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

Optics11

<div>
 We are expanding our team with a
 
 Data &amp; ML Infrastructure Engineer
 
 who will take ownership of the platform that enables data ingestion, processing, storage, and machine learning workflows across multiple products. Operating at the intersection of data science, software engineering, and cloud infrastructure, you will build the foundations and ensure that our ML systems are scalable, reproducible, and production ready.
 
 
 In this role, you will design, implement, and continuously improve a cloud-agnostic data and ML platform that supports multiple products and teams. You will be responsible for platform reliability, scalability, and cost-efficiency, while providing data scientists and ML engineers with the tools and infrastructure to efficiently develop, train, validate, and deploy machine learning models. You will serve as a technical bridge between the data science and IT/infrastructure teams, translating requirements into robust and maintainable systems.
 
 
 This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.
 
 
 Key Responsibilities:
 
 
 <ul>
 <li>
 Design, implement, and maintain the data and ML platform infrastructure, including systems for data ingestion, storage, processing, and training.
 </li>
 <li>
 Ensure high availability, reliability, and uptime of the platform, as it is critical to the business value proposition.
 </li>
 <li>
 Maintain and evolve vendor-delivered platform components and ensure long-term maintainability and independence.
 </li>
 <li>
 Build and maintain data workflows, including tools for dataset versioning, experiment tracking, and model lifecycle management across multiple products.
 </li>
 <li>
 Enforce DevOps / MLOps practices, including CI/CD pipelines, Infrastructure-as-Code, and automated workflows.
 </li>
 <li>
 Develop cloud-agnostic and containerized solutions that can run across public and private cloud environments within EU constraints.
 </li>
 <li>
 Optimize data storage, lifecycle policies, and cost efficiency, including tiered storage and retention strategies.
 </li>
 <li>
 Implement and maintain monitoring and alerting systems (e.g., Prometheus, Grafana) for pipelines, infrastructure, and resource usage.
 </li>
 <li>
 Ensure data governance, security, and compliance, including access control, audit logging, and anonymization requirements.
 </li>
 <li>
 Collaborate closely with data scientists, ML engineers, and IT teams to deliver infrastructure that meets operational needs.
 </li>
 <li>
 Manage resource and cost controls, including budgets, approvals, and tracking.
 </li>
 <li>
 Contribute to documentation, operational runbooks, and onboarding materials for the platform.
 
 
 
 </li>
 </ul>
 
 Why Join Us?
 
 
 
 <ul>
 <li>
 Join one of Europe&rsquo;s most promising deep-tech scale-ups.
 </li>
 <li>
 Shape the future of a rapidly growing multidisciplinary R&amp;D organization.
 </li>
 <li>
 Competitive salary and benefits package.
 </li>
 <li>
 Enjoy regular team activities and company events.
 </li>
 <li>
 Fresh team lunches provided3 times/ week.
 
 
 
 </li>
 </ul>
 Required Qualifications:
 
 
 <ul>
 <li>
 MSc or equivalent with 5+ years&rsquo; industry experience in Computer Science, Data Engineering, Machine Learning, or a related field.
 </li>
 <li>
 Strong experience with data platforms, cloud infrastructure, and distributed systems (AWS, GCP, Azure).
 </li>
 <li>
 Proven experience with Infrastructure-as-Code (e.g., Terraform) and automated deployments.
 </li>
 <li>
 Experience with ML platforms and tools (MLflow, Feast, DVC, DataHub or equivalent).
 </li>
 <li>
 Experience with AWS Sagemaker or Google Vertex AI platforms.
 </li>
 <li>
 Experience with databases (MongoDB, PostgreSQL, InfluxDB or equivalent).
 </li>
 <li>
 Experience with message brokers and streaming systems (Kafka, RabbitMQ or equivalent).
 </li>
 <li>
 Hands-on experience with containerization (Docker/OCI) and orchestration (Kubernetes or similar).
 </li>
 <li>
 Experience with CI/CD systems (e.g., GitLab CI).
 </li>
 <li>
 Programming skills in Python.
 </li>
 <li>
 Experience with monitoring and observability tools (Prometheus, Grafana).
 </li>
 <li>
 Understanding of DevOps and MLOps practices.
 </li>
 <li>
 Ability to work across teams and translate requirements into scalable infrastructure solutions.
 
 
 
 </li>
 </ul>
 
 Nice to Have:
 
 
 
 <ul>
 <li>
 Knowledge of data governance, IAM, and security frameworks.
 </li>
 <li>
 Experience with cost optimization and resource management in cloud environments.
 </li>
 <li>
 Familiarity with multi-tenant or multi-product platform architectures.
 </li>
 <li>
 Experience working with EU data compliance constraints in energy, maritime or defense.
 
 
 
 </li>
 </ul>
 
 Security &amp; Compliance:
 
 
 
 You'll be working with clients in the defense and security sectors, obtaining a Certificate of No Objection issued by the AIVD (Dutch General Intelligence and Security Service) is mandatory. This means you will need to undergo a security screening. For more information, please refer to details on security screening.
</div>

Data & ML Infrastructure Engineer

<div>We are expanding our team with a Data &amp; ML Infrastructure Engineer who will take ownership of the platform that enables data ingestion, processing, storage, and machine learning workflows across multiple products. Operating at the intersection of data science, software engineering, and cloud infrastructure, you will build the foundations and ensure that our ML systems are scalable, reproducible, and production ready.
In this role, you will design, implement, and continuously improve a cloud-agnostic data and ML platform that supports multiple products and teams. You will be responsible for platform reliability, scalability, and cost-efficiency, while providing data scientists and ML engineers with the tools and infrastructure to efficiently develop, train, validate, and deploy machine learning models. You will serve as a technical bridge between the data science and IT/infrastructure teams, translating requirements into robust and maintainable systems.
This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.<h4 class="jobSectionHeader">
Key Responsibilities:</h4><ul><li>
Design, implement, and maintain the data and ML platform infrastructure, including systems for data ingestion, storage, processing, and training.</li><li>
Ensure high availability, reliability, and uptime of the platform, as it is critical to the business value proposition.</li><li>
Maintain and evolve vendor-delivered platform components and ensure long-term maintainability and independence.</li><li>
Build and maintain data workflows, including tools for dataset versioning, experiment tracking, and model lifecycle management across multiple products.</li><li>
Enforce DevOps / MLOps practices, including CI/CD pipelines, Infrastructure-as-Code, and automated workflows.</li><li>
Develop cloud-agnostic and containerized solutions that can run across public and private cloud environments within EU constraints.</li><li>
Optimize data storage, lifecycle policies, and cost efficiency, including tiered storage and retention strategies.</li><li>
Implement and maintain monitoring and alerting systems (e.g., Prometheus, Grafana) for pipelines, infrastructure, and resource usage.</li><li>
Ensure data governance, security, and compliance, including access control, audit logging, and anonymization requirements.</li><li>
Collaborate closely with data scientists, ML engineers, and IT teams to deliver infrastructure that meets operational needs.</li><li>
Manage resource and cost controls, including budgets, approvals, and tracking.</li><li>
Contribute to documentation, operational runbooks, and onboarding materials for the platform.</li></ul><h4 class="jobSectionHeader">
Why Join Us?</h4><ul><li>
Join one of Europe&rsquo;s most promising deep-tech scale-ups.</li><li>
Shape the future of a rapidly growing multidisciplinary R&amp;D organization.</li><li>
Competitive salary and benefits package.</li><li>
Enjoy regular team activities and company events.</li><li>
Fresh team lunches provided3 times/ week.</li></ul><h4 class="jobSectionHeader"> 
Required Qualifications:</h4><ul><li>
MSc or equivalent with 5+ years&rsquo; industry experience in Computer Science, Data Engineering, Machine Learning, or a related field.</li><li>
Strong experience with data platforms, cloud infrastructure, and distributed systems (AWS, GCP, Azure).</li><li>
Proven experience with Infrastructure-as-Code (e.g., Terraform) and automated deployments.</li><li>
Experience with ML platforms and tools (MLflow, Feast, DVC, DataHub or equivalent).</li><li>
Experience with AWS Sagemaker or Google Vertex AI platforms.</li><li>
Experience with databases (MongoDB, PostgreSQL, InfluxDB or equivalent).</li><li>
Experience with message brokers and streaming systems (Kafka, RabbitMQ or equivalent).</li><li>
Hands-on experience with containerization (Docker/OCI) and orchestration (Kubernetes or similar).</li><li>
Experience with CI/CD systems (e.g., GitLab CI).</li><li>
Programming skills in Python.</li><li>
Experience with monitoring and observability tools (Prometheus, Grafana).</li><li>
Understanding of DevOps and MLOps practices.</li><li>
Ability to work across teams and translate requirements into scalable infrastructure solutions.</li></ul>
Nice to Have:<ul><li>
Knowledge of data governance, IAM, and security frameworks.</li><li>
Experience with cost optimization and resource management in cloud environments.</li><li>
Familiarity with multi-tenant or multi-product platform architectures.</li><li>
Experience working with EU data compliance constraints in energy, maritime or defense.</li></ul>
Security &amp; Compliance:
You'll be working with clients in the defense and security sectors, obtaining a Certificate of No Objection issued by the AIVD (Dutch General Intelligence and Security Service) is mandatory. This means you will need to undergo a security screening. For more information, please refer to details on security screening.</div>

<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

Agile Robots SE

<div>
 
 
 The AI Research Division of Agile Robots
 
 is looking for an
 
 ML Platform Engineer (m/f/d)
 
 , who will build and operate the distributed training, deployment, and experimentation infrastructure that research, data, and robotics teams depend on to move models from prototype to production.
 
 
 
 
 
 
 Your Responsibilities
 
 
 <ul>
 <li>
 
 Training Infrastructure:
 
 Design and scale distributed training workflows for large models using tools such as PyTorch Distributed, DeepSpeed, and cluster schedulers like SLURM or Kubernetes.
 </li>
 <li>
 
 ML Platform:
 
 Build and maintain containerised ML environments that support reproducible experimentation and benchmarking.
 </li>
 <li>
 
 CI/CD Pipelines:
 
 Develop and maintain CI/CD pipelines for machine learning systems to enable reliable testing, training, and deployment of models.
 </li>
 <li>
 
 Lifecycle Management:
 
 Implement experiment tracking, model versioning, and reproducibility workflows using tools such as ClearML or Weights &amp; Biases.
 </li>
 <li>
 
 Observability:
 
 Set up monitoring systems such as Prometheus and Grafana to track model performance and system health and detect drift in production.
 </li>
 <li>
 
 Cross-Team Collaboration:
 
 Work with research, data, and robotics teams to connect new models to robust production systems.
 </li>
 </ul>
 
 
 
 
 
 Essential Skills
 
 
 <ul>
 <li>
 
 Background and Experience:
 
 Degree in Computer Science, Software Engineering, or a related field, with professional experience building and operating ML or software infrastructure in production.
 </li>
 <li>
 
 Distributed Training:
 
 Experience designing and operating distributed training systems on Kubernetes and Docker, using PyTorch Distributed, DeepSpeed, and schedulers such as SLURM.
 </li>
 <li>
 
 CI/CD For ML:
 
 Experience building CI/CD pipelines that support reliable model testing, training, and deployment.
 </li>
 <li>
 
 Cloud Infrastructure:
 
 Experience operating ML workloads on cloud infrastructure, preferably AWS.
 </li>
 <li>
 
 Experiment Tracking:
 
 Hands-on experience with experiment tracking and model versioning using tools such as MLflow or Weights &amp; Biases.
 </li>
 <li>
 
 Observability:
 
 Experience with monitoring and drift detection using tools such as Prometheus and Grafana.
 </li>
 <li>
 
 Software Engineering:
 
 Python and system design skills, with experience building and operating ML systems beyond the prototype stage.
 </li>
 </ul>
 
 
 
 
 
 Beneficial Skills
 
 
 <ul>
 <li>
 
 Multimodal Systems:
 
 Experience with large-scale or multimodal ML systems such as vision-language-action models.
 </li>
 <li>
 
 Infrastructure As Code:
 
 Familiarity with infrastructure-as-code tools such as Terraform.
 </li>
 <li>
 
 ML Orchestration:
 
 Experience with ML pipeline and orchestration tools.
 </li>
 <li>
 
 Distributed Compute:
 
 Exposure to high-performance or distributed compute environments.
 </li>
 </ul>
 
 
 
 
 
 What we offer
 
 
 <ul>
 <li>
 Dynamic high-tech company combined with financial soundness and world class investors.
 </li>
 <li>
 Join an interdisciplinary, international team with 60+ different nationalities in a collaborative work environment.
 </li>
 <li>
 Lots of development opportunities in the context of our continued growth.
 </li>
 <li>
 Challenging tasks and impactful projects alongside experts that enable professional and personal growth.
 </li>
 <li>
 Corporate Benefits Program that covers health, mobility and learning with 100 &euro; net per month.
 </li>
 <li>
 Modern office facilites with a rooftop terrace overlooking Munich, free drinks &amp; fruits, and regular company events contribute to a good working environment.
 </li>
 </ul>
 
 
 
 
 
 Agile Robots SE
 
 is an international high-tech company based in Munich, Germany with a production site in Kaufbeuren and more than 2300 employees worldwide. Our mission is to bridge the gap between artificial intelligence and robotics by developing systems that combine state-of-the-art force-moment-sensing and world-leading image-processing technology. This unique combination of technologies allows us to provide user-friendly and affordable robotic solutions that enable intelligent precision assembly.
 
 
 
 
 
 This is made possible by our employees, who bring out the best in each and every day with creativity and enthusiasm. Become part of this team and shape the future of robotics with us!
 
 
 We are proud of our diversity and welcome your application regardless of gender and sexual identity, nationality, ethnicity, religion, age, or disability.
 
</div>

ML Platform Engineer (m/f/d)

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

Search 10000+ Machine learning infrastructure engineer jobs near you this July. New roles added regularly across leading industries. Apply today.

10000 machine learning infrastructure engineer jobs Near Me

AI Infrastructure Architecture Analyst

Data & ML Infrastructure Engineer

Data & ML Infrastructure Engineer

AI Infrastructure Architecture Specialist

Member of Technical Staff - Research Software Engineer

ML Platform Engineer (m/f/d)

Staff Machine Learning Ops Engineer