ml platform engineer

kaiko.ai

<div>
 <strong>
  About Kaiko.ai
  <br/>
  <br/>
 </strong>
 Kaiko is building a next-generation agentic clinical AI assistant that helps clinicians reason across patient data, guidelines, and diagnostics.
 <br/>
 <br/>
 Healthcare decisions are rarely made by a single person or from a single data source. kaiko's assistant maintains longitudinal patient context across encounters, clinicians, and institutions, enabling collaboration, second opinions, and complex diagnostic workflows. The system is designed to operate safely in real clinical environments, with human oversight, auditability, and regulatory alignment at its core.
 <br/>
 <br/>
 Our assistant core supports broadly applicable clinical tasks such as patient data navigation, guideline interaction, multimodal interaction (chat and voice), and care coordination. On top of this foundation, we are developing specialized diagnostic agents in areas such as oncology, radiology, and pathology.
 <br/>
 <br/>
 We build in close collaboration with leading hospitals and research centers, including the Netherlands Cancer Institute (NKI). kaiko is a well-funded company with a growing international team, operating from Zurich and Amsterdam.
 <br/>
 <br/>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 As an ML Platform Engineer, you'll help architect, scale, and evolve the infrastructure that powers kaiko's foundation-model training and serving across the full ML lifecycle: from compute orchestration that runs large-scale training jobs, through experiment tracking and model registry, to GPU-backed serving in production.
 <br/>
 <br/>
 The Data &amp; AI Platform team owns the foundation underneath kaiko's training and serving workloads: the orchestration and distributed compute layer (Dagster + Ray on Kubernetes), the storage layer (Hammerspace), and the observability that keeps them reliable. We support both research, with large-scale training workloads on open-weight MoE models in the hundreds-of-billions to trillion-parameter range, and product, with the engineering teams building kaiko's clinical applications.
 <br/>
 <br/>
 Today, our platform is built around large-scale training. The next frontier is closing the loop: building the model lifecycle layer (experiment tracking, registry, versioning, and GPU-backed serving) that lets our trained models move reliably into product. You'll be part of the team that gets us there.
 <br/>
 <br/>
 You'll work closely with both research and product engineering to understand what the platform needs to deliver, and own the engineering of how. You'll contribute to the platform's evolution across both training and serving dimensions, helping translate team needs into reliable, scalable infrastructure.
 <br/>
 <br/>
 You will be based in either The Netherlands or Switzerland, with the expectation of spending at least 50% of your time at the office.
 <br/>
 <br/>
 <strong>
  Some areas of responsibility
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Build and evolve the infrastructure that makes ML development fast, reliable, and observable: from IaC to CI/CD to Kubernetes-based workload orchestration.
  </li>
  <li>
   Contribute to the compute orchestration layer: help scale GPU workloads across heterogeneous on-prem and cloud clusters using Kubernetes, Ray, and advanced GPU scheduling technologies such as KAI-Scheduler.
  </li>
  <li>
   Support hybrid and multi-cloud strategies that balance performance, compliance, and cost, including the Hammerspace storage rollout.
  </li>
  <li>
   Own and evolve parts of the AI Factory: the Dagster-based orchestrator and its Ray integration that training jobs depend on.
  </li>
  <li>
   Build and maintain the model lifecycle layer: experiment tracking, model registry, versioning, and GPU-backed serving infrastructure, so models trained on our clusters move reliably into production.
  </li>
  <li>
   Partner with both research and product engineering to understand platform requirements and translate them into shared infrastructure that works across use cases.
  </li>
  <li>
   Bring engineering rigour to a fast-moving stack: lineage, reproducibility, ownership boundaries, and documentation that lets the team move quickly without losing track.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  About You
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   2-5 years of experience in production ML platform engineering or ML Ops role.
  </li>
  <li>
   Hands-on experience with Kubernetes, Helm, Terraform, Docker, and CI/CD tooling (ArgoCD, GitHub Actions, or comparable). Comfortable with modern observability stacks (Grafana, Prometheus, Loki, or similar).
  </li>
  <li>
   Compute orchestration at scale. Production experience scheduling GPU workloads on Kubernetes, Ray, or comparable, or strong motivation to grow into this quickly.
  </li>
  <li>
   ML infrastructure exposure. Hands-on experience with Linux and NVIDIA GPU environments, including multi-node training stacks and the networking that connects them (InfiniBand or comparable). You have a feel for how researchers and engineers use the platform, not just how it's built.
  </li>
  <li>
   Familiarity with the full ML workflow: training runs, experiment tracking, model registry and versioning (MLflow or equivalent), and model serving. You understand how models move from training to production.
  </li>
  <li>
   Solid software engineering. Python as a baseline; openness to picking up Go or other programming languages where the platform demands it.
  </li>
  <li>
   Collaborative by default. You work across team boundaries (research, product, data engineering) and design interfaces that serve multiple stakeholders without requiring everyone to understand the underlying plumbing.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Nice to have:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Direct experience supporting large-scale foundation-model training or inference, with a working sense of how a run behaves from the platform's perspective: MFU, GPU utilisation, communication overhead, and dataloader stalls on the training side; serving frameworks (vLLM, Triton, TorchServe), KV cache management, and batching strategies on the inference side.
  </li>
  <li>
   Familiarity with lower-level GPU communication and I/O concepts: RDMA, GPUDirect Storage (GDS), GPUDirect RDMA, NCCL, and how these affect training throughput and cluster design.
  </li>
  <li>
   Experience with Kubernetes-native scheduling stacks for accelerator-heavy workloads, such as Volcano, KAI Scheduler, YuniKorn, or similar, so scheduler tradeoffs are understood rather than defaulted to.
  </li>
  <li>
   High performance (parallel) filesystems that feed GPU's with high throughput and low latency (Hammerspace, CEPH, WEKA, VAST).
  </li>
  <li>
   On-prem and hybrid GPU environments, especially in regulated settings (healthcare, finance, public sector).
  </li>
  <li>
   Exposure to MoE architectures or large-scale distributed training, useful for anticipating what future workloads will demand of the platform.
   <br/>
   <br/>
  </li>
 </ul>
 We are excited to gather a broad range of perspectives in our team, as we believe it will help us build better products to support a broader set of people. If you&rsquo;re excited about us but don&rsquo;t fit every single qualification, we still encourage you to apply: we&rsquo;ve had incredible team members join us who didn&rsquo;t check every box!
 <br/>
 <br/>
 <strong>
  Why kaiko
  <br/>
  <br/>
 </strong>
 At kaiko, we believe the best ideas come from collaboration, ownership and ambition. We&rsquo;ve built a team of international experts where your work has direct impact. Here&rsquo;s what we value:
 <br/>
 <br/>
 <ul>
  <li>
   Ownership: You&rsquo;ll have the autonomy to set your own goals, make critical decisions, and see the direct impact of your work.
  </li>
  <li>
   Collaboration: You&rsquo;ll have to approach disagreement with curiosity, build on common ground and create solutions together.
  </li>
  <li>
   Ambition: You&rsquo;ll be surrounded by people who set high standards for themselves and others, who see obstacles as opportunities, and who are relentless in their work to create better outcomes for patients.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  <br/>
  <br/>
  In addition, we offer:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   An attractive and competitive salary, a good pension plan and 25 vacation days per year.
  </li>
  <li>
   Great offsites and team events to strengthen the team and celebrate successes together.
  </li>
  <li>
   A EUR 1000 learning and development budget to help you grow.
  </li>
  <li>
   Autonomy to do your work the way that works best for you, whether you have a kid or prefer early mornings.
  </li>
  <li>
   An annual commuting subsidy.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Our interview process
  <br/>
  <br/>
 </strong>
 Our interview process is designed to assess mutual fit across skills, motivation, and values. It typically includes the following steps:
 <br/>
 <br/>
 <ul>
  <li>
   Screening call: A short conversation to align on your motivation, career goals, and initial fit for the role.
  </li>
  <li>
   Coding assessment &amp; debrief: You'll complete a time-limited coding exercise, started at a time of your choosing. Afterwards, you'll join a live session with members of our team to walk through your solution, explain your reasoning, and discuss any trade-offs you made.
  </li>
  <li>
   Technical interview: A deep dive into your problem-solving approach through a technical challenge, case study, or role-specific scenario.
  </li>
  <li>
   Onsite meeting (optional): You&rsquo;ll meet team members across functions to explore collaboration dynamics, team fit, and day-to-day context.
  </li>
  <li>
   Final executive conversation: A discussion with a member of the executive team focused on long-term alignment, cultural fit, and shared expectations for impact.
  </li>
 </ul>
</div>


ML Platform Engineer

Bloomberg

<div>
 Location
 <br/>
 <br/>
 London
 <br/>
 <br/>
 Business Area
 <br/>
 <br/>
 Engineering and CTO
 <br/>
 <br/>
 Ref #
 <br/>
 <br/>
 10049302
 <br/>
 <br/>
 <strong>
  Description &amp; Requirements
  <br/>
  <br/>
 </strong>
 Bloomberg&rsquo;s Engineering AI department has 400+ AI practitioners building highly sought after products and features that often require novel innovations. We are investing in AI to build better search, discovery, and workflow solutions using technologies such as transformers, gradient boosted decision trees, large language models, and dense vector databases. We are expanding our group and seeking highly skilled individuals who will be responsible for contributing to the team (or teams) of Machine Learning (ML) and Software Engineers that are bringing innovative solutions to AI-driven customer-facing products. At Bloomberg, we believe in fostering a transparent and efficient financial marketplace. Our business is built on technology that makes news, research, financial data, and analytics on over 35 million financial instruments searchable, discoverable, and actionable across the global capital markets.
 <br/>
 <br/>
 Bloomberg has been building Artificial Intelligence applications that offer solutions to these problems with high accuracy and low latency since 2009. We build AI systems to help process and organize the ever-increasing volume of structured and unstructured information needed to make informed decisions.
 <br/>
 <br/>
 Our use of AI uncovers signals, helps us produce analytics about financial instruments in all asset classes, and delivers clarity when our clients need it most.
 <br/>
 <br/>
 We are looking for Senior ML Platform Engineers with strong expertise and passion for building platforms for (Gen) AI applications.
 <br/>
 <br/>
 As a Senior ML Platform Engineer, you will have the opportunity to create a more cohesive, integrated, and managed AI development life cycle to enable the building and maintenance of our AI systems. Our teams make extensive use of open source technologies such as, Kubernetes, Kubeflow, KServe, Argo, Buildpacks, and other cloud-native MLOps technologies. From technical governance to upstream collaboration, we are committed to enhancing the impact and sustainability of open source.
 <br/>
 <br/>
 <strong>
  Join the AI Group as a Senior ML Platform Engineer and you will have the opportunity to:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Architect, build, and diagnose multi-tenant AI platform systems
  </li>
  <li>
   Work closely with AI application teams to design seamless workflows for continuous model training, inference, and monitoring
  </li>
  <li>
   Work with AI experts to understand workflows, pinpoint, and resolve inefficiencies, and to inform the next set of features for the platforms
  </li>
  <li>
   Collaborate with open-source communities and AI application teams to build a cohesive MLOps experience
  </li>
  <li>
   Design CI/CD automation frameworks that incorporate regulatory requirements
  </li>
  <li>
   Develop cloud-native deployment patterns for AI systems across environments
  </li>
  <li>
   Troubleshoot and debug user issues
  </li>
  <li>
   Provide operational and user-facing documentation
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  We are looking for a Senior ML Platform Engineer with:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Proven years of experience working with an object-oriented programming language (Python, Go, etc.)
  </li>
  <li>
   Experience designing cloud-native, distributed platforms
  </li>
  <li>
   Strong knowledge of Kubernetes, Argo, and container orchestration technologies
  </li>
  <li>
   Previous experience with modern CI/CD tools and GitOps workflows
  </li>
  <li>
   Familiarity with implementing automation for model development lifecycles
  </li>
  <li>
   A proactive mentality and ability to collaborate with peers, stakeholders, and management
  </li>
  <li>
   A Degree in Computer Science, Engineering, Mathematics, or similar field of study or equivalent work experience
  </li>
  <li>
   An understanding of Computer Science fundamentals such as data structures and algorithms
   <br/>
   <br/>
  </li>
 </ul>
 We give back to the technology community and you can read more about our outreach at:
 <br/>
 <br/>
 If indicated, please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role.
 <br/>
 <br/>
 Discover what makes Bloomberg unique - watch our for an inside look at our culture, values, and the people behind our success.
</div>


Senior ML Platform Engineer - Artificial Intelligence

EssilorLuxottica

<div><div><b>Requisition I</b><b>D: </b>934441<br>
<b>Store #: </b>A00152 Tech Srvcs Wholesale DAL-T<br>
<b>Position</b>: Full-Time<br>
<b>Total Rewards: </b><b>Benefits/Incentive Information</b></div><br>
<div></div>
<div>
If you&rsquo;ve worn a pair of glasses, we&rsquo;ve already met.</div>
<div>
We are a global leader in the design, manufacture, and distribution of ophthalmic lenses, frames, and sunglasses. We offer our industry stakeholders in over 150 countries access to a global platform of high-quality vision care products such as the Essilor brand, with Varilux, Crizal, Eyezen, Stellest and Transitions, iconic brands that consumers love such as Ray-Ban, Oakley, Persol and Oliver Peoples, as well as a network that offers consumers high-quality vision care and best-in-class shopping experiences such as Sunglass Hut, LensCrafters, and Target Optical, and leading e-commerce platforms.</div>
<div>
Our portfolio of more than 150 renowned brands span various categories, from frames, lenses and instruments to brick and mortar and digital distribution as well as mid-range to premium segments. Our Shared Services Team, accompany and enable others within the EssilorLuxottica collective to achieve their targets. They keep people and projects running smoothly, ensuring every part of our business is provided for and well taken care of.</div>
<div>
Join our global community of over 200,000 dedicated employees around the world in driving the transformation of the eyewear and eyecare industry. Discover more by following us on LinkedIn!</div><div><b>
GENERAL FUNCTION
</b></div><div>The Data &amp; ML Engineer is a self-sufficient engineering professional responsible for designing, building, and operating scalable, secure, and reliable data and machine learning platforms primarily on Azure, with exposure to multi-cloud environments (AWS, GCP) where applicable.</div>
<div>
Expertise in programming languages such as Python or Scala, experience with data processing frameworks like Spark, and familiarity with container orchestration tools such as Kubernetes are essential for this role. Proficiency with CI/CD pipelines, DevOps, and MLOps practices is expected to ensure robust deployment and operationalization of analytics and AI solutions.</div>
<div>
This role complements the Applied Data Scientist by owning the engineering foundations required to operationalize analytics and AI solutions.</div>
<div><b>
MAJOR DUTIES AND RESPONSIBILITIES</b></div>
<div><b>
Managing</b></div>
<div>
Managing the design, development, and operation of large-scale data ingestion, transformation, and storage pipelines<br>
Managing ML infrastructure, CI/CD, DevOps, and MLOps pipelines to support model training and deployment<br>
Managing platform performance, cost optimization, reliability, and availability<br>
Managing data security, governance, and regulatory compliance across platforms<br>
Managing collaboration with Applied Data Scientists to productionize models</div>
<div><b>
Organizing</b></div>
<div>
Designing and organizing ETL/ELT workflows using Azure Data Factory and orchestration tools<br>
Structuring Lakehouse, Azure Data Lake, and Synapse environments for scalable analytics<br>
Organizing data formats, schemas, and versioning (Delta, Parquet, JSON, CSV)<br>
Structuring reusable data pipelines and ML components to accelerate delivery<br>
Organizing monitoring, logging, and alerting for data and ML pipelines</div>
<div><b>
Leading</b></div>
<div>
Leading engineering best practices for scalable data and ML platformsDriving automation-first and infrastructure-as-code approaches<br>
Guiding solution design to ensure performance, resilience, and cost efficiency<br>
Leading troubleshooting and root-cause analysis for data and ML pipeline issues<br>
Mentoring engineers on cloud-native, big data, and MLOps practices</div>
<div><b>
BASIC QUALIFICATION</b></div>
<div>
Bachelor&rsquo;s degree in Computer Science, Engineering, or a related field<br>
Proven experience as a Data Engineer, ML Engineer, or Platform Engineer<br>
Strong hands-on experience with Azure cloud services and big data platforms<br>
Proficiency in Python, SQL, Scala, and scripting languages<br>
Strong experience building production-grade data pipelines<br>
Demonstrated ability to independently own and deliver complex data and ML engineering solutions end-to-end</div>
<div>
This position is not available for visa sponsorship or transfer. Candidates must have authorization to work in the United States</div>
<div><b>
PREFERRED QUALIFICATIONS</b></div>
<div>
Master&rsquo;s degree in Computer Science, Engineering, or related discipline<br>
Experience with Azure Databricks, Spark, Synapse, and MLFlow<br>
Experience with Docker, AKS, APIs, and containerized ML workloads<br>
Experience with orchestration tools such as Azure Data Factory or Airflow<br>
 Exposure to SAP CDC and enterprise data integration<br>
 Experience working in agile, fast-paced, cross-functional environments</div>
<div><b>
TECHNICAL &amp; BEHAVIORAL SKILLS</b></div>
<div>
Strong engineering ownership mindset with minimal supervision<br>
Ability to translate analytical requirements into scalable engineering solutions<br>
Strong collaboration skills with data scientists and business-facing teams<br>
 Excellent problem-solving and troubleshooting capabilities<br>
 Focus on reliability, scalability, and operational excellence</div><div>
This posting is for an existing vacancy within our business. Employee pay is determined by multiple factors, including geography, experience, qualifications, skills and local minimum wage requirements. In addition, you may also be offered a competitive bonus and/or commission plan, which complements a first-class total rewards package. Benefits may include health care, retirement savings, paid time off/vacation, and various employee discounts.</div><br>
<div></div>
<div>
EssilorLuxottica complies with all applicable laws related to the application and hiring process. If you would like to provide feedback regarding an active job posting, or if you are an individual with a disability who would like to request a reasonable accommodation, please call the EssilorLuxottica SpeakUp Hotline at 844-303-0229 (be sure to provide your name, job id number, and contact information so that we may follow up in a timely manner) or email HRCompliance@luxotticaretail.com.</div><br>
<div></div>
<div>
We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, gender, national origin, social origin, social condition, being perceived as a victim of domestic violence, sexual aggression or stalking, religion, age, disability, sexual orientation, gender identity or expression, citizenship, ancestry, veteran or military status, marital status, pregnancy (including unlawful discrimination on the basis of a legally protected pregnancy or maternity leave), genetic information or any other characteristics protected by law. Native Americans in the US receive preference in accordance with Tribal Law.</div></div>

Data & ML Engineer

Worldpay

<div><p><b>Ready to take your career global?</b></p><p>
Make your mark at one of the biggest names in payments.</p><p>
We are seeking an experienced <b>Senior ML Software Engineer</b> 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.</p><p>
In addition to technical expertise, this job requires strong collaboration skills.</p><p></p><p><b>
What you&rsquo;ll own</b></p><p>
Feature Development &amp; Delivery:</p><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><p>
Technical Ownership and Stewardship:</p><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><p>
Contribute to ML platform:</p><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><p>
Operational Excellence:</p><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><p>
Continuous Improvement:</p><ul><li>
Identify and implement opportunities for process, tooling, and system improvements, proactively addressing technical debt and scaling challenges.</li></ul><p>
Release Management:</p><ul><li>
Oversee pre-release testing, coordinate releases and ensure smooth enablement of new features.</li></ul><p>
Leadership:</p><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><p>
Act Like an Owner:</p><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><p></p><p><b>
What you&rsquo;ll bring</b></p><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><p></p><p><b>
It&rsquo;s a bonus if you have</b></p><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><p></p><p><b>
Why Join Us?</b></p><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><p></p><p><b>
About the team</b></p><p>
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.</p><p></p><p><b>
What makes a Globalpayer?</b></p><p>
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.</p><p></p><p>
Does this sound like you? Then you sound like a Globalpayer. Apply now to take your career global.</p><p></p><p>
#LI-AB1</p></div>

Senior ML Software Engineer

Optics11

<div>
 We are expanding our team with a
 <strong>
  Data &amp; ML Infrastructure Engineer
 </strong>
 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.
 <br/>
 <br/>
 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.
 <br/>
 <br/>
 This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.
 <br/>
 <br/>
 Key Responsibilities:
 <br/>
 <br/>
 <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.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Why Join Us?
  <br/>
  <br/>
 </strong>
 <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.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 Required Qualifications:
 <br/>
 <br/>
 <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.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Nice to Have:
  <br/>
  <br/>
 </strong>
 <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.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Security &amp; Compliance:
  <br/>
  <br/>
 </strong>
 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

Roblox

<div>San Mateo, CA, United StatesEarly CareerID: 6400</div>
<br><p></p><div><div><div><p>Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences&ndash; all created by our global community of developers and creators.
</p><p>At Roblox, we&rsquo;re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.<b> </b>We&rsquo;re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.
</p><p>A career at Roblox means you&rsquo;ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.</p></div><p><b>
Team</b></p>
<ul>
<li><b>Creator Services Machine Intelligence Team</b>: The Machine Intelligence team is building an NPC system that can (1) play any Roblox game and (2) perform real-time inference efficiently enough to support deployment to all Roblox players.
</li><li><b>ML Platform Team</b>: The Foundation AI Group is on a mission to establish Roblox as the standard for 3D foundational models (3DFMs), democratizing creation by making it simple for anyone to generate high-quality, immersive 3D experiences using AI. The AI Platform team is a foundational part of this vision, supporting hundreds of ML use cases and billions of inferences daily across Discovery, Safety, Engine, and more. We are seeking exceptional PhD new graduates to drive innovation across three critical areas: <b>AI Platform, Distributed Inference Systems.</b></li>
</ul>
<p><b>What You Will Do</b></p>
<p>
As a Senior Machine Learning Engineer, you will be a key contributor to building the cutting-edge systems that power AI at Roblox.
</p><p><b>Creator Services Machine Intelligence Team</b></p>
<ul>
<li><b>Develop Scale Data Pipelines:</b> Design, build and maintain robust data pipelines to collect complex 3D game states and real-time player actions across the platform.</li>
<li><b>Train Novel Architectures:</b> Solve the feature extraction across games for NPC model in a general and scalable way and drive model training speed for novel, sophisticated deep learning architectures.</li>
<li><b>Optimize Real-Time Inference:</b> Engineer high-performance model inference solutions to support the seamless deployment of 10s to 100s of autonomous NPCs in real-time environments.</li>
</ul>
<p><b>ML Platform Team</b></p>
<p><b>
Track 1: AI Platform Projects</b></p>
<ul>
<li>Pioneer next-generation AI tooling to enhance the efficiency, cost, and usability of ML@Roblox.</li>
<li>Build and maintain core platform components: Serving Layer, Model Registry, Pipeline Orchestrator, and Training/Inference control planes.</li>
<li>Design great developer experiences (paved-road templates, tooling, visualizations) to reduce time-to-production and ensure foundational AI systems are scalable and reliable.</li>
</ul>
<p><b>Track 2: Distributed Inference &amp; Systems Optimization</b></p>
<ul>
<li>Architect and implement scalable distributed inference systems for efficiently serving LLMs and Large Recommender Models at massive scale.</li>
<li>Optimize our inference engine to serve millions of QPS at low latency.</li>
<li>Conduct deep, low-level performance analysis and optimize ML models (using techniques like continuous batching, speculative decoding, and quantization) and systems on GPU architectures to maintain peak performance and stability.</li>
</ul>
<p><b>You Have</b></p>
<ul>
<li>Possessing or pursuing a Ph.D. in Computer Science, Computer Engineering, Mathematics, Statistics, or a related technical field, with a thesis aligned to Roblox&rsquo;s research areas.</li>
<li>Built end-to-end ML pipelines and managed model inference and deployment.</li>
<li>Experience with novel datasets, and building real-world agentic applications.</li>
<li>Scaled high-performance, high-availability architectures.</li>
<li>Handled<b> </b>infrastructure using Kubernetes and major cloud providers (AWS, Azure, or GCP).</li>
</ul><div><div><div><p>
For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on <b>this page</b>.</p></div><div>
Annual Salary Range</div><div>
$196,750&mdash;$243,290 USD</div></div></div><div><p>
Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).</p>
<p>
Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process.</p>
<p>
For US based roles only, please note the Company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H-1B sponsorship at this time.</p></div></div></div>

[2026] Senior Machine Learning Engineer (Systems), Embodied AI/NPCs, ML Platform - PhD Early Career

<div><p>We are expanding our team with a <b>Data &amp; ML Infrastructure Engineer</b> 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.</p><p></p><p>
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.</p><p></p><p>
This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.</p><p></p><h4 class="jobSectionHeader"><b>
Key Responsibilities:</b></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><p></p><h4 class="jobSectionHeader"><b>
Why Join Us?</b></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"><b><br>
Required Qualifications:</b></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><p></p><p><b>
Nice to Have:</b></p><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><p></p><p><b>
Security &amp; Compliance:</b></p><p>
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.</p></div>

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[2026] Senior Machine Learning Engineer (Systems), Embodied AI/NPCs, ML Platform - PhD Early Career

Data & ML Infrastructure Engineer