data engineer

ai engineer

devops engineer

machine learning engineer

senior ai engineer

machine learning infrastructure engineer

Accenture

<div>YOU ARE
As a hands-on Infrastructure architect, you are an early-career engineer who learns and grows while contributing hands-on to the AI and machine learning infrastructure that powers real-world applications. Under the guidance of senior architects and engineers, you'll develop practical skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on. Day to day, you'll write and test code and deployment scripts, help configure cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and support data pipelines that feed AI and ML workflows. You'll learn to monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and troubleshoot issues across the computational stack with mentorship and support. This is a hands-on, learning-focused where you build expertise across modern tools and platforms &mdash; including container orchestration, model serving, CI/CD pipelines, InfraOps, MLOps, and AI monitoring &mdash; while making meaningful contributions to infrastructure that enables AI-driven busin
ess outcomes.
THE WORK<ul><li>
Write, test, and debug code and scripts for AI infrastructure tasks, including automation and tooling, under the guidance of senior engineers.</li><li>
Develop and maintain infrastructure and software deployment scripts to support reliable, repeatable releases of AI systems and models.</li><li>
Configure and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups.</li><li>
Deploy AI systems and machine learning models into production infrastructure, following established processes and best practices.</li><li>
Deploy data pipelines that feed AI and ML workflows, ensuring data is available, clean, and reliable.</li><li>
Assist with container orchestration and model serving, learning tools such as Docker, Kubernetes, and model deployment frameworks.</li><li>
Support and maintain CI/CD pipelines for automating the build, test, and deployment of AI infrastructure and applications.</li><li>
Monitor AI systems and infrastructure health across InfraOps and MLOps disciplines, tracking performance, reliability, and resource utilization.</li><li>
Perform AI monitoring to track model performance, detect drift or degradation, and surface issues for review.</li><li>
Troubleshoot and help resolve issues across the computational stack &mdash; hardware, networking, software, and models &mdash; with mentorship and support.</li><li>
Document configurations, processes, and procedures to maintain clear, repeatable, and shareable knowledge across the team.</li><li>
Collaborate with senior architects and engineers, participating in code reviews, team discussions, and knowledge-sharing sessions to grow technical skills.</li><li>
Apply security, cost-efficiency, and scalability best practices as you learn them, contributing to well-managed and responsible infrastructure.</li></ul></div>
 
<div>EDUCATION<ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Work or coursework experience with AI/ML or Computer engineering or Computer science.</li><li>
Coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud.</li><li>
Strong understanding of AI and machine learning as a subject.</li><li>
Strong understanding of computing infrastructure a subject, preferred knowledge of AI infrastructure.</li><li>
Proficiency in programming languages such as Python, Java, or C++.</li></ul></div>

AI Infrastructure Architecture Analyst

<div>YOU ARE
As a hands-on Infrastructure Architect, you are an experienced engineer with several years in infrastructure engineering who now takes on more complex, higher-impact work designing and optimizing the AI and machine learning infrastructure that powers real-world applications. Working alongside senior architects and engineers &mdash; and increasingly leading your own workstreams &mdash; you apply proven skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on. Day to day, you architect and optimize infrastructure components, write and review code and deployment scripts, design and tune cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and build and optimize data pipelines that feed AI and ML workflows. You optimize the computational stack for performance, cost, power, and scalability, monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and independently troubleshoot and resolve complex issues across the stack. You also mentor junior engineers, contribute to architectural decisions, and help establish best practices. This is a hands-on, ownership-driven where you apply and deepen your expertise across modern tools and platforms &mdash; including container orchestration, model serving, CI/CD pipelines, InfraOps, MLOps, and AI monitoring &mdash; while making meaningful contributions to infrastructure that enables AI-driven business outcomes.
THE WORK<ul><li>
Write, review, and debug code, scripts, and infrastructure-as-code for AI infrastructure, automation, and tooling, setting standards for quality across the team.</li><li>
Architect, configure, and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups, optimizing for performance and utilization.</li><li>
Design and maintain deployment automation and CI/CD pipelines to support reliable, repeatable releases of AI systems, models, and applications.</li><li>
Deploy AI systems, models, and data pipelines into production, defining and improving the processes and best practices others follow.</li><li>
Lead container orchestration and model serving using tools such as Docker, Kubernetes, and model deployment frameworks.</li><li>
Architect and optimize the computational stack for performance, power, cost, and scalability, balancing trade-offs against business goals.</li><li>
Evaluate and select tools, frameworks, and platforms, making recommendations that shape the infrastructure roadmap.</li><li>
Integrate AI models and systems into existing enterprise systems, ensuring interoperability, security, and regulatory compliance.</li><li>
Own AI monitoring and infrastructure health across InfraOps and MLOps, tracking performance, reliability, and utilization, and driving remediation.</li><li>
Independently troubleshoot and resolve complex issues across the computational stack &mdash; hardware, networking, software, and models &mdash; and lead root-cause analysis.</li><li>
Mentor junior engineers and lead code reviews, providing technical direction and supporting their growth.</li><li>
Define and document architecture standards, processes, and procedures, and apply security, cost-efficiency, and scalability best practices across the infrastructure.</li></ul></div>
 
<div>EDUCATION<ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Experienced with coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud.</li><li>
Strong understanding of AI and machine learning as a subject.</li><li>
Strong understanding of computing infrastructure a subject, preferred knowledge of AI infrastructure.</li><li>
Proficient in programming languages such as Python, Java, or C++.</li><li>
Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow).</li><li>
Strong problem-solving skills and ability to work in a fast-paced environment.</li><li>
Excellent communication and collaboration skills.</li><li>
Experience in AI/ML infrastructure engineering or related roles on a hyperscaler platform for deploying large scale solutions.</li></ul></div>

AI Infrastructure Architecture Specialist

Reflection

<div>
 
 Our Mission
 
 
 
 Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
 
 
 
 The Roles Mission
 
 
 
 Bridge the gap between research and production by turning cutting-edge algorithms into scalable training systems. You will design and optimize the core infrastructure behind frontier AI models &mdash; from reinforcement learning training loops and distributed GPU training to massive-scale data pipelines.
 
 
 Our systems train models across thousands of GPUs and process petabyte-scale datasets. We care deeply about numerical stability, throughput, and reproducibility.
 
 
 
 What This Team Does
 
 
 
 This team owns and evolves the core infrastructure behind our training systems.
 
 
 
 We Focus On
 
 
 
 <ul>
 <li>
 Reinforcement learning training infrastructure
 </li>
 <li>
 Distributed training and inference systems
 </li>
 <li>
 Experiment infrastructure and reproducibility
 </li>
 <li>
 Large-scale data pipelines
 
 
 </li>
 </ul>
 The goal is to build the engineering foundation that allows researchers to iterate quickly while training models at massive scale.
 
 
 
 About The Role
 
 
 
 You will architect and optimize the core training infrastructure that powers our models. This includes RL training loops, distributed GPU systems, and large-scale data pipelines.
 
 
 You will work closely with researchers to transform new ideas into reliable, scalable training systems.
 
 
 
 Responsibilities Include
 
 
 
 <ul>
 <li>
 Designing and optimizing large-scale training loops and data pipelines.
 </li>
 <li>
 Implementing state-of-the-art techniques and ensuring they are numerically stable and computationally efficient.
 </li>
 <li>
 Building internal tooling for launching, monitoring, and reproducing complex experiments.
 </li>
 <li>
 Diagnosing deep bottlenecks across the training stack (GPU memory issues, communication overhead, dataloader stalls).
 </li>
 <li>
 Translating research prototypes into reusable, production-grade infrastructure.
 
 
 </li>
 </ul>
 
 What You'll Work With
 
 
 
 
 Distributed Training
 
 
 
 <ul>
 <li>
 GPU parallelism (data, tensor, pipeline, expert)
 </li>
 <li>
 Large-scale distributed training infrastructure
 </li>
 <li>
 Communication optimization (NCCL, RDMA, GPU interconnects)
 </li>
 <li>
 FSDP / ZeRO and model sharding
 
 
 </li>
 </ul>
 Orchestration &amp; Runtime Systems
 
 
 <ul>
 <li>
 Ray, Kubernetes, Slurm
 </li>
 <li>
 Distributed runtimes and async systems
 </li>
 <li>
 Containerization and sandboxing
 
 
 </li>
 </ul>
 Frameworks
 
 
 <ul>
 <li>
 PyTorch
 </li>
 <li>
 JAX
 </li>
 <li>
 Megatron-style training stacks
 </li>
 <li>
 Triton / custom kernels
 
 
 </li>
 </ul>
 Data Infrastructure
 
 
 <ul>
 <li>
 Large-scale dataset curation pipelines
 </li>
 <li>
 Deduplication and filtering systems
 </li>
 <li>
 Tokenization and preprocessing
 </li>
 <li>
 Distributed data processing frameworks
 
 
 </li>
 </ul>
 
 About You
 
 
 
 <ul>
 <li>
 You are a strong software engineer who speaks the language of machine learning.
 </li>
 <li>
 You may not have a PhD, but you know how to implement a research paper.
 </li>
 <li>
 You have deep experience in at least one of the following: Distributed Training &amp; Inference or Data Infrastructure
 </li>
 <li>
 You enjoy working at the boundary between:
 </li>
 <ul>
 <li>
 Machine learning algorithms
 </li>
 <li>
 Distributed systems
 </li>
 <li>
 High-performance computing
 </li>
 </ul>
 <li>
 You care deeply about performance, numerical stability, and reproducibility.
 </li>
 <li>
 You thrive in high-agency environments and enjoy solving hard technical problems.
 
 </li>
 </ul>
 
 What We Offer
 
 
 
 We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models.
 
 
 We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported.
 
 
 <ul>
 <li>
 Top-tier compensation: Salary and equity structured to recognize and retain our talent globally.
 </li>
 <li>
 Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options.
 </li>
 <li>
 Health &amp; wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance.
 </li>
 <li>
 Meals: Lunch and dinner are provided in the office daily.
 </li>
 <li>
 Life &amp; family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.
 </li>
 <li>
 Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K.
 </li>
 <li>
 Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.
 </li>
 <li>
 Team building: We have regular off-sites, happy hours, and team celebrations.
 
 
 </li>
 </ul>
</div>

Member of Technical Staff - Research Software Engineer

Preply

<div><h2 class="jobSectionHeader">We power people&rsquo;s progress.</h2>
At Preply, we&rsquo;re all about creating life-changing learning experiences. We help people discover the magic of the perfect tutor, craft a personalised learning journey, and stay motivated to keep growing. Our approach is human-led, tech-enabled - and it&rsquo;s creating real impact.
We&rsquo;ve just reached unicorn status with a $150M Series D, accelerating our vision to transform education through human-led, AI-enhanced learning. Today, 100,000+ tutors teach 90+ languages to learners in 180 countries - and we&rsquo;re only getting started. As a category-defining company, we&rsquo;re shaping what the future of learning looks like at global scale.
Every Preply lesson sparks change, fuels ambition, and drives progress that matters. Joining Preply means helping define the future of education at global scale, and building something that truly matters for millions of people, every day.<h2 class="jobSectionHeader">
Meet the team!</h2>
Preply is scaling an AI-powered learning platform used by millions of learners and tutors worldwide. As ML and GenAI become increasingly central to our product, marketplace, and operational systems, we&rsquo;re looking for a Staff ML Ops Engineer to define and evolve the platform that enables machine learning at company scale.
This is a senior technical leadership role for an engineer who can combine deep hands-on expertise with platform vision. You will help define the current and future direction of Preply&rsquo;s ML platform, set technical standards, and design the systems that allow ML teams to move from research to production quickly, safely, and cost-efficiently.
You&rsquo;ll partner closely with Applied Science, Data, Product, and Engineering leadership to build a scalable, secure, and observable ML platform that powers multiple business lines. Your impact will come not only from the systems you design, but from the standards you set, the engineers you mentor, and the technical decisions you help de-risk across the organization.<h2 class="jobSectionHeader">
Why is this role important:</h2>
Preply&rsquo;s next stage of growth depends on making AI a scalable company-wide capability, not a collection of isolated models and experiments. This role will define how ML systems are built, deployed, monitored, and improved across the company.
You&rsquo;ll shape the platform that powers personalized learning, smarter experiences for tutors and learners, marketplace intelligence, content generation, automation, and future GenAI products. The goal is to make it dramatically easier for teams to turn ML ideas into reliable, secure, cost-efficient product experiences at a global scale.<h2 class="jobSectionHeader"> 
What you&rsquo;ll be doing:</h2><ul><li>
Define the technical vision and roadmap for Preply&rsquo;s ML platform, ensuring it can support growing ML and GenAI adoption across multiple teams, products, and business lines.</li><li>
Lead the architecture of platform capabilities across the full ML lifecycle: experimentation, feature engineering, artifact management, training, deployment, monitoring, retraining, and governance.</li><li>
Design cloud-native infrastructure for distributed training and inference, including GPU-based environments, autoscaling, workload isolation, rollout strategies, and cost optimization.</li><li>
Set the technical direction for CI/CD for ML, embedding testing, validation, security, performance checks, and release confidence into deployment pipelines.</li><li>
Establish observability standards for ML systems, including model metrics, service health, alerts, drift detection, data quality, lineage, and business-impact monitoring.</li><li>
Lead the evolution of Preply&rsquo;s GenAI and LLM platform capabilities, including building LLM Gateway services, vector retrieval infrastructure, prompt experimentation, evaluation frameworks, latency-optimized inference, and reliable model-serving patterns.</li><li>
Partner with Applied Science and Data leads, Product leaders, and Engineering teams to align platform investments with experimentation velocity, cost efficiency, operational reliability, and user impact.</li><li>
Design platform abstractions, internal libraries, templates, and self-service tooling that help ML Scientists and engineers move faster without compromising reliability or security.</li><li>
Act as a technical multiplier across engineering by mentoring senior engineers, influencing architecture, raising standards, and guiding teams through complex platform decisions.</li><li>
Identify and eliminate bottlenecks in the path from ML research to production, making the platform easier, safer, and more efficient for all ML-powered product development.</li></ul><h2 class="jobSectionHeader">
What you need to succeed:
</h2><ul><li>9+ years of engineering experience, with significant depth in large-scale ML, data, infrastructure, or platform systems.</li><li>
Proven ability to architect and scale production-grade ML platforms that support many teams, workflows, and ML use cases.</li><li>
Deep understanding of cloud-native architecture and end-to-end ML workflows, including experimentation, feature management, model versioning, training, deployment, monitoring, performance benchmarking, and lifecycle management.</li><li>
Strong hands-on experience with cloud platforms such as GCP or AWS, Kubernetes, distributed compute, CI/CD, observability, and infrastructure-as-code practices.</li><li>
Experience building enabling tools and platform capabilities for Applied Scientists, Data Scientists, and engineering teams.</li><li>
Strong technical judgment and the ability to make pragmatic architecture decisions across reliability, scalability, security, cost, and developer experience.</li><li>
Excellent communication and influence skills, with experience aligning cross-functional stakeholders and translating platform strategy into execution.</li><li>
Demonstrated ability to mentor engineers, raise engineering standards, and multiply the impact of teams around you.</li><li>
Product-impact mindset: you care about building platform capabilities that accelerate experimentation, improve user experiences, and unlock measurable business value.</li><li>
Familiarity with LLM frameworks and GenAI infrastructure, such as LangChain, LlamaIndex, vector stores, retrieval systems, prompt evaluation, model serving, or LLM observability.</li></ul><h2 class="jobSectionHeader">
Why you&rsquo;ll love it at Preply</h2><ul><li>
An open, collaborative, dynamic and diverse culture;</li><li>
A generous monthly allowance for lessons on Preply.com, Learning &amp; Development budget and time off for your self-development;</li><li>
A competitive financial package with equity, leave allowance and health insurance;</li><li>
Access to free mental health support platforms;</li><li>
The opportunity to unlock the potential of learners and tutors through language learning and teaching in 175 countries (and counting!)</li></ul>
#LI-AG1<h2 class="jobSectionHeader">
Our Principles</h2><ul><li>
Care to change the world - We are passionate about our work and care deeply about its impact to be life changing.</li><li>
We do it for learners - For both Preply and tutors, learners are why we do what we do. Every day we focus on empowering tutors to deliver an exceptional learning experience.</li><li>
Keep perfecting - To create an outstanding customer experience, we focus on simplicity, smoothness, and enjoyment, continually perfecting it as every detail matters.</li><li>
Now is the time - In a fast-paced world, it matters how quickly we act. Now is the time to make great things happen.</li><li>
Disciplined execution - What makes us disciplined is the excellence in our execution. We set clear goals, focus on what matters, and utilize our resources efficiently.</li><li>
Dive deep - We leverage business acumen and curiosity to investigate disparities between numbers and stories, unlocking meaningful insights to guide our decisions.</li><li>
Growth mindset - We proactively seek growth opportunities and believe today's best performance becomes tomorrow's starting point. We humbly embrace feedback and learn from setbacks.</li><li>
Raise the bar - We raise our performance standards continuously, alongside each new hire and promotion. We build diverse and high-performing teams that can make a real difference.</li><li>
Challenge, disagree and commit - We value open and candid communication, even when we don&rsquo;t fully agree. We speak our minds, challenge when necessary, and fully commit to decisions once made.</li><li>
One Preply - We prioritize collaboration, inclusion, and the success of our team over personal ambitions. Together, we support and celebrate each other's progress.</li></ul><h2 class="jobSectionHeader">
Diversity, Equity, and Inclusion</h2>
Preply.com is committed to creating an inclusive environment where people of diverse backgrounds can thrive. We believe that the presence of different opinions and viewpoints is a key ingredient for our success as a multicultural Ed-Tech company. That means that Preply will consider all applications for employment without regard to race, color, religion, gender identity or expression, sexual orientation, national origin, disability, age or veteran status.</div>

Staff Machine Learning Ops 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.

Team Overview

We're building a scalable feature platform that powers Ads ML by making high-quality features and training datasets easy to build, share, and maintain. Our small but growing team works on projects like batch &amp; realtime feature management platform, training set generation platform, sequence features platform and, agentic and automated ML workflows for feature lifecycle management.

We are looking for an engineer with experience in building high-scale data infrastructure and exposure to ML platforms to help evolve and scale our feature management systems.

This is not a pure ML modeling role. The ideal candidate is excited about building reliable infrastructure, data pipelines, and developer-facing tools that make ML engineers more productive.

What You'll Do
<ul>
<li>Design and build data infrastructure that supports large-scale feature and training set computation, transformation, and storage.</li>
<li>Develop frameworks for batch and real-time features with a focus on reliability, scalability, and ease of use.</li>
<li>Build platform capabilities for feature governance, including lineage tracking, validation, drift detection, anomaly monitoring, reproducibility, and versioning</li>
<li>Partner with ML engineers to ensure smooth integration of feature engineering workflows into ML production systems.</li>
<li>Build systems that support agentic ML workflows, including automated feature discovery, feature quality evaluation and feature lifecycle management</li>
<li>Contribute to operational excellence through observability, performance tuning, reliability engineering, and cost optimization initiatives.</li>
</ul>
What You Bring
<ul>
<li>3+ years in data infrastructure/platform engineering or ML infrastructure platforms.</li>
<li>Hands-on experience building production services, data pipelines, APIs, workflow systems, or developer tools.</li>
<li>Experience with at least one distributed data or compute system such as Spark, PySpark, Flink, Kafka, Ray, Airflow, Kubernetes, BigQuery, or similar technologies.</li>
<li>Familiarity with ML data workflows such as feature generation, training dataset creation, batch processing, real-time data processing, model training, experimentation, or online serving.</li>
<li>Strong coding skills and ability to write clean, maintainable, well-tested code.</li>
<li>Experience building intelligent automation or agentic workflows for ML systems is a strong plus</li>
<li>Experience with ML infrastructure and MLOps workflows spanning feature engineering, training pipelines, experimentation, model deployment, and online serving is a plus</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>

Machine Learning Systems Engineer, Ads ML Platform

Understanding Recruitment

<div>
 
 
 Founding MLOps &amp; Data Engineer - London (4 days on site)
 
 
 
 
 
 
 A venture-backed AI startup that's using machine learning to solve one of the biggest challenges in the mining industry are looking for a Founding MLOps &amp; Data Engineer to build the infrastructure that takes cutting-edge ML into production.
 
 
 
 
 
 
 What will I be doing?
 
 
 
 
 
 <ul>
 <li>
 Own the company's MLOps and data engineering strategy from the ground up.
 </li>
 <li>
 Build scalable data pipelines and production ML infrastructure.
 </li>
 <li>
 Develop reproducible, versioned and auditable machine learning workflows.
 </li>
 <li>
 Work closely with ML researchers to productionise new models.
 </li>
 <li>
 Collaborate with product engineers to integrate ML into customer-facing applications.
 </li>
 <li>
 Shape the architecture of the ML platform as the company grows, with plenty of autonomy to influence technical decisions.
 </li>
 </ul>
 
 
 
 
 
 What we're looking for:
 
 
 
 
 
 <ul>
 <li>
 Strong Python engineering skills with experience building production data pipelines.
 </li>
 <li>
 Commercial experience owning MLOps infrastructure and deploying machine learning models.
 </li>
 <li>
 Experience working with cloud platforms (Azure, AWS or GCP).
 </li>
 <li>
 A self-starter who enjoys working in fast-paced startup environments with lots of ownership.
 </li>
 <li>
 Someone who can work through ambiguity, prioritise effectively and solve problems independently
 </li>
 <li>
 Geospatial ML or scientific computing experience would be a plus
 </li>
 <li>
 Experience working in an early-stage or founding engineering team.
 </li>
 </ul>
 
 
 
 
 
 What's in it for you?
 
 
 
 
 
 <ul>
 <li>
 Salary between &pound;70,000 - &pound;100,000 depending on experience.
 </li>
 <li>
 Meaningful equity in a well-funded, early-stage startup.
 </li>
 <li>
 The opportunity to build and own the company's ML platform from day one.
 </li>
 <li>
 Work alongside experienced founders and engineers tackling genuinely difficult technical problems.
 </li>
 </ul>
 
 
 
 
 Apply now for immediate consideration!
 
</div>

Founding MLOps & Data 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.
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 Influence developer tooling choices so that AI‑assisted coding tools integrate safely with CI/CD, testing, and governance workflows.
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 Ensure the platform enables fast, safe experimentation while supporting robust, long‑lived production systems.
 
 
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 Governance, Reliability &amp; Commercial Impact
 
 
 
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 Ensure ML systems meet architecture, security, compliance, and operational standards.
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 Define and implement robust frameworks for monitoring technical health, model performance, and commercial impact of ML systems in production.
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 Champion operational excellence across ML services, including monitoring, alerting, incident management, and post‑incident learning.
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 Ensure clear ownership and lifecycle management for models and ML‑backed services.
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 Promote responsible use of automation and AI‑assisted tooling in safety‑critical or regulated contexts.
 
 
 
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 </ul>
 
 Collaboration &amp; Influence
 
 
 
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 Work closely with the Data Science team to ensure a smooth and repeatable transition from experimentation to production.
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 Collaborate with software engineers, product managers, and business stakeholders to deliver end‑to‑end ML‑driven solutions.
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 Act as a senior technical voice in design reviews, architecture forums, and strategic discussions.
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 Influence technical direction and standards through expertise, credibility, and collaboration rather than line management.
 
 
 
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 </ul>
 
 Technical Mentorship &amp; Capability Development
 
 
 
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 Provide hands‑on technical mentorship to Machine Learning Engineers and Data Scientists.
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 Raise engineering standards by sharing best practices, patterns, and lessons learned from real production systems.
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 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.
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 Contribute to technical hiring, assessment, and onboarding from a senior engineering perspective.
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 Help shape long‑term capability by identifying gaps in tooling, skills, and platform maturity.
 
 
 
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 What You&rsquo;ll Bring
 
 
 
 
 Experience &amp; Background
 
 
 
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 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, or a related quantitative field (or equivalent experience).
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 Extensive experience as a senior or principal Machine Learning Engineer delivering production ML systems at scale.
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 Proven track record of owning or shaping ML platforms, MLOps frameworks, or critical ML infrastructure.
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 Experience operating in complex, cross‑functional environments (insurance or financial services experience is advantageous but not essential).
 
 
 
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 </ul>
 
 Technical Expertise
 
 
 
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 Exceptional Python skills in a machine learning engineering context, with strong software engineering fundamentals (OOP, testing, design patterns).
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 Deep experience building, deploying, and operating production ML systems, including:
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 Online and batch model serving
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 Monitoring, alerting, and observability
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 Retraining and lifecycle management
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 </ul>
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 Strong understanding of core data science concepts, sufficient to:
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 Review and challenge modelling approaches
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 Ensure models are production‑ready and correctly evaluated
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 Hands‑on experience with a major cloud platform (AWS, GCP, or Azure), including containerised deployments.
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 Expert knowledge of MLOps and CI/CD, including:
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 Git‑based workflows
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 Infrastructure as Code (e.g. Terraform)
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 Automated testing and deployment pipelines
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 </ul>
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 Strong operational mindset, including APIs, logging, monitoring, reliability, and incident response.
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 Working knowledge of SQL and integration of ML services into wider data and application ecosystems.
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 Experience using AI‑assisted coding tools in a production engineering context, with a clear understanding of their benefits, limitations, and risks.
 
 
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 </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
 
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Principal Machine Learning Engineer

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