ml engineer

Cloudbeds

<div>
 
 
 
 
 What Makes Us Unique&nbsp;
 
 
 
 
 
 
 At Cloudbeds, we're not just building software, we&rsquo;re transforming hospitality. Our intelligently designed platform powers properties across 150 countries, processing billions in bookings annually. From independent properties to hotel groups, we help hoteliers transform operations and uplevel their commercial strategy through a unified platform that integrates with hundreds of partners. And we do it with a completely remote team. Imagine working alongside global innovators to build AI-powered solutions that solve hoteliers' biggest challenges. Since our founding in 2012, we've become the World's Best Hotel PMS Solutions Provider and landed on Deloitte's Technology Fast 500 again in 2024 &ndash; but we're just getting started.&nbsp;
 
 
 
 Location - Remote (Europe)
 
 
 
 
 
 
 How You'll Make an Impact:
 
 
 
 
 
 As a
 
 Staff Machine Learning Engineer
 
 
 
 , you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. Some of these features will use simple heuristic data, while others will leverage advanced machine learning techniques to optimize revenue strategies.
 
 
 You&rsquo;ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You&rsquo;ll be instrumental in establishing robust ML practices and rigorous testing processes across the entire ML lifecycle. From structuring data pipelines to implementing and validating ML models, you&rsquo;ll own the end-to-end development of our revenue management application&mdash;ensuring hotels have the reliable, accurate insights they need to maximize their success.
 
 
 
 Our Machine Learning Team:
 
 
 
 
 
 Our machine learning team is energized by the unique challenge of revolutionizing guest experiences through AI-driven insights, transforming traditional hospitality with cutting-edge predictive algorithms.&nbsp;
 
 
 We thrive on collaborative innovation, where data scientists, engineers, and product experts seamlessly blend their expertise to prototype bold ideas and directly impact operational efficiency.&nbsp;
 
 
 People who are passionate about continuous learning, unafraid to challenge conventions, and excited by the intersection of hospitality and deep technical prowess will find their home among our forward-thinking team.
 
 
 
 What You Bring to the Team:
 
 
 
 
 <ul>
 <li>
 Architectural Expertise: Proven track record in designing, deploying, and maintaining production-grade, distributed ML systems (Sagemaker)
 </li>
 <li>
 Deep MLOps Proficiency: Expert-level knowledge of CI/CD, orchestration (e.g., Apache Airflow, Flink), and model monitoring/drift detection at scale.
 </li>
 <li>
 Software Engineering Rigor: Strong background in Python, distributed systems, and backend development, with a firm grasp of software engineering best practices.
 </li>
 <li>
 Technical Strategy: Experience defining SLIs/SLOs and managing large-scale technical roadmaps.
 </li>
 <li>
 Leadership: Demonstrated ability to influence cross-functional teams, mentor junior talent, and drive consensus on complex technical decisions.
 </li>
 <li>
 Domain Knowledge: Ability to apply statistical and ML methods to optimize revenue management and pricing strategies.
 </li>
 </ul>
 
 
 What Sets You Up for Success:
 
 
 
 
 <ul>
 <li>
 5+ years of experience in a machine learning role, with demonstrated success in ML Engineering and deploying models to production.
 </li>
 <li>
 Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
 </li>
 <li>
 Great understanding of machine learning principles (experimental design, statistical distributions and test, machine learning algorithms)
 </li>
 <li>
 Expertise in deploying ML models at scale on AWS, with experience using MLFlow, Sagemaker or similar platforms.
 </li>
 <li>
 Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews, using Docker, Terform, Kubernetes).
 </li>
 <li>
 Expert-level SQL skills and experience working with large datasets for analysis and modeling.
 </li>
 <li>
 Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
 </li>
 <li>
 Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.
 </li>
 <li>
 Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
 </li>
 </ul>
 
 
 Bonus Skills to Stand Out (Optional):
 
 
 
 
 <ul>
 <li>
 Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
 </li>
 <li>
 Experience with data quality monitoring tools and frameworks.
 </li>
 <li>
 Master&rsquo;s or PhD in Computer Science, Mathematics, or a related field.&nbsp;
 </li>
 </ul>
 
 
 
 
 
 What to Expect - Your Journey with Us&nbsp;
 
 
 
 
 
 Behind Cloudbeds' revolutionary technology is a team of redefining what's possible in hospitality. We're 650+ employees across 40+ countries, bringing together elite engineers, AI architects, world-class designers, and hospitality veterans to solve challenges others haven't dared to tackle. Our diverse team speaks 30+ languages, but we all share one language: a passion for innovation and travel. From pioneering breakthroughs in machine learning to revolutionizing how hotels operate, we're not just watching the future of hospitality unfold &ndash; we're coding it, designing it, writing it and shipping it. If you're ready to work alongside some of the brightest minds in tech who are obsessed with using AI to transform a trillion-dollar industry, this is your chance to be part of something extraordinary.
 
 
 Learn more online at
 
 cloudbeds.com
 
 
 
 
 
 Company Awards to Check Out!&nbsp;
 
 
 
 
 <ul>
 <li>
 Best All-In-One Hotel Management System | HotelTechAwards (2025)
 </li>
 <li>
 Overall 10 Best Places to Work | HotelTechAwards (2025)
 </li>
 <li>
 Most Loved Workplace&reg; Certified (2024)&nbsp;
 </li>
 <li>
 Top 10 People&rsquo;s Choice(2024)
 </li>
 <li>
 Deloitte Technology Fast 500 (2024)
 </li>
 </ul>
 &nbsp;
 
 
 
 Discover our Benefits:
 
 
 
 
 <ul>
 <li>
 Remote First, Remote Always&nbsp;
 </li>
 <li>
 PTO in accordance with local labor requirements
 </li>
 <li>
 Monthly Wellness Fridays - enjoy an extra long weekend every month
 </li>
 <li>
 Full Paid Parental Leave
 </li>
 <li>
 Home office stipend based on country of residency
 </li>
 <li>
 Professional development courses in Cloudbeds University
 </li>
 <li>
 Access to professional development, including manager training, upskilling and knowledge transfer.
 </li>
 </ul>
 
 
 
 Everyone is Welcome - A Culture of Inclusion&nbsp;&nbsp;
 
 
 
 
 
 
 Cloudbeds is proud to be an Equal Opportunity Employer that celebrates the diversity in our global team! We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
 
 
 
 
 Cloudbeds is committed to the full inclusion of all qualified individuals. As part of this commitment, Cloudbeds will ensure that persons with disabilities are provided reasonable accommodations in the hiring process. We encourage deaf, hard of hearing, deaf-blind, and deaf-disabled individuals to apply. If reasonable accommodation is needed to participate in the job application or interview process or to perform essential job functions, please contact our HR team by phone at (858) 201-7832 or via email at
 
 accommodations@cloudbeds.com
 
 . Cloudbeds will provide an American Sign Language (ASL) interpreter where needed as a reasonable accommodation for the hiring processes.
 
 
 
 
 To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Cloudbeds. Staffing, recruiting agencies, and individuals being represented by an agency are not authorized to use this site or to submit applications, and any such submissions will be considered unsolicited. Cloudbeds does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Cloudbeds employees, or any other company location. Cloudbeds is not responsible for any fees related to unsolicited resumes/applications.
 
 
 
 
</div>

Staff ML Engineer

JetBrains

<div>
 Software engineers and AI agents alike suffer from the same problem: finding that one person or place that will answer their tough, specific question. Many solutions promise to solve this with similarity search in vector databases. Unfortunately, finding the answer is often a puzzle with pieces to be collected across a myriad of contradictory sources and cannot be solved without surgical search and careful reasoning.
 
 
 Spectrum collects data from an organization&rsquo;s code, docs, and issues, and organizes knowledge in a unified ontology that AI agents can efficiently search through and reason over. We aim to revolutionize the semantic layer space for software-building organizations and move beyond specs that fall out of sync with code, introducing a living spec &ndash; one that&rsquo;s extracted from the whole system and used to keep it aligned. Spectrum is meant to be the single source of truth for all product and architectural knowledge.
 
 
 Spectrum is a resident of JetBrains' startup incubator, with startup speed and autonomy, and backed by 25 years of developer tooling expertise. We are looking for a top-class ML Engineer who will help us shape the future of software development. You will own our AI and ML engineering stack and help define the research agenda for our team. Your technical vision and design decisions will directly shape the product and determine its success.
 
 
 
 Your Responsibilities Will Include
 
 
 
 <ul>
 <li>
 Designing and building the ML/LLM solution for data ingestion, knowledge extraction, retrieval, and subsequent reasoning.
 </li>
 <li>
 Creating the datasets, metrics, and pipelines that drive measurable improvements across the system.
 </li>
 <li>
 Architecting and improving agents for context retrieval, knowledge extraction, and data alignment, which includes prompt engineering, model selection, and inference optimization.
 </li>
 <li>
 Establishing MLOps practices, including orchestration, observability, and experiment tracking.
 </li>
 <li>
 Collaborating with the engineering team on system design and with JetBrains Research on the research agenda.
 </li>
 <li>
 Defining hiring criteria, growing the ML team, and shaping the ML team culture.
 
 
 </li>
 </ul>
 
 We Expect You To Have
 
 
 
 <ul>
 <li>
 A proven track record as an ML/AI Lead.
 </li>
 <li>
 At least five years of experience in ML/AI systems, with at least two years focused on LLMs and generative AI.
 </li>
 <li>
 A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches.
 </li>
 <li>
 Hands-on experience with:
 </li>
 <ul>
 <li>
 Prompt engineering and LLM pipeline design, including evaluation.
 </li>
 <li>
 Agentic frameworks such as LangChain, LlamaIndex, LangSmith, smolagents, or an equivalent.
 </li>
 <li>
 Vector databases and retrieval-augmented generation (RAG) patterns.
 </li>
 <li>
 Deploying and scaling LLM-powered applications using APIs (e.g. OpenAI or Anthropic) or open-source models.
 </li>
 </ul>
 <li>
 Strong Python skills &ndash; Kotlin knowledge would be a plus.
 </li>
 <li>
 Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
 </li>
 <li>
 Proficiency in English, both written and verbal.
 
 </li>
 </ul>
 
 Our Ideal Candidate Would Have
 
 
 
 <ul>
 <li>
 Experience with ontologies, knowledge graphs, or graph-based reasoning.
 </li>
 <li>
 Experience in early-stage startups &ndash; you enjoy the zero-to-one phase.
 </li>
 <li>
 The ability to think strategically about product-led AI, beyond just training models in isolation.
 </li>
 <li>
 A background in code analysis, developer tools, or software engineering research.
 </li>
 <li>
 Experience with multi-agent systems or complex agentic workflows.
 </li>
 <li>
 Actively contributed to relevant open-source projects or publications.
 
 
 </li>
 </ul>
 What we offer
 
 
 <ul>
 <li>
 A competitive salary and JetBrains benefits.
 </li>
 <li>
 A generous runway and corporate resources with startup autonomy.
 
 
 </li>
 </ul>
 
 We are an equal opportunity employer
 
 
 
 We know great ideas can come from anyone, anywhere. That&rsquo;s why we do our best to create an open and inclusive workplace &ndash; one that welcomes everyone regardless of their background, identity, religion, age, accessibility needs, or orientation.
 
 
 
 We process the data provided in your job application in accordance with the Recruitment Privacy Policy.
 
 
 
</div>

Founding ML Engineer (Spectrum)

kaiko.ai

<div>
 
 About Kaiko.ai
 
 
 
 Kaiko is building a next-generation agentic clinical AI assistant that helps clinicians reason across patient data, guidelines, and diagnostics.
 
 
 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.
 
 
 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.
 
 
 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.
 
 
 
 About The Role
 
 
 
 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.
 
 
 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.
 
 
 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.
 
 
 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.
 
 
 You will be based in either The Netherlands or Switzerland, with the expectation of spending at least 50% of your time at the office.
 
 
 
 Some areas of responsibility
 
 
 
 <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.
 
 
 </li>
 </ul>
 
 About You
 
 
 
 <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.
 
 
 </li>
 </ul>
 
 Nice to have:
 
 
 
 <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.
 
 
 </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!
 
 
 
 Why kaiko
 
 
 
 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:
 
 
 <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.
 
 
 </li>
 </ul>
 
 
 
 In addition, we offer:
 
 
 
 <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.
 
 
 </li>
 </ul>
 
 Our interview process
 
 
 
 Our interview process is designed to assess mutual fit across skills, motivation, and values. It typically includes the following steps:
 
 
 <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

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.<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>

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.
 
 
 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>

IMC Trading

<div>
 As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and deployment of large-scale ML models across our global operations. You'll collaborate with leading researchers, hardware experts, and software engineers to build robust solutions that maximize the potential of GPU acceleration, distributed computing, and the latest open-source tools. Your work will influence our trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement.
 
 
 This is a unique opportunity to solve problems at the intersection of advanced machine learning and trading, where your contributions will shape the future of IMC&rsquo;s technology and trading capabilities.
 
 
 
 Your Core Responsibilities
 
 
 
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 The opportunity to be in a brand new position in a growing team
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 Develop large-scale distributed training pipelines to manage datasets and complex models
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 Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems
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 Develop libraries to improve the performance of machine learning frameworks
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 Maximize performance in training and inference using GPU hardware and acceleration libraries
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 Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions
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 Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining
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 Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs
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 Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities
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 Dig into the internals of open-source ML tools to extend their capabilities and improve performance
 
 
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 Your Skills And Experience
 
 
 
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 5+ years of experience in machine learning with a focus on training or inference systems
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 Hands-on experience with real-time, low-latency ML pipelines in high-performance environments is a strong plus
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 Strong engineering skills, including Python, CUDA, or C++
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 Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX
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 Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT)
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 Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL)
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 Exposure to cloud platforms and orchestration tools
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 A track record of contributing to open-source projects in machine learning, data science, or distributed systems is a plus
 
 
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 About Us
 
 
 
 IMC is a global trading firm powered by a cutting-edge research environment and a world-class technology backbone. Since 1989, we&rsquo;ve been a stabilizing force in financial markets, providing essential liquidity upon which market participants depend. Across our offices in the US, Europe, Asia Pacific, and India, our talented quant researchers, engineers, traders, and business operations professionals are united by our uniquely collaborative, high-performance culture, and our commitment to giving back. From entering dynamic new markets to embracing disruptive technologies, and from developing an innovative research environment to diversifying our trading strategies, we dare to continuously innovate and collaborate to succeed.
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Machine Learning Engineer

Picnic Technologies

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 In a nutshell
 
 
 As a Machine Learning Engineer at Picnic, you will design and deploy intelligent systems to solve our company&rsquo;s biggest challenges. Beyond building models, you will productionize them with strong monitoring and seamless integration into operational systems. You will collaborate closely with stakeholders to ensure the right problems are solved and that solutions deliver real-world impact.
 
 
 All using high quality data. Our data is registered, logged, and consistent, enabling you to focus on building and improving models rather than cleaning data. This role combines the skills of a consultant, software engineer, and applied mathematician.
 
 
 Tricks of the trade
 
 
 You will definitely:
 
 
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 Identify and execute improvement possibilities together with other ML Engineers and (product) analysts
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 Collaborate on your project from inception to production, to continuous improvement
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 Develop tools and enhancements for our cloud-based Machine Learning platform
 
 
 
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 You could work on various projects and fields, such as:
 
 
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 Demand forecasting: delivery, article, trip volumes, used Picnic-wide
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 Fraud detection: helping us keep troublemakers out of our store
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 Recommendation systems: generating useful in-app grocery suggestions
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 Search and ranking: helping our customers find exactly what they need
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 Predicting delivery drop-off times: making sure we&rsquo;re on-time at every customer&rsquo;s door
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 Vision: ensuring quality control and smooth operations within our warehouses
 
 
 
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 Check out some of our previous machine learning projects here: https://blog.picnic.nl/tagged/machine-learning
 
 
 We&rsquo;re looking for
 
 
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 Master&rsquo;s degree or higher in AI, Computer Science, Mathematics, or a related field
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 5+ years of experience in data science or applied machine learning
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 Excellent communication skills, you can clearly explain complex ideas in non-technical terms and enjoy working closely with business stakeholders to solve problems together
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 Experienced in designing, deploying, and maintaining production ML systems, with solid software engineering and ML principles
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 Strong Python and SQL skills
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 Able to define the long-term vision and technical architecture for ML solutions within your domain
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 Experience with Docker, Kubernetes, and CI is a plus
 
 
 
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 Picnic Perks
 
 
 
 🫱🏼&zwj;🫲🏾 Teamwork makes the dream work
 
 
 
 With more than 80 nationalities across 3 countries, you&rsquo;ll be part of a diverse company with plenty of cool stuff to get involved with, from board game evenings to after-work drinks to our company ski trip and more!
 
 
 
 💡 Make a difference
 
 
 
 You&rsquo;ll work in an awesome startup environment with the freedom to drive your own projects and create a visible impact. Our fully electric vehicles and sustainable business model mean you&rsquo;ll also be contributing to making the world a better place!
 
 
 
 🥗 Fresh Lunch, coffee, and snacks
 
 
 
 Our offices are equipped with fully-fledged coffee bars and a kitchen and chefs. They prepare delicious fresh and warm lunches every day so you can keep your energy up.
 
 
 
 🚲 Health insurance discount &amp; bike plan
 
 
 
 We have a partnership with CZ (a health insurance provider). Picnic employees get a discount on CZ insurance packages between 5% and 15%. Furthermore, through our partnership with Lease a Bike, you can rent-to-own a new (e)bike at a discounted rate
 
 
 
 🌎 Relocation
 
 
 
 If you&rsquo;re moving from another country to join Picnic we make it as smooth as possible for you. We&rsquo;ll cover your flight costs for you and your partner and kids, your first month's rent and moving costs (up to &euro;2000), and help you with the 30% tax ruling setup and application.
 
 
 
 📆 All the rest
 
 
 
 At Picnic you get 25 holidays, we cover your travel expenses and offer a pension plan. And your phone and laptop are on us, as well.
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Senior Machine Learning Engineer

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