Rowden on cord

Rowden

Machine Learning Engineer

External position
Bristol, UK
Hiring internally for Rowden?
Engineering · Security
Building the UK’s next generation engineering powerhouse, providing critical technology for national resilience.
Posted
6 days ago
Checked
cord regularly checks that external positions are still open
23 hours ago

Skills & Experience

Job roles: Machine Learning Engineer
Experience level: Mid, Senior, Lead
Core skills considered: Machine Learning, Deep Learning, Data Pipelines, Python
Other skills considered: CI/CD, Docker, Kubernetes

Logistics

Base salary: £50K - £95K
Employment type: Permanent
Remote working: Hybrid (up to 2 remote days p/w)
Visa sponsorship: Not available

Job Description

We are growing our ML team and hiring across mid, senior, lead and principal levels. We are looking for AI builders; you will be working on developing and deploying AI systems to solve complex problems that have real-world impact. You'll join an existing ML team that works in close collaboration with software, hardware and systems teams to get useful AI into the hands of users. Our ML team works end-to-end, from R&D to deployment, across traditional ML, deep learning, data engineering, foundation models and LLM/agentic systems. We are now hiring across a broad range of ML skills, including model training, evaluation, optimisation, infrastructure and deployment.

As an ML Engineer at Rowden, you will contribute to, own or lead development effort on projects and products, depending on your experience and level. You will work from applied research through to production, developing and deploying AI systems that solve complex problems with real-world impact. Our work is broad, spanning edge and embedded deployment, model evaluation, performance optimisation, data pipelines, large-scale training and ML infrastructure all focused on bringing useful AI capability to edge and embedded environments. We are building a team with complementary strengths.

No prior defence experience is required. We're interested in people who've built and deployed AI systems in demanding environments and are passionate about delivering tangible value to end users, whatever the sector.


Responsibilities

  • Own and ship ML in production: take ideas from R&D to robust, maintainable deployments - often onto edge or embedded hardware.
  • Train and adapt models: work on model development, fine-tuning, evaluation and optimisation for real-world use cases.
  • Work at scale where needed: run and improve training and inference workloads across GPUs, including multi-GPU or multi-node environments, to support models that can perform reliably in constrained settings.
  • Improve performance: profile, optimise and debug ML systems across model code, data pipelines, inference stacks and hardware constraints.
  • Own evaluation quality: design evaluation pipelines, benchmarks, test sets and feedback loops that help us understand model behaviour before and after deployment.
  • End-to-end ownership: data collection/curation, feature engineering, model training, evaluation, deployment, monitoring, and iteration.
  • MLOps/LLMOps: CI/CD for models, containerisation/orchestration, experiment tracking and registry, model evaluation pipelines, safety guardrails, canaries, and performance monitoring.
  • Cross-team collaboration: partner with software, systems, and product colleagues; simplify complex topics for other disciplines and customers.
  • Data foundations: establish pragmatic data pipelines (batch/stream) that make curation, provenance, and reproducibility first-class.
  • Raise the bar: depending on level, mentor others, guide technical decisions and improve engineering standards across the team.

Requirements

  • Proven delivery: experience building, training, evaluating, optimising or deploying ML systems for real-world use, ideally in demanding environments.
  • Deep domain expertise: Strong capability in at least one major area of ML, such as optimisation, computer vision, sequence modelling, LLMs, probabilistic methods, model evaluation or large-scale training.
  • ML & maths depth: Strong grounding in ML/DL (optimisation, generalisation, probability, model architecture) and the ability to reason about these trade-offs in production.
  • Software development: Strong Python skills and good software engineering habits, including version control, testing, code review, debugging and maintainability.
  • Interpersonal skills: strong communicator who can mentor, influence, and bridge technical and non-technical audiences.
  • Education: Degree, postgraduate study or equivalent practical experience in machine learning, computer science, engineering, mathematics or a related technical field.
  • Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity.
  • MLOps excellence: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation.
  • Data engineering: proficiency with Databricks, Apache Spark, Delta Lake, MLflow, and SQL; experience integrating datasets and maintaining data quality.
  • Model training and optimisation: experience with pre-training, fine-tuning, distributed training, inference optimisation or adapting models for constrained environments.
  • Education: PhD in AI/ML/CS or related field.

About you

You've built ML systems that persist - deployed in real settings, iterated over time, and improved through real-world feedback. You enjoy guiding others, keeping systems healthy, and making the complex understandable.

Machine Learning Engineer at Rowden
Position posted 6 days ago

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Starling Bank on cord

Starling Bank

Full Stack Engineer (ML Ops)

London, UK
FinTech · Banking · Finance
A better bank for everyone
Active
over 6 months ago
Responds to
0% of requests
Responds in
1h 55m
Requests
7 pending

Skills & Experience

Job roles: Full Stack
Experience level: Mid, Senior
Core skills considered: Java, React, Redux, AWS, JavaScript

Logistics

Base salary: £70K - £110K
Employment type: Permanent
Remote working: Hybrid (up to 2 remote days p/w)
Visa sponsorship: Not available

Job Description

Starling is the UK’s first and leading digital bank on a mission to fix banking! Our vision is fast technology, fair service, and honest values. All at the tap of a phone, all the time.

Starling is the UK’s first and leading digital bank on a mission to fix banking! We built a new kind of bank because we knew technology had the power to help people save, spend and manage their money in a new and transformative way.

We’re a fully licensed UK bank with the culture and spirit of a fast-moving, disruptive tech company. We’re a bank, but better: fairer, easier to use and designed to demystify money for everyone. We employ more than 3,000 people across our London, Southampton, Cardiff and Manchester offices.

Our technologists are at the very heart of Starling and enjoy working in a fast-paced environment that is all about building things, creating new stuff, and disruptive technology that keeps us on the cutting edge of fintech. We operate a flat structure to empower you to make decisions regardless of what your primary responsibilities may be, innovation and collaboration will be at the core of everything you do. Help is never far away in our open culture, you will find support in your team and from across the business, we are in this together!

The way to thrive and shine within Starling is to be a self-driven individual and be able to take full ownership of everything around you: From building things, designing, discovering, to sharing knowledge with your colleagues and making sure all processes are efficient and productive to deliver the best possible results for our customers. Our purpose is underpinned by five Starling values: Listen, Keep It Simple, Do The Right Thing, Own It, and Aim For Greatness.

Hybrid Working

We have a Hybrid approach to working here at Starling - our preference is that you're located within a commutable distance of one of our offices so that we're able to interact and collaborate in person. We don't like to mandate how much you visit the office and work from home, that's to be agreed upon between you and your manager.

Our Data Environment

Our Data teams are excited about the value of data within the business, powers our product decisions to improve things for our customers and enhance effective and agile decision making, regardless of what their primary tech stack may be. Hear from the team in our latest blogs or our case studies with Women in Tech.

We are looking for talented data professionals at all levels to join the team. We value people being engaged and caring about customers, caring about the code they write and the contribution they make to Starling. People with a broad ability to apply themselves to a multitude of problems and challenges, who can work across teams do great things here at Starling, to continue changing banking for good.

Requirements

We have built our entire banking platform in house and mostly in Java. We are looking for people who want to work on building the tooling that is used by our engineers on a daily basis.

We are looking for people who are truly full stack, and are as comfortable polishing their javascript front end as they are debugging the innards of their java applications database interactions, or standing up infrastructure with terraform. We are looking for people who can:

  • Design REST apis.
  • Code backend services, ideally using Java, or another other server side compiled language.
  • Develop modern front ends, ideally using React and Redux.
  • Get their code into the cloud and support it there, ideally on AWS.
  • Believe in clean coding, simple solutions, automated testing and continuous deployment.
  • Like to take ownership of a feature from the original idea through to live.
  • Think (like us) that a small number of empowered developers is the right way to deliver software.

Company Benefits

  • 33 days holiday (including flexible bank holidays)
  • An extra day’s holiday for your birthday
  • 16 hours paid volunteering time a year
  • Part-time and/or flexible hours available for most roles
  • Salary sacrifice, company enhanced pension scheme
  • Life insurance at 4x your salary
  • Hybrid/remote working
  • Private Medical Insurance with VitalityHealth including mental health support and cancer care. Partner benefits include discounts with Waitrose, Mr&Mrs Smith and Peloton
  • Generous family-friendly policies
  • Varied social groups set up and run by our employees
  • Perkbox membership giving access to retail discounts, a wellness platform for physical and mental health, and weekly free and boosted perks
  • Access to initiatives like Cycle to Work, Salary Sacrificed Gym partnerships and Electric Vehicle (EV) leasing

Full details are available on our careers site

Interview Process

Interviewing is a two way process and we want you to have the time and opportunity to get to know us, as much as we are getting to know you! Our interviews are conversational and we want to get the best from you, so come with questions and be curious. In general you can expect the below, following a chat with one of our Talent Team:

  • Stage 1 - 30 mins with one of the team
  • Stage 2 - Take home challenge
  • Stage 3 - 90 mins technical interview with two team members
  • Stage 3 - 45 min final with an executive and a member of the people team
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