staff Machine Learning Engineer

Machine Learning Engineer (Manager)

Machine Learning Engineer (Manager)

Machine Learning Engineer (Manager)

Lecturer / Senior Lecturer in Artificial Intelligence (Machine Learning, NLP, Reinforcement Lea[...]

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.