ai engineer (m/f/d)

full stack engineer

ml ops engineer

engineering manager

forward deployed engineer

ml engineer

Zeta Global

<p></p><div><p><b>WHO WE ARE</b></p>
<p>
Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform &ndash; powered by one of the industry&rsquo;s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.</p><br>
<p></p>
<p><b>
The Role</b></p>
<p>
We&rsquo;re looking for a skilled <b>ML Engineer / Data Scientist</b> with <b>3+ years of software or applied ML experience</b> to design, build, and improve machine learning solutions in a dynamic cloud environment, primarily on <b>AWS</b>.This role sits at the intersection of <b>data science and engineering</b>: exploring data, developing models, running rigorous experiments, and bringing the best approaches into production with a reliable, reproducible workflow. If strong Python skills, curiosity about hard modeling problems, and collaborative work in multicultural teams are a fit, this is a chance to do meaningful, end-to-end ML work&mdash;not just notebooks, and not just infrastructure.</p><br>
<p></p>
<div>
<p><b>Who you are:</b></p>
<ul>
<li>Strong foundation in <b>machine learning, statistics</b> and <b>experiment design</b>.</li>
<li>Experience building models for <b>real business or product problems</b>, not only academic benchmarks.</li>
<li>Comfortable working with <b>structured and unstructured data</b>: feature engineering, dataset construction, labeling quality, leakage checks, and train/validation/test discipline.</li>
<li>Able to compare approaches with clear <b>metrics</b>, error analysis, and sound judgment about tradeoffs (accuracy, latency, cost, maintainability).</li>
<li>Interest in <b>modern ML</b>, including classical ML, deep learning, and <b>LLM / GenAI workflows</b> where relevant (fine-tuning, RAG, evaluation, prompt/versioning).</li>
<li>Proficient in <b>Python</b> and able to write <b>clean, modular, testable code</b>.</li>
<li>Experience developing and deploying ML solutions in a <b>cloud environment</b>, especially AWS.</li>
<li>Comfortable moving from prototype to production: packaging models, building inference paths, monitoring performance, and iterating after launch.</li>
<li>Independent engineer who can own work from <b>problem framing experimentation implementation rollout</b>.</li>
<li>Excellent <b>written and spoken English</b>.</li>
<li>Enjoy working closely with engineers, product partners, and other data scientists.</li>
<li>Clear communicator who can explain methods, results, and limitations to technical and non-technical audiences.</li>
<li><b>Master&rsquo;s degree</b> in Science or Engineering (Computer Science, Mathematics, Physics, Statistics, or similar), <b>or equivalent practical experience</b>.</li>
</ul>
<p><b>Nice to have:</b></p>
<ul>
<li>Experience with <b>scikit-learn, PyTorch, TensorFlow, XGBoost</b>, or similar modeling stacks.</li>
<li>Familiarity with <b>ML experiment tracking</b> and reproducibility (e.g. MLflow, W&amp;B).</li>
<li>Experience with <b>SQL</b>, data warehouses/lakes, and pipeline tools such as <b>Airflow, dbt, or Spark</b>.</li>
<li>Exposure to <b>feature stores</b>, embedding pipelines, or <b>vector search</b> for retrieval-based systems.</li>
<li>Experience building <b>HTTP/gRPC APIs</b> or lightweight services around model inference.</li>
<li>Working knowledge of <b>Docker</b>, basic orchestration, and CI/CD (e.g. <b>GitLab CI</b>).</li>
<li>Experience in <b>agile</b>, <b>remote</b> and <b>async</b> team environments.</li>
<li>Publications, patents, Kaggle/competition results, or open-source ML contributions.</li>
</ul>
<p><b>What you might like about this role:</b></p>
<ul>
<li><b>Hands-on modeling work</b> with room to explore, benchmark, and improve real systems.</li>
<li>Collaboration on <b>ML patent submissions</b> and participation in weekly ML / research paper review meetings.</li>
<li>A <b>multicultural, engineering-focused team</b> with strong peer support.</li>
<li><b>High trust and autonomy</b>&mdash;clear goals, freedom in how to reach them.</li>
<li><b>Internal product impact</b>: meaningful projects that improve developer and user experience, not endless maintenance tickets.</li>
<li><b>Short approval cycles</b> and solid product partnership.</li>
<li>A <b>healthy meeting policy </b>and emphasis on protecting focus time.</li>
<li><b>Flexible hours</b>, remote/home office options, and a calm, engineers-only office when on-site.</li>
<li><b>Competitive compensation</b>, including stock options.</li></ul><br>
<div>
</div><div></div><div><div><p>We&rsquo;re hiring across <b>multiple levels</b>. Title, scope, and compensation depend on experience&mdash;from strong applied ML generalists to senior people who can lead modeling direction and mentor others.</p>
<p>
We&rsquo;re especially interested in candidates who are <b>technically strong, intellectually curious, and motivated by difficult, ambiguous problems</b> where good data science and solid engineering both matter.</p><br>
<p></p>
<p><b>
PEOPLE &amp; CULTURE AT ZETA</b></p>
<p>
Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual&rsquo;s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.
</p><p>We&rsquo;re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here: https://zetaglobal.com/blog/a-look-into-zetas-ergs/</p>
<p><b>
ZETA IN THE NEWS!</b></p>
<p>
https://zetaglobal.com/press/?cat=press-releases</p><br>
<p></p>
<p>
#LI-NP1</p></div></div></div></div>

ML Ops Engineer

<div>
 <p>
  &nbsp;
 </p>
 <p>
  <strong>
   WHO WE ARE&nbsp;
  </strong>
 </p>
 <p>
  Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform &ndash; powered by one of the industry&rsquo;s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.
 </p>
 <p>
  &nbsp;
 </p>
 <p>
  <strong>
   The Role
  </strong>
 </p>
 <p>
  We&rsquo;re looking for a skilled&nbsp;
  <strong>
   ML Engineer / Data Scientist
  </strong>
  &nbsp;with&nbsp;
  <strong>
   3+ years of software or applied ML experience
  </strong>
  &nbsp;to design, build, and improve machine learning solutions in a dynamic cloud environment, primarily on&nbsp;
  <strong>
   AWS
  </strong>
  .This role sits at the intersection of&nbsp;
  <strong>
   data science and engineering
  </strong>
  : exploring data, developing models, running rigorous experiments, and bringing the best approaches into production with a reliable, reproducible workflow. If strong Python skills, curiosity about hard modeling problems, and collaborative work in multicultural teams are a fit, this is a chance to do meaningful, end-to-end ML work&mdash;not just notebooks, and not just infrastructure.
 </p>
 <p>
  &nbsp;
 </p>
 <p>
  <strong>
   Who you are:
  </strong>
 </p>
 <ul>
  <li>
   Strong foundation in&nbsp;
   <strong>
    machine learning,&nbsp;statistics
   </strong>
   and&nbsp;
   <strong>
    experiment design
   </strong>
   .
  </li>
  <li>
   Experience building models for&nbsp;
   <strong>
    real business or product problems
   </strong>
   , not only academic benchmarks.
  </li>
  <li>
   Comfortable working with&nbsp;
   <strong>
    structured and unstructured data
   </strong>
   : feature engineering, dataset construction, labeling quality, leakage checks, and train/validation/test discipline.
  </li>
  <li>
   Able to compare approaches with clear&nbsp;
   <strong>
    metrics
   </strong>
   , error analysis, and sound judgment about tradeoffs (accuracy, latency, cost, maintainability).
  </li>
  <li>
   Interest in&nbsp;
   <strong>
    modern ML
   </strong>
   , including classical ML, deep learning, and&nbsp;
   <strong>
    LLM / GenAI workflows
   </strong>
   &nbsp;where relevant (fine-tuning, RAG, evaluation, prompt/versioning).
  </li>
  <li>
   Proficient in&nbsp;
   <strong>
    Python
   </strong>
   &nbsp;and able to write&nbsp;
   <strong>
    clean, modular, testable&nbsp;code
   </strong>
   .
  </li>
  <li>
   Experience developing and deploying ML solutions in a&nbsp;
   <strong>
    cloud environment
   </strong>
   , especially&nbsp;AWS.
  </li>
  <li>
   Comfortable moving from prototype to production: packaging models, building inference paths, monitoring performance, and iterating after launch.
  </li>
  <li>
   Independent engineer who can own work from&nbsp;
   <strong>
    problem framing &rarr; experimentation &rarr; implementation &rarr; rollout
   </strong>
   .
  </li>
  <li>
   Excellent&nbsp;
   <strong>
    written and spoken English
   </strong>
   .
  </li>
  <li>
   Enjoy working closely with engineers, product partners, and other data scientists.
  </li>
  <li>
   Clear communicator who can explain methods, results, and limitations to technical and non-technical audiences.
  </li>
  <li>
   <strong>
    Master&rsquo;s degree
   </strong>
   &nbsp;in Science or Engineering (Computer Science, Mathematics, Physics, Statistics, or similar),&nbsp;
   <strong>
    or equivalent practical experience
   </strong>
   .
  </li>
 </ul>
 <p>
  <strong>
   Nice to have:
  </strong>
 </p>
 <ul>
  <li>
   Experience with&nbsp;
   <strong>
    scikit-learn,&nbsp;PyTorch,&nbsp;TensorFlow,&nbsp;XGBoost
   </strong>
   , or similar modeling stacks.
  </li>
  <li>
   Familiarity with&nbsp;
   <strong>
    ML experiment tracking
   </strong>
   &nbsp;and reproducibility (e.g. MLflow, W&amp;B).
  </li>
  <li>
   Experience with&nbsp;
   <strong>
    SQL
   </strong>
   , data warehouses/lakes, and pipeline tools such as&nbsp;
   <strong>
    Airflow,&nbsp;dbt, or&nbsp;Spark
   </strong>
   .
  </li>
  <li>
   Exposure to&nbsp;
   <strong>
    feature stores
   </strong>
   , embedding pipelines, or&nbsp;
   <strong>
    vector search
   </strong>
   &nbsp;for retrieval-based systems.
  </li>
  <li>
   Experience building&nbsp;
   <strong>
    HTTP/gRPC APIs
   </strong>
   &nbsp;or lightweight services around model inference.
  </li>
  <li>
   Working knowledge of&nbsp;
   <strong>
    Docker
   </strong>
   , basic orchestration, and CI/CD (e.g.&nbsp;
   <strong>
    GitLab CI
   </strong>
   ).
  </li>
  <li>
   Experience in&nbsp;
   <strong>
    agile
   </strong>
   ,&nbsp;
   <strong>
    remote
   </strong>
   and&nbsp;
   <strong>
    async
   </strong>
   &nbsp;team environments.
  </li>
  <li>
   Publications, patents, Kaggle/competition results, or open-source ML contributions.
  </li>
 </ul>
 <p>
  <strong>
   What you might like about this role:
  </strong>
 </p>
 <ul>
  <li>
   <strong>
    Hands-on modeling work
   </strong>
   &nbsp;with room to explore, benchmark, and improve real systems.
  </li>
  <li>
   Collaboration on&nbsp;
   <strong>
    ML patent submissions
   </strong>
   &nbsp;and participation in&nbsp;weekly ML / research paper review&nbsp;meetings.
  </li>
  <li>
   A&nbsp;
   <strong>
    multicultural, engineering-focused team
   </strong>
   &nbsp;with strong peer support.
  </li>
  <li>
   <strong>
    High trust and autonomy
   </strong>
   &mdash;clear goals, freedom in how to reach them.
  </li>
  <li>
   <strong>
    Internal product impact
   </strong>
   : meaningful projects that improve developer and user experience, not endless maintenance tickets.
  </li>
  <li>
   <strong>
    Short approval cycles
   </strong>
   &nbsp;and solid product partnership.
  </li>
  <li>
   A&nbsp;
   <strong>
    healthy meeting policy&nbsp;
   </strong>
   and emphasis on protecting focus time.
  </li>
  <li>
   <strong>
    Flexible hours
   </strong>
   , remote/home office options, and a calm, engineers-only office when on-site.
  </li>
  <li>
   <strong>
    Competitive compensation
   </strong>
   , including&nbsp;stock options.
  </li>
 </ul>
 &nbsp;
 <p>
  We&rsquo;re hiring across
  <strong>
   multiple levels
  </strong>
  . Title, scope, and compensation depend on experience&mdash;from strong applied ML generalists to senior people who can lead modeling direction and mentor others.
 </p>
 <p>
  We&rsquo;re especially interested in candidates who are&nbsp;
  <strong>
   technically strong, intellectually curious, and motivated by difficult, ambiguous problems
  </strong>
  &nbsp;where good data science and solid engineering both matter.
 </p>
 <p>
  &nbsp;
 </p>
 <p>
  &nbsp;
 </p>
 <p>
  <strong>
   PEOPLE &amp; CULTURE AT ZETA
  </strong>
 </p>
 <p>
  Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual&rsquo;s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.&nbsp;&nbsp;
 </p>
 <p>
  We&rsquo;re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:&nbsp; https://zetaglobal.com/blog/a-look-into-zetas-ergs/&nbsp;
 </p>
 <p>
  <strong>
   ZETA IN THE NEWS!
  </strong>
 </p>
 <p>
  https://zetaglobal.com/press/?cat=press-releases&nbsp;
 </p>
 <p>
  &nbsp;
 </p>
 <p>
 </p>
 <span>
 </span>
</div>


Code Compass 🧭

<div>
 <p>
  🤖 Machine Learning Engineer &ndash; Computer Vision &amp; Generative AI &ndash; 100%
 </p>
 <p>
  <br/>
 </p>
 <p>
  📍 Berlin, Germany | 💰 &euro;50,000 - &euro;70,000 + Benefits | 🌍 Hybrid Working
 </p>
 <p>
  <br/>
 </p>
 <p>
  Our client is a technology-driven organisation developing intelligent software solutions that leverage artificial intelligence, automation, and advanced data processing to solve complex business challenges at scale.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Operating at the intersection of machine learning, software engineering, and product innovation, they are investing heavily in next-generation AI capabilities and expanding their engineering team to support the continued evolution of their platform and services.
 </p>
 <p>
  <br/>
 </p>
 <p>
  This role is ideal for a Machine Learning Engineer who enjoys taking cutting-edge AI solutions from research to production and wants to work on technically challenging projects with tangible real-world impact.
 </p>
 <p>
  <br/>
 </p>
 <p>
  ⚙️ Tech Stack:
 </p>
 <p>
  Python, PyTorch, TensorFlow, Computer Vision, Generative AI, Diffusion Models, GANs, Image Processing, Machine Learning, Docker, Cloud Platforms, Production ML Systems, Automation, AI Workflows
 </p>
 <p>
  <br/>
 </p>
 <p>
  🚀 About the Role:
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   As a Machine Learning Engineer, you will:
  </li>
  <li>
   Develop, train, and optimise machine learning models focused on computer vision and generative AI applications
  </li>
  <li>
   Build and deploy scalable ML solutions into production environments
  </li>
  <li>
   Design automation solutions that reduce manual effort and improve operational efficiency
  </li>
  <li>
   Monitor model performance, identify improvement opportunities, and enhance reliability and accuracy
  </li>
  <li>
   Collaborate closely with engineering, product, and operational teams to deliver practical AI-driven solutions
  </li>
  <li>
   Research emerging technologies and rapidly evaluate their suitability for business applications
  </li>
  <li>
   Contribute to the evolution of production-grade machine learning infrastructure and workflows
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  ➕ Nice to Have:
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Several years of hands-on experience within machine learning and computer vision
  </li>
  <li>
   Strong Python development skills with experience using frameworks such as PyTorch or TensorFlow
  </li>
  <li>
   Practical experience working with generative AI technologies including diffusion models and GANs
  </li>
  <li>
   Understanding of ML deployment, scalability, performance optimisation, and production environments
  </li>
  <li>
   Experience with Docker and cloud-based infrastructure
  </li>
  <li>
   Knowledge of creative AI tools and visual content workflows
  </li>
  <li>
   Experience working with image-based products, digital content platforms, or related industries
  </li>
  <li>
   Strong analytical thinking combined with a pragmatic, solution-oriented mindset
  </li>
  <li>
   Ability to communicate effectively in an international and cross-functional environment
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  💪 Benefits:
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Opportunity to work on cutting-edge AI and machine learning technologies
  </li>
  <li>
   Highly collaborative and innovative working environment
  </li>
  <li>
   Flexible working arrangements with hybrid options
  </li>
  <li>
   Exposure to challenging technical problems with real-world impact
  </li>
  <li>
   Professional development support, training, and continuous learning opportunities
  </li>
  <li>
   Modern working environment with strong team culture and regular social activities
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <em>
   ✉️ Due to the volume of applications, we may not be able to respond to everyone individually. However, we genuinely appreciate every application and will contact shortlisted candidates directly.
  </em>
 </p>
</div>


Machine Learning Engineer

ALMEDIA

<div><p>This isn&rsquo;t your regular job. Almedia is a place where those who want to push harder can accelerate their careers faster than anywhere else. We&rsquo;re aiming to become Germany&rsquo;s second bootstrapped unicorn. Almedia is already Europe&rsquo;s #3 fastest-growing company in 2025 (FT1000).</p><p></p><p>
We are building the future of marketing by rewarding our community of over 70 million users for engaging with our advertisers&rsquo; products. We are offering a new way to acquire users for the biggest companies in the world.</p><p></p><h2 class="jobSectionHeader"><b><br>
Principal Machine Learning Engineer</b><b>
</b></h2><p>You'll take ownership of the hardest ML problems across Almedia's products and growth. This is a hands-on role for someone who identifies what matters most, sets the technical direction, and drives it end to end with minimal oversight.</p><p></p><h2 class="jobSectionHeader"><b>
Types of problems you'll be solving</b></h2><ul><li><p>
Designing and owning the strategy for user reward schemes at scale, across behaviour signals and market dynamics</p></li><li><p>
Architecting real-time personalisation systems for reward values that operate at production scale</p></li><li><p>
Defining how we detect and diagnose underperforming campaigns, and building the systems that act on it</p></li></ul><p></p><h2 class="jobSectionHeader"><b>
Your role</b></h2><ul><li><p>
Own end-to-end delivery of high-impact ML systems: from problem framing through to production and iteration</p></li><li><p>
Set the technical bar for ML across the org, establishing best practices and raising the quality of decisions</p></li><li><p>
Define where ML creates the most business value and align the roadmap accordingly</p></li><li><p>
Apply advanced statistical and causal inference methods with rigour and production-readiness</p></li><li><p>
Partner with product and engineering leadership to shape how technical capability maps to strategic priorities</p></li></ul><p></p><h2 class="jobSectionHeader"><b>
You have</b></h2><ul><li><p>
A strong track record of taking ambiguous, high-impact problems from first principles to production independently</p></li><li><p>
Deep expertise in statistics, causal inference, and experimentation at scale</p></li><li><p>
Mastery of Python and SQL, with extensive hands-on cloud and production systems experience</p></li><li><p>
A history of setting technical direction and raising the bar for engineering teams around you</p></li><li><p>
The communication range to influence both engineers and senior business stakeholders</p></li></ul><p></p><h2 class="jobSectionHeader"><b>
Bonus points for</b></h2><ul><li><p>
Passion for gaming and a strong intuition for player behaviour</p></li><li><p>
Experience in adtech, monetisation platforms, or the gambling industry</p></li><li><p>
Familiarity with gaming KPIs such as pLTV, retention, and ROAS</p></li></ul><p></p><h2 class="jobSectionHeader"><b>
Why Almedia?</b></h2><ul><li><p>
Scale With Almedia: Have a real impact and grow alongside a startup that has been profitable from day one.</p></li><li><p>
High-Growth Environment: We encourage all staff to take ownership of projects and consistently raise the bar.</p></li><li><p>
Do More, Get More: Generous bonus scheme to ensure great, proactive work is valued.</p></li></ul><p></p><p>
We believe in fostering talent, evaluating all skill levels during the hiring process, and providing a clear path for growth. Almedia is an equal opportunity employer. We embrace and celebrate diversity, and encourage individuals from all backgrounds to apply.</p></div>

Principal Machine Learning Engineer

REPLY

<div><div><p>Aufgaben</p><div><ul><li>
Als Teil unseres innovativen und internationalen Teams &uuml;bernimmst du <b>Verantwortung</b> bei der <b>Realisierung innovativer AI- und GenAI-Anwendungen </b>in unterschiedlichsten Branchen</li><li>
Dies umfasst den <b>eigenverantwortlichen Entwurf</b>, die <b>Entwicklung </b>und <b>Integration </b>GenAI-gest&uuml;tzter, Cloud-nativer Anwendungen unter Einsatz von <b>Amazon Web Services, Microservices</b> auf Basis von <b>Serverless Applications mit AWS</b> <b>Lambda </b>und <b>Amazon API Gateway, Containertechnologien</b> (z.B. Docker, K8s, EKS, Fargate) sowie DevOps-Prinzipien / Infrastructure as Code (AWS CDK, Terraform, CloudFormation), mit denen du Projekte in den reibungslosen Betrieb &uuml;berf&uuml;hrst</li><li>
Du <b>konzipierst </b>und <b>implementierst RAG-Architekturen</b> mit Vektordatenbanken (z.B. Amazon OpenSearch Service, Aurora PostgreSQL mit pgvector), entwickelst <b>Agenten</b>-<b>basierte Systeme </b>und <b>Agent</b>-<b>Orchestrierungen </b>mit <b>Amazon Bedrock </b>und modernen<b> Agent-Frameworks</b>, bindest Tools und Datenquellen &uuml;ber das <b>Model Context Protocol (MCP)</b> an und integrierst Foundation Models sicher und kosteneffizient in die Anwendungslandschaften unserer Kunden</li><li>
Du verantwortest die <b>Anforderungsdefinition</b> mit unseren Kunden und unterst&uuml;tzt aktiv bei der <b>Erstellung von Angeboten</b></li><li>
Du arbeitest im Rahmen <b>agiler Projekte</b> und entwickelst dich im engen <b>Austausch </b>mit unseren erfahrenen Engineers kontinuierlich weiter</li></ul></div></div><div><p>
Benefits</p><ul><div><div><div>
SPORTZUSCHUSS</div></div></div><div><div><div>
MODERNE TECHNOLOGIEN</div></div></div><div><div><div>
HYBRIDES ARBEITEN</div></div></div><div><div><div>
COMPANY EVENTS</div></div></div></ul><div><ul><li>
Bei uns kannst du <b>deine Ideen einbringen</b> und die Weiterentwicklung der Firma mitgestalten</li><li>
Wir bieten dir einen<b> modernen Arbeitsplatz </b>mit super Hardware (nach Wahl) und eine internationale und famili&auml;re Arbeitsumgebung mit <b>flachen Hierarchien</b>. Bei uns l&auml;sst sich der Job gut mit der Familie verbinden</li><li>
Bei uns kannst du ein <b>JobRad </b>bekommen, wir beteiligen uns an deinen<b> sportlichen Aktivit&auml;ten </b>und &uuml;ber die Reply Gruppe bekommst du Zugriff auf zahlreiche weitere Benefits</li><li><b>
Team Building</b> ist bei uns ein wichtiger Faktor: Im Jahr finden diverse <b>Events </b>statt</li></ul></div></div><div><p>
Qualifikationen</p><div><ul><li>
Wir freuen uns auf dein <b>erfolgreich abgeschlossenes Hochschulstudium</b> in Informatik, Ingenieurwesen, Mathematik, Physik oder einem vergleichbaren technischem Studiengang mit <b>erster Berufserfahrung (bis zu 3 Jahre)</b> als AI/ML Engineer</li><li><b>
Sehr gute Kenntnisse der AWS</b> &mdash; AWS und KI Zertifizierungen (Associate-Level oder h&ouml;her) sind ein Plus</li><li><b>
Sehr gute Kenntnisse g&auml;ngiger Programmier- und Skriptsprachen</b> (Python, Typescript / JavaScript, Java) und <b>Erfahrung </b>in mehreren Bausteinen des <b>nachfolgenden Tech- und Toolstacks</b>: Node.JS / NPM, Vue.JS, Nuxt.JS, Spring, JUnit, Jest, Mocha, Lodash, Git, Github Actions, Bitbucket Pipelines, AppSync (GraphQL), Amazon API Gateway, AWS Lambda, Infrastructure as Code (AWS CDK, Terraform), sowie SQL und NoSQL Datenbanken (z.B. Amazon RDS, Amazon Aurora, Amazon DynamoDB, Amazon Timestream)</li><li><b>
Praktische Erfahrung mit Generativer KI und LLMs </b>- insbesondere mit GenAI-Architekturen und ihren Bausteinen wie RAG, Agent-Orchestrierung und Model Context Protocol (MCP) -, AWS AI-Services (z.B. Amazon Bedrock, Amazon SageMaker), Agent-Frameworks (z.B. Strands Agents SDK, LangChain / LangGraph, CrewAI, Vercel AI SDK) sowie mit AI-gest&uuml;tzter und agentischer Softwareentwicklung (z.B. Claude Code, Amazon Q Developer, GitHub Copilot)</li><li><b>
Erfahrung im Requirements Engineering</b> und in der &Uuml;bersetzung fachlicher Anforderungen in technische L&ouml;sungen</li><li><b>
Sehr gute</b> <b>Kommunikationsf&auml;higkeit </b>in <b>deutscher </b>und <b>englischer Sprache </b>sowie Bereitschaft zu <b>gelegentlichen Reisen </b>runden dein Profil ab</li></ul></div></div><div><p>
Develop yourself and the digital future &ndash; at Reply!</p><div><p>
Reply ist spezialisiert auf die Konzeption und Implementierung von L&ouml;sungen, die auf neuen Kommunikationskan&auml;len und digitalen Medien basieren. Mit einem Netzwerk hoch spezialisierter Unternehmen definiert und entwickelt Reply Gesch&auml;ftsmodelle, die durch KI, Big Data, Cloud Computing, digitale Medien und dem Internet der Dinge erm&ouml;glicht werden. Reply bietet Beratung, Systemintegration und Digital Services f&uuml;r Unternehmen aus den Bereichen Telekommunikation und Medien, Industrie und Dienstleistungen, Banken und Versicherungen sowie &ouml;ffentliche Verwaltung.</p></div></div></div>

AI ENGINEER — GENAI & AGENTIC SOLUTIONS (M/W/D) AMAZON WEB SERVICES

Moss

<div><p><b>At Moss, we give finance professionals the power to automate their day-to-day and make forward-thinking decisions.</b></p><p><b>
Our team and culture make us unique &mdash; we&rsquo;re driven by impact and growth, where every one of us strives to learn and excel. Recognised by </b><b>Sifted&rsquo;s Rising 100</b><b> and </b><b>LinkedIn's Top Startups</b><b>, we&rsquo;re here to help propel your career and together, make Moss a lasting success.</b></p><p>
We&rsquo;re expanding our Data &amp; AI team and looking for a senior/lead contributor who has hands-on LLM app/agent experience and is fluent in classical ML. You&rsquo;ll own end-to-end LLM/ML initiatives - from problem framing and modeling to evaluation, deployment, and iteration - directly improving core product workflows (invoice understanding, approval matching, reconciliation, anomaly detection, and user-assist).</p><p></p><p><b>
Your responsibilities</b></p><ul><li><p>
Own LLM/ML pipelines for extraction, classification, anomaly detection, and recommendations.</p></li><li><p>
Define evaluation &amp; monitoring: gold sets, automated tests, robustness checks, SLAs, and cost/latency tracking.</p></li><li><p>
Productionize with Platform/Data Eng: bring your models live and iterate/improve until they are adopted by your stakeholders</p></li></ul><p></p><p><b>
About you</b></p><ul><li><p>
5-10+ years in applied ML with production ownership of shipping models/systems.</p></li><li><p>
Hands-on LLM app/agent experience (tool use/function calling, RAG, structured outputs, guardrails) in production.</p></li><li><p>
Strong Python + ML stack (e.g., scikit-learn, XGBoost/LightGBM, PyTorch/TF) and solid experiment/evaluation skills.</p></li></ul><ul><li><p>
Strategic Problem Solver: You approach ambiguous problems by deeply understanding business context and framing data science solutions that drive meaningful outcomes. You rigorously validate assumptions, identify root causes, and prioritize solving the right problem before building.</p></li><li><p>
End-to-End Executor: You independently own projects from ideation to production, balancing model performance with engineering realities. You ensure solutions are scalable, monitored, and maintained with thoughtful evaluation, deployment, and iteration strategies</p></li></ul><p><b>
About Moss</b></p><p>
Moss is a SaaS scale-up founded in Berlin, with a team of 300+ people from 50+ nationalities in 5 offices across Europe.</p><p>
Our ambition is bold: to power every SMB&rsquo;s spend across Europe - fully digital, AI-driven, and seamlessly integrated for complete control. To date, over 5000 businesses in Germany, Netherlands and the UK use Moss&rsquo; leading spend management product, with modules such as corporate cards, accounts payables, employee cash reimbursements and procurement.</p><p>
Moss has raised a total of &euro;180 million in funding and is backed by the most renowned tech investors including Valar Ventures, Tiger Global, Global Founders Capital, Cherry Ventures and A-Star.</p><p>
Be part of a culture that thrives on impact and speed, where you can take bold moves, learn fast and accomplish more. We&rsquo;re a place where you can fast track your career - here's <b>what else to expect:</b></p><ul><li><p>
Top-of-market compensation package, including equity.</p></li><li><p>
Our vibrant offices are at the heart of our culture, where in-person time fuels collaboration and connection over weekly breakfasts and Friday demos.</p></li><li><p>
Additional benefits include: 20 days &ldquo;work from abroad&rdquo;, 600EUR/GBP Learning &amp; Development Budget, and other local benefits.</p></li></ul><p><i>
Unless stated otherwise, benefits apply to full-time positions (interns and working students receive a tailored package).</i></p><p><b><i>
By applying for the above position, you will confirm that you have reviewed and agreed to our </i></b><b><i>Data Privacy Policy</i></b><b><i>.</i></b></p></div>

Senior Data Scientist (f/m/d)

Deutsche Kreditbank AG

<div><div><div><div>Senior Data Scientist - Lending &amp; ML (m/w/d)</div>
</div></div><div><div><div><div>DKB AG</div>
<div>Standort: Berlin</div>
<div>unbefristet</div>
<div>Teilzeit/Vollzeit</div>
<div>ab sofort</div></div><br>
<p></p>
<div><div><div><div><h2 class="jobSectionHeader"><b>
Dein Profil</b></h2>
</div><div><ul><li>Ein abgeschlossenes Studium der Mathematik, Physik, (Wirtschafts-)Informatik oder eines anderen quantitativen Studiengangs</li>
</ul><ul><li>Fundierte Python-Kenntnisse mit mind. 3 Jahren praktischer Erfahrung</li>
</ul><ul><li>Erfahrung mit Cloud-Diensten, idealerweise AWS, sowie in der Bereitstellung von Machine Learning Services im Unternehmenskontext</li>
</ul><ul><li>Tiefes Verst&auml;ndnis statistischer Lernverfahren und moderner Machine-Learning-Algorithmen</li>
</ul><ul><li>Pr&auml;gnante und strukturierte Ausdrucksweise</li>
</ul><ul><li>Sehr gute Deutsch- und Englischkenntnisse</li>
</ul></div></div><div><div><h2 class="jobSectionHeader"><b>Deine Aufgaben</b></h2>
</div><div><ul><li>Entwicklung leistungsf&auml;higer Services zur Optimierung von Kreditprozessen auf Basis von Machine Learning Methoden (z. B. RandomForest, xgboost, lightGBM)</li>
</ul><ul><li>Partnerschaftliche Zusammenarbeit mit Fachkolleg*innen, um Use Cases zu identifizieren und datengetriebene L&ouml;sungen mit direktem Mehrwert f&uuml;rs Kreditgesch&auml;ft umzusetzen</li>
</ul><ul><li>End-to-End-Verantwortung f&uuml;r die Entwicklung ML-basierter Services vom Proof of Concept bis zum produktiven Betrieb</li>
</ul><ul><li>Sicherstellung von Qualit&auml;t, Nachvollziehbarkeit und Robustheit der Modelle (z. B. Monitoring, Testing, Dokumentation)</li>
</ul><ul><li>Du erweiterst deine Kenntnisse regelm&auml;&szlig;ig und setzt die jeweils besten verf&uuml;gbaren Methoden f&uuml;r die Modellentwicklung ein</li>
</ul><ul><li>In einem schnell wachsenden Team kannst du deine Skills ausbauen, Verantwortung &uuml;bernehmen und deine Ideen aktiv einbringen</li></ul></div></div></div></div><br>
<p></p>
<div>
<p><i>Die DKB steht f&uuml;r Vielfalt und Chancengerechtigkeit. Egal, woher du kommst, wie alt du bist, welches Geschlecht, welche Genderidentit&auml;t, sexuelle Orientierung oder Religion du hast, ob mit oder ohne Behinderung - wir freuen uns auf deine Bewerbung!</i></p>
</div>
</div></div><div><div><div>Deine Benefits und mehr</div>
<div></div><div><ul><li><div>Attraktive Verg&uuml;tung<br>
(tariflich oder<br>
au&szlig;ertariflich)</div>
</li><div></div><li><div>Flexibles und mobiles<br>
Arbeiten &ndash; tempor&auml;r auch<br>
im EU-Ausland</div>
</li><div></div><li><div>Du hast die Wahl:
<br>
Benefits Pass, Jobticket<br>
oder Jobrad</div>
</li><div></div><li><div>Moderne Technik-<br>
ausstattung und Tool<br>
Stack</div>
</li><div></div><li><div>Individuelle Qualifi-<br>
zierungsm&ouml;glichkeiten</div>
</li><div></div><li><div>Duz-Kultur und kein<br>
Dresscode</div>
</li><div></div><li><div>Externe<br>
Mitarbeitenden-<br>
beratung</div>
</li><div></div><li><div>Angebote zum<br>
Gesundbleiben</div>
</li><div></div><li><div>Weitere Extras wie z.B.<br>
betriebliche Altersvorsorge<br>
und Versicherungsschutz</div>
</li></ul></div></div></div><div></div><div><div><div><div><div><div>Immer f&uuml;r dich ansprechbar</div>
<div>Oleg Chapaykin<br>
Expertise Lead<br>
oleg.chapaykin@dkb.de<br>
Deutsche Kreditbank AG</div></div></div></div></div></div></div><p></p>

Senior Data Scientist - Lending & ML (m/w/d)

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AI ENGINEER — GENAI & AGENTIC SOLUTIONS (M/W/D) AMAZON WEB SERVICES

Senior Data Scientist (f/m/d)

Senior Data Scientist - Lending & ML (m/w/d)