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data ml infrastructure 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>

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

<div>Company Description 
Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We&rsquo;re optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity. With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes. 
Job Description 
The Data Science team is young, diverse and growing. As we are still relatively small you will be in the driver seat to shape its future. We have a flat structure and value the entrepreneurial spirit of our members: initiative is highly encouraged. We are also striving for well-being, diversity and knowledge-sharing inside the team, so that we all grow.
As a Data engineer consultant you will be working on various client projects as well as on building our own Sia AI solutions, together with our French Data Engineering team:<ul><li>
Cloud Services at GCP, AWS and Azure: architecture choices, use of storage &amp; compute services, cost monitoring and optimization, role management, Infrastructure as Code</li><li>
Docker and Kubernetes: containerization &amp; orchestration of applications, management of K8s clusters adapted to Data Science workloads</li><li>
Python programming: development of server-side tools (bulk data processing, REST API server, authentication, ...)</li><li>
CI/CD: managing the integration and ongoing deployment of our applications and internal platforms, supporting the deployment of Data Science projects at our clients' sites</li><li>
Data pipelines: development of ETL scripts in Big Data environments</li><li>
Infrastructures &amp; Services adapted to Machine Learning: technology watch and implementation of solutions useful to Data Scientists in the learning and provision of their ML models.</li></ul> 

Qualifications 
<ul><li>
 You hold a Master&rsquo;s Degree with a strong quantitative component (e.g. Econometrics, Physics, Mathematics, Statistics, Quantitative Finance)</li><li>
You have 1-3 years of relevant experience in Data, DevOps, Cloud or Software Engineering.</li><li>
You have a good level in Python and have a solid foundation in other languages. You are familiar with at least one SQL DBMS (ideally PostgreSQL) and ideally noSQL (ideally MongoDB).</li><li>
Ideally, you are already familiar with at least one cloud services platform (GCP, AWS, Azure) with experience mixing different services within the same project. If not, you have a strong appetite to learn more about these topics.</li><li>
You are comfortable using Docker, automating CI/CD pipelines and have a strong appetite for gaining competence in Kubernetes if you do not yet have experience with this solution.</li><li>
You will be able to work in a DevOps culture, taking responsibility for the code lifecycle, from development to deployment and maintenance of applications (mastery of the Gitlab-CE ecosystem is a plus). You are also familiar with Infrastructure as Code concepts and tools (ideally Terraform).</li><li>
You are sensitive to security issues and have experience in implementing application protection solutions (certificate management, HTTPS security, OAuth2 authentication, credential management)</li><li>
You have a strong interest in data and AI-related topics, the ability to quickly build expertise in these areas, and you&rsquo;re excited about helping our clients seize the opportunities they offer.</li><li>
You have excellent interpersonal and team working skills</li><li>
Excellent English and Dutch verbal and written skills are required.</li></ul> 

Additional Information 
We are unable to provide a work permit for this position 
Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.</div>

Consultant Data Engineer

<div>We are expanding our Underwater Acoustics R&amp;D team with a Hydrodynamic Acoustic Signature Engineer who is motivated to contribute to our acoustic intelligence capabilities by developing hydrodynamic, acoustic, and mechanical noise models.
Optics11 is a deep-tech company developing advanced fiber-optic sensing technologies for demanding applications in underwater acoustics, offshore energy, and defense. Our novel fiber-optic technology is redefining underwater security by enabling next-generation sonar solutions to protect critical naval assets and underwater infrastructure.
You will bridge first-principles physics and data-driven methods to support the development of classification and identification pipelines by linking acoustic signatures to identifiable target characteristics. Your expertise will contribute to threat spectrum definition and realistic operational scenarios. This is expected to be achieved by the development and maintenance of a deep physics-based understanding and models of underwater acoustic signatures. Working closely with experts from multiple disciplines, you will help develop technologies that operate in some of the world's most challenging environments.<h4 class="jobSectionHeader">
Key Responsibilities:</h4>
Hydrodynamics, Underwater Acoustic Emission and Signature Modelling:<ul><li>
Model hydrodynamic and mechanical noise generation mechanism of targets of interests, e.g., flow noise, cavitation, coupled structural vibration, that result in underwater acoustic noise.</li><li>
Translate physical phenomena into acoustic emission models, i.e., passive and target strength, to be used for system performance modelling.</li><li>
Define parametrized models capturing variability across operating conditions (speed, sea state, depth, configuration).Parameter Space &amp; Uncertainty Definition:</li><li>
Establish realistic parameter brackets for target surface/underwater kinematic parameters, e.g., speed, acceleration, maneuverability, etc., and for their related underwater acoustic signatures. This is based on physical principles and validated through experimental data.</li><li>
Quantify uncertainties and sensitivities to support robust system-level predictions.Data Processing &amp; Machine Learning:</li><li>
Generate synthetic data or semi-synthetic datasets based on physics-informed models.</li><li>
Define meaningful features derived from first principles and the properties of generation of underwater acoustic signatures.</li><li>
Support the development of classification models ensuring that models leveraging physics-based principles, for example making use of hand-crafted features.</li><li>
Support the development of pure data-driven ML/AI models by ensuring that models learn physically plausible patterns.</li><li>
Derive physics-based explanations from data-driven ML/AI models to support stakeholders&rsquo; decisions.</li><li>
Support other data processing pipelines, such as tracking (e.g., providing credible kinematic brackets) and detection (e.g., providing physically grounded inputs for detectability analysis, including array shape effects, effective aperture, and flow-induced performance degradation).</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 degree (or equivalent) with 1-3 years&rsquo; industry experience in applied physics, hydrodynamics, mechanical engineering or a related field.</li><li>
A strong background in hydrodynamic modeling, acoustic modeling, physics-informed modeling, applied physics or equivalent.</li><li>
Strong programming skills (e.g. Python, Julia, C++, etc.) with readiness to deepen your programming proficiency.</li><li>
Strong analytical and problem-solving skills.</li><li>
You have a can-do mentality with no fear of getting your hands dirty when needed.</li><li>
You have strong verbal and written communication skills in English.</li><li>
You can work independently as well as in a team with well-developed communication skills.</li><li>
Experience with fiber optic measurement principles and/or interferometry is a plus.</li><li>
Applicants should be eligible for NATO/Dutch security clearance.</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>

Hydrodynamic Acoustic Signature Engineer

Picnic Technologies

<div>
 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:
 
 
 <ul>
 <li>
 Identify and execute improvement possibilities together with other ML Engineers and (product) analysts
 </li>
 <li>
 Collaborate on your project from inception to production, to continuous improvement
 </li>
 <li>
 Develop tools and enhancements for our cloud-based Machine Learning platform
 
 
 
 </li>
 </ul>
 You could work on various projects and fields, such as:
 
 
 <ul>
 <li>
 Demand forecasting: delivery, article, trip volumes, used Picnic-wide
 </li>
 <li>
 Fraud detection: helping us keep troublemakers out of our store
 </li>
 <li>
 Recommendation systems: generating useful in-app grocery suggestions
 </li>
 <li>
 Search and ranking: helping our customers find exactly what they need
 </li>
 <li>
 Predicting delivery drop-off times: making sure we&rsquo;re on-time at every customer&rsquo;s door
 </li>
 <li>
 Vision: ensuring quality control and smooth operations within our warehouses
 
 
 
 </li>
 </ul>
 Check out some of our previous machine learning projects here: https://blog.picnic.nl/tagged/machine-learning
 
 
 We&rsquo;re looking for
 
 
 <ul>
 <li>
 Master&rsquo;s degree or higher in AI, Computer Science, Mathematics, or a related field
 </li>
 <li>
 5+ years of experience in data science or applied machine learning
 </li>
 <li>
 Excellent communication skills, you can clearly explain complex ideas in non-technical terms and enjoy working closely with business stakeholders to solve problems together
 </li>
 <li>
 Experienced in designing, deploying, and maintaining production ML systems, with solid software engineering and ML principles
 </li>
 <li>
 Strong Python and SQL skills
 </li>
 <li>
 Able to define the long-term vision and technical architecture for ML solutions within your domain
 </li>
 <li>
 Experience with Docker, Kubernetes, and CI is a plus
 
 
 
 </li>
 </ul>
 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.
</div>

Senior Machine Learning Engineer

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
 
 
 
 <ul>
 <li>
 The opportunity to be in a brand new position in a growing team
 </li>
 <li>
 Develop large-scale distributed training pipelines to manage datasets and complex models
 </li>
 <li>
 Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems
 </li>
 <li>
 Develop libraries to improve the performance of machine learning frameworks
 </li>
 <li>
 Maximize performance in training and inference using GPU hardware and acceleration libraries
 </li>
 <li>
 Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions
 </li>
 <li>
 Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining
 </li>
 <li>
 Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs
 </li>
 <li>
 Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities
 </li>
 <li>
 Dig into the internals of open-source ML tools to extend their capabilities and improve performance
 
 
 </li>
 </ul>
 
 Your Skills And Experience
 
 
 
 <ul>
 <li>
 5+ years of experience in machine learning with a focus on training or inference systems
 </li>
 <li>
 Hands-on experience with real-time, low-latency ML pipelines in high-performance environments is a strong plus
 </li>
 <li>
 Strong engineering skills, including Python, CUDA, or C++
 </li>
 <li>
 Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX
 </li>
 <li>
 Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT)
 </li>
 <li>
 Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL)
 </li>
 <li>
 Exposure to cloud platforms and orchestration tools
 </li>
 <li>
 A track record of contributing to open-source projects in machine learning, data science, or distributed systems is a plus
 
 
 </li>
 </ul>
 
 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

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