Principal Engineer

Trust In SODA
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Principal Machine Learning Engineer - Production Systems

Principal Machine Learning Engineer

Principal Machine Learning Engineer – Production Systems

Join a Pioneering Team in Machine Intelligence

A cutting-edge technology company is seeking a talented and experienced Software Engineer to join their team and help revolutionize machine learning.


Remote - United Kingdom

Salary - very flexible, reflecting your experience.

We are looking for highly experienced individuals as most of the team would be staff level.


Key Responsibilities:

  • Responsible for architecting and implementing robust backend systems using Rust (or other systems-level language)
  • Contribute to and maintain core tools by implementing new features, optimizing existing solutions, fixing bugs, and participating in architectural design.
  • Conduct code reviews to uphold high standards of code quality.
  • Measure and optimize existing components to ensure they meet end-user requirements effectively.


Required Skills and Experience:

  • 6+ years of commercial experience
  • Strong foundation in computer science fundamentals and a proven track record of shipping production-grade code.
  • Deep understanding of distributed systems or systems at scale.
  • Experience designing and building solutions form start to finish
  • Willingness to learn Rust and proficiency in at least one other system-level language (Golang/Java/C++)
  • Strong foundation in operating systems, preferably Linux or macOS.


Preferred Skills and Experience:

  • Experience with UNIX APIs and networking protocols.
  • Experience working in high-growth startup or scale-up environments.
  • Experience working with metrics, spans, and traces.


If you're passionate about pushing the boundaries of technology and shaping the future of AI, this is an excellent opportunity to join a pioneering team building something completely new.

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.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

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

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.