software engineer

machine learning engineer

ai engineer

senior software engineer

analytics engineer

ml platform engineer

Worldpay

<div>Ready to take your career global?
Make your mark at one of the biggest names in payments.
We are seeking an experienced Senior ML Software Engineer to join our Data Science Enablement team. You will be the primary software engineering expert for a product area, working as part of a cross-functional agile team and owning the successful launches and ongoing operations of high-profile products in production. Your focus will be on driving new feature delivery, maintaining operational excellence, and collaborating on enhancements to our shared ML platform. You will work closely with cross-functional collaborators&mdash;including ML research engineers, product managers, and platform engineers&mdash;to deliver scalable, reliable, and low-latency ML solutions.
In addition to technical expertise, this job requires strong collaboration skills.
What you&rsquo;ll own
Feature Development &amp; Delivery:<ul><li>
Design, implement, and deploy new features and enhancements to ML products, collaborating with Product and ML Research teams to refine requirements.</li></ul>
Technical Ownership and Stewardship:<ul><li>
Technical ownership of existing and new production ML products in your area, ensuring alignment of technical investments with business goals (in collaboration with a product owner) and engineering best practices.</li><li>
End-to-end technical stewardship of ML products, ensuring ongoing reliability and performance.</li></ul>
Contribute to ML platform:<ul><li>
Contribute to the evolution of the shared ML platform alongside other engineers to drive best practices and shared tooling across all products.</li></ul>
Operational Excellence:<ul><li>
Maintain and improve automated CI/CD pipelines, testing frameworks, and monitoring/logging, ensuring high operational standards.</li><li>
Conduct comprehensive code reviews to enforce coding standards, improve code quality, and share knowledge.</li></ul>
Continuous Improvement:<ul><li>
Identify and implement opportunities for process, tooling, and system improvements, proactively addressing technical debt and scaling challenges.</li></ul>
Release Management:<ul><li>
Oversee pre-release testing, coordinate releases and ensure smooth enablement of new features.</li></ul>
Leadership:<ul><li>
Provide technical guidance and support to other engineers and data scientists to solve complex technical challenges.</li><li>
Mentor and coach other engineers, supporting their professional growth.</li><li>
Foster a culture of collaboration, continuous improvement, and knowledge sharing.</li></ul>
Act Like an Owner:<ul><li>
Proactively identify and resolve blockers, navigate processes, and independently seek out information and connect with relevant teams to drive solutions in the face of ambiguity.</li><li>
Operate with a strong sense of urgency, consistently prioritizing and executing tasks to meet timelines and deliver results.</li></ul>
What you&rsquo;ll bring<ul><li>
5+ years as an ML-focused software engineer, ML Engineer, MLOps Engineer, or similar, with hands-on production experience</li><li>
Proven expertise with ML model deployment, API design, and integration into production environments</li><li>
Strong Python programming and relevant ML/data libraries</li><li>
Experience with containerization, orchestration, and AWS cloud services</li><li>
Building and operating CI/CD pipelines</li><li>
Monitoring, troubleshooting, and optimizing production ML systems</li><li>
Pre-release testing and release management</li><li>
Demonstrated ability to work independently, navigate ambiguity, and deliver results</li><li>
Excellent communication skills and ability to collaborate both within engineering organizations and on cross-functional teams</li><li>
Experience with OpenAPI, FastAPI, or similar</li></ul>
It&rsquo;s a bonus if you have<ul><li>
Experience with MLFlow, model versioning, and storage</li><li>
Familiarity with Databricks or similar platforms</li><li>
Experience supporting high-volume, real-time data products</li><li>
Automated testing and validation frameworks</li><li>
Experience designing and configuring low-latency databases to serve real-time features, such as DynamoDB</li><li>
Experience with Terraform Cloud</li><li>
Experience in large companies, mature engineering teams, and/or highly regulated industries</li><li>
AI-assisted coding experience (e.g. GitHub Copilot)</li></ul>
Why Join Us?<ul><li>
Impact: Play a key role as the technical owner of high-profile ML products delivering meaningful business impact to merchants and advancing key pillars of the company&rsquo;s strategy. Your work will directly influence the reliability, scalability, and evolution of critical production systems.</li><li>
Autonomy: Take end-to-end technical ownership of your product area, with the freedom and responsibility to drive technical solutions, shape best practices, and deliver results in a fast-paced, supportive environment.</li><li>
Collaboration: Join a cross-functional, high-performing team where your expertise is valued and your contributions make a real difference.</li></ul>
About the team
Our inclusive and global teams win together every day. We&rsquo;re proud to have the best minds in the industry, whom you can learn from as you grow your career. The people, the energy, and the connections are unmatched. Come and be part of an ever-evolving company and get dynamic opportunities that transcend borders.
What makes a Globalpayer?
Globalpayers think like a client, act like an owner, and win as one team. We&rsquo;re curious and innovative &ndash; always finding better ways to deliver impact. We empower each other to make decisions, and our passion drives excellence in everything we set out to do.
Does this sound like you? Then you sound like a Globalpayer. Apply now to take your career global.
#LI-AB1</div>

Senior ML Software Engineer

Explore Group

<div>
 
 
 London, 4 or 5 days onsite
 
 
 
 
 Upto &pound;120,000 + meaningful equity
 
 
 
 
 
 
 
 To meet global demand, the world needs to mine as much copper in the next 25 years as in all of human history. Current methods cannot scale to this challenge. We are a venture-backed university spin-out developing frontier technology to fix this, starting with the core of every mining company: the understanding of the orebody
 
 
 
 
 
 .
 
 Using explainable AI and value-of-information analysis, our uncertainty-aware models map subsurface resources from multimodal data. This allows mining companies to make faster, better-evidenced drilling decisions, halving costs and accelerating time to productio
 
 
 
 
 
 
 n.
 
 The R
 
 
 
 oleAs our Founding Engineer, you will build and own the pipelines that move data through our system and the infrastructure that turns our models into a reliable, auditable product. You will have full ownership over how our data and ML platform architecture evolves as we sca
 
 
 
 
 
 
 le.
 
 Key Responsibili
 
 
 <ul>
 <li>
 tiesRun MLOps by building and maintaining infrastructure for scalable training and inference, frictionless ML development, and reproducible, traceable mo
 </li>
 <li>
 delsShip clean, easy-to-debug pipeline code that is highly extensible for new workf
 </li>
 <li>
 lowsAlign with product engineers to ensure seamless integration of new models into the pro
 </li>
 </ul>
 
 
 
 
 
 duct
 
 Abou
 
 
 <ul>
 <li>
 t YouMission-driven and energized by solving hard, ambiguous problems in foundational indus
 </li>
 <li>
 triesSelf-directing with a proven track record of executing projects end-to-end and prioritizing ruthl
 </li>
 <li>
 esslyExpert Python skills and deep knowledge of modern data-pipeline to
 </li>
 <li>
 olingProven experience owning production-grade ML workflows and standing up cloud infrastru
 </li>
 <li>
 ctureBonus points if you have experience with geospatial ML platforms, scientific computing stacks, or making ML reproducible in high-stakes environ
 </li>
 </ul>
 
 
 
 ments
</div>

Machine Learning Engineer

Monzo

<div><div>We're on a mission to make money work for everyone.

We're waving goodbye to the complicated and confusing ways of traditional banking.

After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts, accounts for 16-17 year olds, a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save, invest and combine their pensions with us.

With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers!

We're not about selling products - we want to solve problems and change lives through Monzo ❤️</div> 
London / UK Remote | &pound;85,000&ndash;&pound;110,000 + Incentive Awards tied to your performance + Benefits 

About Machine Learning Platform Engineering at Monzo

The Machine Learning Platform team builds the systems that help teams across Monzo train, evaluate, deploy and serve ML models and AI features safely and reliably.

We work on backend services, Python libraries, model lifecycle tooling, evaluation workflows and low-latency serving systems. Our users are internal ML engineers, scientists and product teams building with ML and LLMs.

The work matters because machine learning powers many important decisions and experiences at Monzo, from fraud checks and credit decisions to customer operations. We help teams move faster while keeping production systems reliable, observable and safe.

This is a platform engineering role in the ML and AI space. We're looking for someone who combines strong software engineering foundations with ML or AI context, and who enjoys building tools and systems for other engineers.

 What you'll be working with...
<ul>
<li>Go for backend services, platform APIs, and production systems</li>
<li>Python for libraries, workflows, and tooling used by our ML engineers and scientists</li>
<li>Feature platforms and data workflows using Chronon, Feast, and DataHub</li>
<li>Model training pipelines and experiment tracking using Vertex AI and Comet</li>
<li>AI observability, evaluation, and tracing using Langfuse and Bifrost</li>
<li>AWS for real-time serving and online inference, GCP for batch compute and our BigQuery data warehouse.</li>
</ul>
(Please note direct experience with all of them is helpful but not required and our interview process can be completed in any language).

 We'd love to hear from you if...
<ul>
<li>You combine solid backend engineering with real ML or AI platform experience (ML pipelines, feature stores, model serving, experiment tracking or LLM tooling)</li>
<li>You've designed and operated distributed systems that handle scale, concurrency and failure</li>
<li>You think like a platform engineer, focused on developer experience and removing friction for internal teams</li>
<li>You're happy working across both Go and Python</li>
<li>You enjoy ambiguity and want to shape a platform as it grows</li>
<li>You have experience with strongly typed languages, writing and working on backend software</li>
<li>You're curious about how systems behave in production, including reliability, latency, quality, safety and operational risk</li>
</ul>
 This might NOT be the right fit if...
<ul>
<li>Your background is predominantly SRE, DevOps or infrastructure operations</li>
<li>You're focused on data science or modelling</li>
<li>You've shipped AI product features but haven't worked on the platform side (serving, evaluation, model lifecycle)</li>
</ul>
Levels

We're on the look out for Engineers of varying levels at the moment. You can read more in our Engineering Progression Framework - we will interview you across the entire framework, so if you are not sure what level you are aiming for please chat to your recruiters!

About our Engineering Teams

We have around 600 engineers out of roughly 5,000 people in total - and we have big ambitions. There are many interesting challenges ahead, and we're happy for people to move between teams or to specialise, whatever you prefer. As an engineer here you'd be able to work directly with anyone across the company, and we run regular knowledge-sharing sessions so you'll learn heaps about everything from how banks work to effective communication.

We contribute to open source software as much as possible. Our blog is a good place to learn even more about what we do.

 How we work

Locations &amp; Flexible Working

Our main tech hub is in London, but our engineers live everywhere in the UK, from Brighton to the Western Isles.

We value meeting in person but there's no pressure to come into the office, even if you're nearby. We believe you'll do your best work if you are where you want to be. If you live outside of London and we ask you to come into the office, Monzo will support you with the costs ✨

Our offices are naturally social, especially Tuesdays, Wednesdays and Thursdays, which happen to line up with our twice-weekly Monzo lunches &amp; treat Thursdays ️

Teams also often schedule time together for work and play, whether in or around the office, or online.

Set up a work schedule that delivers impact and fits your life

At Monzo, we value connections, flexibility, and wellbeing. We keep our meetings during core hours to stay connected and believe in maintaining work/life balance.

You'll be empowered to manage your work in a way that suits you and your team, giving you the freedom for children drop-offs and pick-ups, walking your dog or adventurous cat, avoiding peak commuting times or gym slots, appointments, or supporting your family in an emergency ✨

If you prefer to work part-time, we'll make this happen whenever we can - whether this is to help you meet other commitments or strike a great work-life balance.

The Interview Process

Our interview process involves three main stages:
<div>
<ul><li>Initial Call</li></ul>
<ul><li>Take home task or pair coding exercise</li></ul>
<ul><li>Final interview: including a system design and a behavioural interview</li></ul>
</div>
Our average process takes around 4 weeks but we will always work around your availability.

One of our engineers has written a detailed blog on their experience through this process, for extra details, hints and tips please see here.

What's in it for you
<div>
 &pound;85,000&ndash;&pound;110,000 + Incentive Awards tied to your performance 
✈️ We can help you relocate to the UK 
✅ We can sponsor visas. 
 This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London). 
<ul><li>We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team.</li></ul>
 Learning budget of &pound;1,000 a year for books, training courses and conferences 
➕ And much more, see our full list of benefits here</div><div> 

Equal opportunities for everyone

Diversity and inclusion are a priority for us and we're making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we're embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our blog, 2026 Diversity and Inclusion Report and 2025 Gender Pay Gap Report.

We're an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status.

If you have a preferred name, please use it to apply. We don't need full or birth names at application stage</div></div>

Machine Learning Platform Engineer

reddit

<div><div><div><div><div><div><div><div>Reddit is a community of communities. It's built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet's largest sources of information. For more information, visit www.redditinc.com.</div>
</div>
</div></div></div></div></div>Location: Reddit has a flexible first workforce. Don't live near our office? No worries: you can work remotely from anywhere in the UK or the Netherlands.

About the Team

The ML Efficiency team builds the infrastructure, tooling, and optimization systems that enable machine learning engineers and researchers to train, evaluate, deploy, and operate models efficiently at scale. We focus on improving developer productivity, reducing infrastructure costs, increasing hardware utilization, and accelerating experimentation across the company's ML ecosystem.

Responsibilities
<ul>
<li>Design and build systems that improve the efficiency of ML training and inference workloads.</li>
<li>Develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance.</li>
<li>Improve GPU and general resource utilization through scheduling, resource management, caching, and workload optimization.</li>
<li>Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements.</li>
<li>Build benchmarking frameworks and performance dashboards for training and serving systems.</li>
<li>Optimize distributed training infrastructure, data pipelines, and model serving architectures.</li>
<li>Lead cross-functional initiatives that improve the productivity of Reddit ML engineers.</li>
<li>Drive technical strategy for ML platform scalability, reliability, and cost efficiency.</li>
</ul>
Qualifications

Required
<ul>
<li>BS, MS, or PhD in Computer Science or a related field.</li>
<li>5+ years of software engineering experience.</li>
<li>Strong proficiency in Python</li>
<li>Profiency in at least one systems language (Go, C++, Rust, or Java) preferred</li>
<li>Experience building distributed systems at scale.</li>
<li>Experience with machine learning infrastructure, training systems, or model serving platforms.</li>
<li>Deep understanding of performance engineering and systems optimization.</li>
<li>Strong debugging and profiling skills.</li>
</ul>
Preferred
<ul>
<li>Experience with large-scale recommendation, ranking, generative AI, or foundation model systems.</li>
<li>Experience with distributed training frameworks such as PyTorch Distributed, Ray, Tensorflow, Spark</li>
<li>Familiarity with GPU architectures and performance analysis tools.</li>
<li>Experience optimizing cloud infrastructure costs across large ML workloads.</li>
<li>Contributions to internal platforms used by multiple ML teams.</li>
<li>Experience with building real time ML inference applications</li>
</ul>
What Success Looks Like
<ul>
<li>ML engineers can move from idea to experiment faster.</li>
<li>Training and inference costs decrease, performance increases, while model quality is maintained or improved.</li>
<li>GPU utilization and cluster efficiency increase.</li>
<li>Platform reliability improves as ML workloads scale.</li>
<li>Teams spend less time managing infrastructure and more time building models.</li>
<li>Average recommendation model size increases.</li>
</ul>
Benefits:
<ul>
<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>
<li>Family Planning Support</li>
<li>Gender-Affirming Care</li>
<li>Mental Health &amp; Coaching Benefits</li>
<li>Group Personal Pension Scheme with Employer match</li>
<li>Private Medical and Dental Scheme</li>
<li>Income Replacement Programs</li>
<li>Bike to Work scheme</li>
<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>
<li>Generous Paid Parental Leave</li>
</ul><div>
In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.

During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.

Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.</div></div>

Staff Machine Learning Engineer, ML Efficiency

Resolution

<div><h1 class="jobSectionHeader">About Resolution</h1>
Resolution does research on how to align artificial superintelligence (ASI). ASI may be developed in the next few years, but it is unclear whether alignment is on track to be ready in the same timeframe. We aim at higher a priori confidence in aligned outcomes by pursuing a portfolio of theory and empirics bets, any one of which &mdash; if it succeeds &mdash; would meaningfully advance the field. We invest heavily in research automation to accelerate progress, and we believe that stronger alignment theory unlocks higher automation: more principled approaches give us better filters for which directions of automated research are promising.
Resolution was founded in 2026 by researchers from UK AISI's Alignment Team, who ran the &pound;30m Alignment Project, and Timaeus, who pioneered applying singular learning theory to alignment.
For more information, see our announcement.<h1 class="jobSectionHeader">
About the Team</h1>
We are hiring Research Engineers across several main focus areas. We expect the boundaries between these areas to be flexible, but please indicate which mode you're more interested in (or &quot;either&quot;) in your application.
Research Automation (primary focus). A cross-cutting function that builds the infrastructure and tooling our researchers use to scale their work, increasingly leveraging fleets of AI research assistants alongside small teams of humans.
Program-embedded Research Engineering (also hiring). Research engineers embedded within one of our research programs (scalable oversight, complexity theory, learning theory, personas, and possible future programs like heuristic arguments or game theory), partnering with researchers on scaling experiments, building program-specific infrastructure, and translating theoretical insights into empirical tools.<h2 class="jobSectionHeader">
About the Role</h2>
Research Engineers at Resolution are core members of our research teams, directly driving both research and the core infrastructure behind it. We believe clean engineering on automation, experimentation, and infra is essential to ambitious research, and that excellence on this front requires active research participation.<h2 class="jobSectionHeader">
Responsibilities</h2><ul><li>
(Research automation track) Build agentic research infrastructure: experiment orchestration, hypothesis generation, automated analysis pipelines; autoformalization tooling for the theory side; internal AI-powered tools for researchers</li><li>
(Program-embedded track) Scale program experiments to frontier-tier models; build program-specific infrastructure; partner with researchers on engineering and implementation.</li><li>
(Both) Maintain and extend distributed training, experiment, and evaluation infrastructure</li><li>
(Both) Contribute to and maintain shared codebases across the org</li><li>
(Both) Communication of engineering &amp; automation progress, obstacles &amp; learnings to your team and the wider org via Slack and in weekly meetings.</li></ul>
We're on the lookout for excellence, so if you're a cracked engineer who doesn't precisely fit these descriptions, please still apply!<h2 class="jobSectionHeader">
You May Be a Good Fit If You</h2><ul><li>
Have a strong software engineering background, including production-quality Python</li><li>
Have deep experience with ML frameworks (PyTorch or Jax) and distributed-training stacks</li><li>
Have a demonstrated ability to ship complex systems end-to-end</li><li>
Have a Bachelor's degree or equivalent in CS, physics, math, ML, or related</li><li>
Are willing to use AI tools aggressively in your own workflow, with appropriate care to not get fooled!</li><li>
Are motivated by alignment of artificial superintelligence (ASI) and want to contribute to it full-time.</li></ul><h2 class="jobSectionHeader">
Strong Candidates May Also Have</h2><ul><li>
Experience with autoformalization, Lean, or other proof-assistant tooling</li><li>
Background in research infrastructure or ML platform engineering at frontier labs</li><li>
Experience scaling ML systems to 100B+ parameter scale</li><li>
Experience with CUDA kernel development or GPU optimization</li><li>
Familiarity with alignment research</li></ul><h1 class="jobSectionHeader">
Application Process</h1>
Initial screener. A 20-minute conversation with a senior researcher to discuss your research background, motivations, and interests.
Work trial. A 4-hour take-home assignment. We'll collect references in parallel. We'll compensate you for your time, subject to right-to-work constraints.
Work trial review. A 45-minute discussion of your work trial.
Research talk. A 30&ndash;40 minute interactive conversation about your previous research.
Senior 1:1. A 30-minute conversation with a member of our research leadership.<h1 class="jobSectionHeader">
Logistics</h1>
Salary: Your salary depends on the scope, autonomy, and impact we expect you to have while working at Resolution. The expected range of salaries for this role is:<ul><li>
L3 (SWE I): $141,000 remote; $236,000 in-person</li><li>
L4 (SWE II): $208,000 remote; $346,000 in-person</li><li>
L5 (Senior SWE): $270,000 remote; $451,000 in-person</li><li>
L6 (Staff SWE): $402,000 remote; $670,000 in-person</li><li>
L7 (Senior Staff SWE): $738,000 remote; $930,000 in-person</li></ul>
Strong early-career engineers will typically come in at L3. Someone with significant independent research engineering experience would likely start at L4 or L5.
Location: Berkeley, California. Remote may be considered in exceptional cases.
Benefits:<ul><li>
5 weeks of paid vacation per year, in addition to public holidays.</li><li>
Comprehensive healthcare insurance (medical, dental, vision).</li><li>
Unlimited sick leave to prioritize your well-being.</li><li>
An unconditional 401(k) contribution equal to 4% of your salary.</li></ul>
Visa sponsorship: We can sponsor visas for relocation to Berkeley.
Minimum education: A bachelor's degree in a field relevant to the role, or an equivalent combination of education, training, and/or professional experience that demonstrates comparable knowledge.
Deadline: Applications are rolling &mdash; please apply ASAP. We'll respond to applications within one week of receipt.
Start date: ASAP.
Compensation Range: $141K - $930K</div>

Research Engineer

Hiscox

<div>
 
 Job Type
 
 
 
 Permanent
 
 
 
 Build a brilliant future with Hiscox
 
 
 
 
 Principal Machine Learning Engineer
 
 
 
 
 Location:
 
 London/York
 
 
 
 Why Hiscox London Market
 
 
 
 Hiscox London Market sits at the centre of global specialist insurance, tackling some of the most complex and unusual risks in the world. These are not commoditised problems, they demand deep expertise, strong judgement, and increasingly, sophisticated data and machine learning capabilities.
 
 
 We have a strong track record of putting AI into real production use, from augmenting underwriting decisions to shaping future market standards through partnerships and market‑first innovation. This is an environment where advanced ML systems are expected to operate reliably, safely, and at scale, not remain in experimentation.
 
 
 You&rsquo;ll join a culture that values technical excellence, ownership, and courage, where senior individual contributors are trusted to set direction, challenge thinking, and build platforms that matter. For a Principal Machine Learning Engineer, this is a chance to work on high‑impact ML systems, influence how AI is adopted across the London Market, and help shape the future of insurance.
 
 
 
 Role Purpose
 
 
 
 As a
 
 Principal Machine Learning Engineer (MLE)
 
 , you bring a wealth of experience in building, scaling, and operating production machine learning systems, and use that experience to provide deep technical leadership across machine learning engineering and MLOps.
 
 
 You play a key role in shaping the architectural strategy for production ML systems and the ML Platform, working closely with Data Science, Engineering, and Platform teams to define patterns, standards, and tooling that enable reliable, repeatable delivery at scale. Through hands‑on contribution, design leadership, and technical mentorship, you help teams navigate complex technical decisions and build robust, maintainable systems.
 
 
 A central focus of the role is enabling the organisation to move quickly without sacrificing quality, evolving the ML platform, supporting the transition from experimentation to production, and helping teams adopt modern engineering practices. This includes championing the effective and responsible use of AI‑assisted development tools as part of a broader approach to improving developer experience, system quality, and long‑term sustainability.
 
 
 Success in this role comes from the practical application of deep experience, strong architectural thinking, and the ability to help others build better systems that deliver real business value.
 
 
 
 Key Responsibilities
 
 
 
 
 Technical Leadership &amp; Ownership (Individual Contributor)
 
 
 
 <ul>
 <li>
 Act as the technical lead for Machine Learning Engineering and MLOps across London Market.
 </li>
 <li>
 Technically lead the most complex and business‑critical ML systems, from architectural design through to production operation.
 </li>
 <li>
 Define and evolve production ML patterns and best practices, covering deployment, orchestration, monitoring, retraining, and decommissioning.
 </li>
 <li>
 Lead deep technical decision‑making, balancing scalability, reliability, security, and developer experience.
 </li>
 <li>
 Contribute hands‑on to critical systems, frameworks, and platform components where the complexity or impact demands it.
 </li>
 <li>
 Define best practices and guardrails for the use of AI‑assisted coding tools, ensuring they are used to improve productivity and code quality without compromising maintainability or operational safety.
 </li>
 <li>
 Lead by example in applying AI‑assisted development techniques (e.g. for prototyping, refactoring, and accelerating complex engineering work) with strong engineering judgement.
 
 
 
 </li>
 </ul>
 
 ML Platform Strategy &amp; Build‑Out
 
 
 
 <ul>
 <li>
 Partner closely with Group and Platform teams to design, build, and evolve the ML Platform, ensuring it supports:
 </li>
 <ul>
 <li>
 Reusable and scalable deployment patterns
 </li>
 <li>
 CI/CD for machine learning
 </li>
 <li>
 Full model lifecycle management
 </li>
 <li>
 Monitoring, observability, and alerting
 </li>
 <li>
 Secure and compliant operation
 </li>
 </ul>
 <li>
 Shape platform standards and interfaces that enable consistent ML delivery across squads and value streams.
 </li>
 <li>
 Lead technical spikes and proof‑of‑concepts to evaluate new tools, approaches, and architectural patterns.
 </li>
 <li>
 Influence developer tooling choices so that AI‑assisted coding tools integrate safely with CI/CD, testing, and governance workflows.
 </li>
 <li>
 Ensure the platform enables fast, safe experimentation while supporting robust, long‑lived production systems.
 
 
 </li>
 </ul>
 
 Governance, Reliability &amp; Commercial Impact
 
 
 
 <ul>
 <li>
 Ensure ML systems meet architecture, security, compliance, and operational standards.
 </li>
 <li>
 Define and implement robust frameworks for monitoring technical health, model performance, and commercial impact of ML systems in production.
 </li>
 <li>
 Champion operational excellence across ML services, including monitoring, alerting, incident management, and post‑incident learning.
 </li>
 <li>
 Ensure clear ownership and lifecycle management for models and ML‑backed services.
 </li>
 <li>
 Promote responsible use of automation and AI‑assisted tooling in safety‑critical or regulated contexts.
 
 
 
 </li>
 </ul>
 
 Collaboration &amp; Influence
 
 
 
 <ul>
 <li>
 Work closely with the Data Science team to ensure a smooth and repeatable transition from experimentation to production.
 </li>
 <li>
 Collaborate with software engineers, product managers, and business stakeholders to deliver end‑to‑end ML‑driven solutions.
 </li>
 <li>
 Act as a senior technical voice in design reviews, architecture forums, and strategic discussions.
 </li>
 <li>
 Influence technical direction and standards through expertise, credibility, and collaboration rather than line management.
 
 
 
 </li>
 </ul>
 
 Technical Mentorship &amp; Capability Development
 
 
 
 <ul>
 <li>
 Provide hands‑on technical mentorship to Machine Learning Engineers and Data Scientists.
 </li>
 <li>
 Raise engineering standards by sharing best practices, patterns, and lessons learned from real production systems.
 </li>
 <li>
 Coach teams on the effective and critical use of AI‑assisted coding tools, reinforcing the importance of code review discipline, testing, and long‑term maintainability.
 </li>
 <li>
 Contribute to technical hiring, assessment, and onboarding from a senior engineering perspective.
 </li>
 <li>
 Help shape long‑term capability by identifying gaps in tooling, skills, and platform maturity.
 
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Bring
 
 
 
 
 Experience &amp; Background
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, or a related quantitative field (or equivalent experience).
 </li>
 <li>
 Extensive experience as a senior or principal Machine Learning Engineer delivering production ML systems at scale.
 </li>
 <li>
 Proven track record of owning or shaping ML platforms, MLOps frameworks, or critical ML infrastructure.
 </li>
 <li>
 Experience operating in complex, cross‑functional environments (insurance or financial services experience is advantageous but not essential).
 
 
 
 </li>
 </ul>
 
 Technical Expertise
 
 
 
 <ul>
 <li>
 Exceptional Python skills in a machine learning engineering context, with strong software engineering fundamentals (OOP, testing, design patterns).
 </li>
 <li>
 Deep experience building, deploying, and operating production ML systems, including:
 </li>
 <ul>
 <li>
 Online and batch model serving
 </li>
 <li>
 Monitoring, alerting, and observability
 </li>
 <li>
 Retraining and lifecycle management
 </li>
 </ul>
 <li>
 Strong understanding of core data science concepts, sufficient to:
 </li>
 <ul>
 <li>
 Review and challenge modelling approaches
 </li>
 <li>
 Ensure models are production‑ready and correctly evaluated
 </li>
 </ul>
 <li>
 Hands‑on experience with a major cloud platform (AWS, GCP, or Azure), including containerised deployments.
 </li>
 <li>
 Expert knowledge of MLOps and CI/CD, including:
 </li>
 <ul>
 <li>
 Git‑based workflows
 </li>
 <li>
 Infrastructure as Code (e.g. Terraform)
 </li>
 <li>
 Automated testing and deployment pipelines
 </li>
 </ul>
 <li>
 Strong operational mindset, including APIs, logging, monitoring, reliability, and incident response.
 </li>
 <li>
 Working knowledge of SQL and integration of ML services into wider data and application ecosystems.
 </li>
 <li>
 Experience using AI‑assisted coding tools in a production engineering context, with a clear understanding of their benefits, limitations, and risks.
 
 
 </li>
 </ul>
 
 Why Join Us?
 
 
 
 This is an opportunity to shape the future of machine learning engineering at Hiscox, build a high-performing sub-chapter, and influence strategic decisions, while staying close to the craft you love. You&rsquo;ll have the autonomy to set standards, mentor talent, and explore emerging technologies, all within a collaborative and forward-thinking environment.
 
 
 
 Work with amazing people and be part of a unique culture
 
</div>

Principal Machine Learning Engineer

Databricks

<div>ReqID: FEQ327R328
Recruiter: Dina Hussain
Location: London, United Kingdom - Hybrid
Skills: Data Science, Machine Learning, AI, LLM, GenAI
As a Senior Specialist Solutions Engineer (SSE), ML Engineering, you will be the trusted technical ML expert to both Databricks customers and the Field Engineering organisation. You will work with Solution Architects to guide customers in architecting production-grade ML applications on Databricks, while aligning their technical roadmap with the evolving Databricks Data Intelligence Platform. You will continue to strengthen your technical skills through applying the latest technologies in GenAI, LLMOps, and ML, while expanding your impact through mentorship and establishing yourself as an ML expert.
You will be reporting to the Manager, Field Engineering (Specialist Team)
The impact you will have:
<ul><li>Lead the architectural design of production-grade ML workloads on our unified platform, encompassing the entire MLOps lifecycle from end-to-end pipeline creation and optimization (training/inference) to seamless integration with cloud-native services.</li>
<li>Provide advanced technical support to the Solution Architects during the technical sales cycle by building MVPs, leading deep-dive technical sessions, and strategically aligning ML/data science solutions to complex customer business challenges using relevant real-world examples.</li>
<li>Serve as the trusted technical advisor for customers developing GenAI solutions, specializing in the design and implementation of RAG architectures on enterprise knowledge bases, enabling natural language querying of structured data, and establishing content generation and monitoring frameworks.</li>
<li>Drive community growth and platform adoption through thought leadership activities, including the creation of technical tutorials and training materials, as well as leading hackathons and presenting at industry conferences.</li>
</ul>What we look for:
<ul><li>Experienced, technical, customer-facing, and with a background in Data Science / Machine Learning, and Data Engineering. Looking to learn and develop in a customer-facing technical role as a subject matter expert (SME) in a pre-sales environment.</li>
<li>Pre-sales or post-sales experience working with external clients across a variety of industry markets</li>
</ul>Data Science/ML Skills
<ul><li>Hands-on industry ML experience in at least one of the following:</li>
<li><ul><li>ML Engineer: Develop production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring</li>
<li>Data Scientist: Experience with the latest techniques in natural language processing, including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI</li>
</ul></li><li>Hands-on experience working with Distributed Spark based systems.</li>
<li>Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience</li>
<li>Experience communicating and teaching technical concepts to non-technical and technical audiences alike</li>
<li>Passion for collaboration, life-long learning, and driving our values through ML</li>
<li>[Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role</li>
<li>[Preferred] Experience working with Apache Spark&trade; to process large-scale distributed datasets</li>
<li>Can meet expectations for technical training and role-specific outcomes within 3 months of hire</li>
<li>Can travel up to 30% when needed</li>
</ul><div>About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide &mdash; including Comcast, Cond&eacute; Nast, Grammarly, and over 50% of the Fortune 500 &mdash; rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark&trade;, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
 
 
Benefits
 
 
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. 
 
 
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.</div></div>

Senior Specialist Solutions Engineer (AI/ML)

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