Machine Learning Engineer, Trilogy (Remote) - $100,000/year USD

Crossover
Hemel Hempstead
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer - London

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Ready to leverage your mastery of LLMs to drive productivity? At Trilogy, we're opening doors to an exceptional tech career, welcoming those who've honed their AI skills to elevate their expertise in a dynamic environment. We're offering a rare chance where your primary focus will be to further expand your proficiency in LLMs.

In an industry often filled with more buzzwords than tangible progress, Trilogy stands out as a hub of genuine innovation. Our focus is on practical, real-world applications aimed at reshaping industries. Imagine creating AI-driven tools that streamline workflow, automate tasks, or enhance decision-making processes, all to significantly boost productivity.

Your mandate? Harnessing LLMs to revolutionize how businesses operate, improving efficiency and effectiveness. You'll be architecting solutions that integrate AI seamlessly, making intricate processes more accessible and refining workflows for maximum output. Here, you won't be lost in bureaucratic hurdles or pitching ideas into the void. Instead, you'll witness the direct impact of your efforts, as your work directly influences the evolution of productivity tools.

Ready to unleash your expertise and become a force of change? Let's explore if you're the catalyst we're seeking for this exciting opportunity!


What you will be doing

  • Designing and building high-quality AI automations to streamline processes, enhance productivity, and deliver robust, scalable solutions across diverse applications
  • Experimenting with state-of-the-art AI tools like GPT-4 Vision and Amazon CodeWhisperer, integrating them into our developmental process to assess and enhance their utility
  • Evaluating and optimizing the implementation of AI solutions across various infrastructures, including AWS, to ensure seamless performance and integration


What you will NOT be doing

  • Traditional coding - our AI does the heavy lifting, freeing you to innovate and strategize
  • Being stuck on repetitive tasks - no two problems are the same


Key Responsibilities

Architecting and deploying sophisticated, fully-automated AI systems that require zero human intervention for a truly scalable impact


Candidate Requirements

  • Advanced generative AI proficiency (i.e., use of multiple AI tools, ability to automate workflows and custom GPTs); if you've only used LLMs for research, learning, brainstorming, or content generation, that will be deemed insufficient
  • At least 3 years of professional work experience
  • Proficiency in Python and API integration
  • Proficiency in AWS


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.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

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.