Engineering Manager

Annapurna
Sheffield
1 year ago
Applications closed

Related Jobs

View all jobs

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hyb...

Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR...

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hyb...

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hyb...

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hyb...

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hyb...

Engineering Manager


Job Type:Permanent Position


Location:Hybrid (UK Based)


Start Date:ASAP



About The Company:


We are a leading developer of embodied intelligence for autonomous vehicles. We use AI to pioneer a next-generation approach to self-driving: AV2.0, which enables fleet operators to unlock the benefits of AV technology at scale. We were the first to deploy AVs on public roads with end-to-end deep learning.



The role:


  • Lead a multidisciplinary team of Software Engineers and Systems Engineers, setting clear objectives and milestones. Drive strategic software deployment across AV systems, aligning with the company’s objectives.
  • Oversee the design and implementation of software that supports full sensor integration and data capture, ensuring high quality and scalability necessary for autonomous operations.
  • Ensure the delivery and maintenance of soft-real-time applications on Linux-based platforms, focusing on data collection, storage, and on-edge machine learning inference.
  • Develop fault-tolerant software solutions with comprehensive diagnostic tools to swiftly address and resolve issues impacting the operational capacity of our deployed AV fleet.
  • Craft and utilize advanced system monitoring tools to enhance performance metrics and troubleshoot both ad-hoc and systemic issues effectively.
  • Efficiently allocate resources, including personnel and technical infrastructure, to meet project timelines and performance goals.


About you:


Essential

  • At least 2 years in a leadership role within software development or embedded systems, including directly managing a software development team through all stages of the software lifecycle.
  • Strong knowledge of software development for embedded systems, real-time data processing, and system diagnostics, preferably within the automotive or similar regulated industries.
  • Hands-on experience with Linux-based development, real-time systems, and edge computing. Proficiency in programming languages such as C++ or Rust, and experience with relevant software development tools and environments.


Desirable

  • Automotive Software:Background in developing automotive software, with knowledge of ASPICE, DriveOS, or AutoSAR
  • Educational Background:A Master’s degree or greater in Computer Science, Electrical Engineering, or a related field is desired



If you would like to have a chat about this exciting opportunity, apply below or reach out directly to

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.

Jobs Careers

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.

Jobs

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.

Education Jobs

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.