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devops engineer (all genders)

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Zeta Global

<div>WHO WE ARE

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. 


The Role

We&rsquo;re looking for a skilled ML Engineer / Data Scientist with 3+ years of software or applied ML experience to design, build, and improve machine learning solutions in a dynamic cloud environment, primarily on AWS.This role sits at the intersection of data science and engineering: 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. 

<div>
Who you are:
<ul>
<li>Strong foundation in machine learning, statistics and experiment design.</li>
<li>Experience building models for real business or product problems, not only academic benchmarks.</li>
<li>Comfortable working with structured and unstructured data: feature engineering, dataset construction, labeling quality, leakage checks, and train/validation/test discipline.</li>
<li>Able to compare approaches with clear metrics, error analysis, and sound judgment about tradeoffs (accuracy, latency, cost, maintainability).</li>
<li>Interest in modern ML, including classical ML, deep learning, and LLM / GenAI workflows where relevant (fine-tuning, RAG, evaluation, prompt/versioning).</li>
<li>Proficient in Python and able to write clean, modular, testable code.</li>
<li>Experience developing and deploying ML solutions in a cloud environment, 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 problem framing experimentation implementation rollout.</li>
<li>Excellent written and spoken English.</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>Master&rsquo;s degree in Science or Engineering (Computer Science, Mathematics, Physics, Statistics, or similar), or equivalent practical experience.</li>
</ul>
Nice to have:
<ul>
<li>Experience with scikit-learn, PyTorch, TensorFlow, XGBoost, or similar modeling stacks.</li>
<li>Familiarity with ML experiment tracking and reproducibility (e.g. MLflow, W&amp;B).</li>
<li>Experience with SQL, data warehouses/lakes, and pipeline tools such as Airflow, dbt, or Spark.</li>
<li>Exposure to feature stores, embedding pipelines, or vector search for retrieval-based systems.</li>
<li>Experience building HTTP/gRPC APIs or lightweight services around model inference.</li>
<li>Working knowledge of Docker, basic orchestration, and CI/CD (e.g. GitLab CI).</li>
<li>Experience in agile, remote and async team environments.</li>
<li>Publications, patents, Kaggle/competition results, or open-source ML contributions.</li>
</ul>
What you might like about this role:
<ul>
<li>Hands-on modeling work with room to explore, benchmark, and improve real systems.</li>
<li>Collaboration on ML patent submissions and participation in weekly ML / research paper review meetings.</li>
<li>A multicultural, engineering-focused team with strong peer support.</li>
<li>High trust and autonomy&mdash;clear goals, freedom in how to reach them.</li>
<li>Internal product impact: meaningful projects that improve developer and user experience, not endless maintenance tickets.</li>
<li>Short approval cycles and solid product partnership.</li>
<li>A healthy meeting policy and emphasis on protecting focus time.</li>
<li>Flexible hours, remote/home office options, and a calm, engineers-only office when on-site.</li>
<li>Competitive compensation, including stock options.</li></ul> 
<div>
</div><div></div><div><div>We&rsquo;re hiring across multiple levels. Title, scope, and compensation depend on experience&mdash;from strong applied ML generalists to senior people who can lead modeling direction and mentor others.

We&rsquo;re especially interested in candidates who are technically strong, intellectually curious, and motivated by difficult, ambiguous problems where good data science and solid engineering both matter. 


PEOPLE &amp; CULTURE AT ZETA

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.
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/

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 


#LI-NP1</div></div></div></div>

ML Ops Engineer

<div>
 
 &nbsp;
 
 
 
 WHO WE ARE&nbsp;
 
 
 
 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.
 
 
 &nbsp;
 
 
 
 The Role
 
 
 
 We&rsquo;re looking for a skilled&nbsp;
 
 ML Engineer / Data Scientist
 
 &nbsp;with&nbsp;
 
 3+ years of software or applied ML experience
 
 &nbsp;to design, build, and improve machine learning solutions in a dynamic cloud environment, primarily on&nbsp;
 
 AWS
 
 .This role sits at the intersection of&nbsp;
 
 data science and engineering
 
 : 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.
 
 
 &nbsp;
 
 
 
 Who you are:
 
 
 <ul>
 <li>
 Strong foundation in&nbsp;
 
 machine learning,&nbsp;statistics
 
 and&nbsp;
 
 experiment design
 
 .
 </li>
 <li>
 Experience building models for&nbsp;
 
 real business or product problems
 
 , not only academic benchmarks.
 </li>
 <li>
 Comfortable working with&nbsp;
 
 structured and unstructured data
 
 : feature engineering, dataset construction, labeling quality, leakage checks, and train/validation/test discipline.
 </li>
 <li>
 Able to compare approaches with clear&nbsp;
 
 metrics
 
 , error analysis, and sound judgment about tradeoffs (accuracy, latency, cost, maintainability).
 </li>
 <li>
 Interest in&nbsp;
 
 modern ML
 
 , including classical ML, deep learning, and&nbsp;
 
 LLM / GenAI workflows
 
 &nbsp;where relevant (fine-tuning, RAG, evaluation, prompt/versioning).
 </li>
 <li>
 Proficient in&nbsp;
 
 Python
 
 &nbsp;and able to write&nbsp;
 
 clean, modular, testable&nbsp;code
 
 .
 </li>
 <li>
 Experience developing and deploying ML solutions in a&nbsp;
 
 cloud environment
 
 , 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;
 
 problem framing &rarr; experimentation &rarr; implementation &rarr; rollout
 
 .
 </li>
 <li>
 Excellent&nbsp;
 
 written and spoken English
 
 .
 </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>
 
 Master&rsquo;s degree
 
 &nbsp;in Science or Engineering (Computer Science, Mathematics, Physics, Statistics, or similar),&nbsp;
 
 or equivalent practical experience
 
 .
 </li>
 </ul>
 
 
 Nice to have:
 
 
 <ul>
 <li>
 Experience with&nbsp;
 
 scikit-learn,&nbsp;PyTorch,&nbsp;TensorFlow,&nbsp;XGBoost
 
 , or similar modeling stacks.
 </li>
 <li>
 Familiarity with&nbsp;
 
 ML experiment tracking
 
 &nbsp;and reproducibility (e.g. MLflow, W&amp;B).
 </li>
 <li>
 Experience with&nbsp;
 
 SQL
 
 , data warehouses/lakes, and pipeline tools such as&nbsp;
 
 Airflow,&nbsp;dbt, or&nbsp;Spark
 
 .
 </li>
 <li>
 Exposure to&nbsp;
 
 feature stores
 
 , embedding pipelines, or&nbsp;
 
 vector search
 
 &nbsp;for retrieval-based systems.
 </li>
 <li>
 Experience building&nbsp;
 
 HTTP/gRPC APIs
 
 &nbsp;or lightweight services around model inference.
 </li>
 <li>
 Working knowledge of&nbsp;
 
 Docker
 
 , basic orchestration, and CI/CD (e.g.&nbsp;
 
 GitLab CI
 
 ).
 </li>
 <li>
 Experience in&nbsp;
 
 agile
 
 ,&nbsp;
 
 remote
 
 and&nbsp;
 
 async
 
 &nbsp;team environments.
 </li>
 <li>
 Publications, patents, Kaggle/competition results, or open-source ML contributions.
 </li>
 </ul>
 
 
 What you might like about this role:
 
 
 <ul>
 <li>
 
 Hands-on modeling work
 
 &nbsp;with room to explore, benchmark, and improve real systems.
 </li>
 <li>
 Collaboration on&nbsp;
 
 ML patent submissions
 
 &nbsp;and participation in&nbsp;weekly ML / research paper review&nbsp;meetings.
 </li>
 <li>
 A&nbsp;
 
 multicultural, engineering-focused team
 
 &nbsp;with strong peer support.
 </li>
 <li>
 
 High trust and autonomy
 
 &mdash;clear goals, freedom in how to reach them.
 </li>
 <li>
 
 Internal product impact
 
 : meaningful projects that improve developer and user experience, not endless maintenance tickets.
 </li>
 <li>
 
 Short approval cycles
 
 &nbsp;and solid product partnership.
 </li>
 <li>
 A&nbsp;
 
 healthy meeting policy&nbsp;
 
 and emphasis on protecting focus time.
 </li>
 <li>
 
 Flexible hours
 
 , remote/home office options, and a calm, engineers-only office when on-site.
 </li>
 <li>
 
 Competitive compensation
 
 , including&nbsp;stock options.
 </li>
 </ul>
 &nbsp;
 
 We&rsquo;re hiring across
 
 multiple levels
 
 . Title, scope, and compensation depend on experience&mdash;from strong applied ML generalists to senior people who can lead modeling direction and mentor others.
 
 
 We&rsquo;re especially interested in candidates who are&nbsp;
 
 technically strong, intellectually curious, and motivated by difficult, ambiguous problems
 
 &nbsp;where good data science and solid engineering both matter.
 
 
 &nbsp;
 
 
 &nbsp;
 
 
 
 PEOPLE &amp; CULTURE AT ZETA
 
 
 
 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;
 
 
 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;
 
 
 
 ZETA IN THE NEWS!
 
 
 
 https://zetaglobal.com/press/?cat=press-releases&nbsp;
 
 
 &nbsp;
 
 
 
 
 
</div>

Moss

<div>At Moss, we give finance professionals the power to automate their day-to-day and make forward-thinking decisions.
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 Sifted&rsquo;s Rising 100 and LinkedIn's Top Startups, we&rsquo;re here to help propel your career and together, make Moss a lasting success.
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).
Your responsibilities<ul><li>
Own LLM/ML pipelines for extraction, classification, anomaly detection, and recommendations.</li><li>
Define evaluation &amp; monitoring: gold sets, automated tests, robustness checks, SLAs, and cost/latency tracking.</li><li>
Productionize with Platform/Data Eng: bring your models live and iterate/improve until they are adopted by your stakeholders</li></ul>
About you<ul><li>
5-10+ years in applied ML with production ownership of shipping models/systems.</li><li>
Hands-on LLM app/agent experience (tool use/function calling, RAG, structured outputs, guardrails) in production.</li><li>
Strong Python + ML stack (e.g., scikit-learn, XGBoost/LightGBM, PyTorch/TF) and solid experiment/evaluation skills.</li></ul><ul><li>
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.</li><li>
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</li></ul>
About Moss
Moss is a SaaS scale-up founded in Berlin, with a team of 300+ people from 50+ nationalities in 5 offices across Europe.
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.
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.
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 what else to expect:<ul><li>
Top-of-market compensation package, including equity.</li><li>
Our vibrant offices are at the heart of our culture, where in-person time fuels collaboration and connection over weekly breakfasts and Friday demos.</li><li>
Additional benefits include: 20 days &ldquo;work from abroad&rdquo;, 600EUR/GBP Learning &amp; Development Budget, and other local benefits.</li></ul>
Unless stated otherwise, benefits apply to full-time positions (interns and working students receive a tailored package).
By applying for the above position, you will confirm that you have reviewed and agreed to our Data Privacy Policy.</div>

Senior Data Scientist (f/m/d)

DATATRONiQ

<div>
 
 
 DATATRONiQ
 
 ist ein Deep-Tech-Startup aus Deutschland f&uuml;r Industrial IoT und Edge AI. Als Cloud &amp; DevOps Engineer verantwortest Du unsere Kubernetes-Landschaft &ndash; vom Edge-Cluster beim Kunden bis zur Cloud. Du sorgst daf&uuml;r, dass unsere Go-Services, Dashboards und ML-Pipelines zuverl&auml;ssig laufen. Dein K&ouml;nnen entscheidet, wie verl&auml;sslich wir deployen und wie reibungslos unsere Plattform l&auml;uft. Wenn Du verstehst, was gute Infrastruktur ausmacht, und sie auch gestalten willst, dann melde Dich gerne bei uns.
 
 
 
 
 
 
 &Uuml;ber die Stelle
 
 
 
 
 
 
 Unsere Plattform l&auml;uft auf mehreren Kubernetes-Clustern: on-prem beim Kunden, in der Cloud und auf Edge-Gateways direkt in der Produktion. Ein Teil davon in Umgebungen, die keine Ausf&auml;lle erlauben &ndash; Werkshallen, in denen rund um die Uhr produziert wird. Dein Job ist, dass diese Systeme zuverl&auml;ssig laufen und das Team schneller deployt, ohne dass etwas schief l&auml;uft.
 
 
 
 
 
 
 Dein Stack:
 
 Kubernetes, Linux, Helm, g&auml;ngige Cloud-Plattformen, CI/CD und GitOps &uuml;ber GitLab. Observability l&auml;uft &uuml;ber Prometheus und Grafana. Security und Network-Design sind Teil des Jobs &ndash; wir bauen in Umgebungen, in denen Cyber-Security nicht nur ein H&auml;kchen in der Checkliste ist.
 
 
 
 
 
 Wir arbeiten in einem kleinen, eng abgestimmten Team. Code Reviews und Pair Programming geh&ouml;ren fest zum Alltag. Freitags zeigen wir uns interessante Funde im Web und neue Tools in unseren Show-and-Tell-Sessions. Als Cloud &amp; DevOps Engineer bist Du nicht Teil eines reinen Ops-Teams, sondern arbeitest t&auml;glich mit den Backend-, Frontend- und Data-Science-Teams zusammen und stellst die Verf&uuml;gbarkeit der Plattform sicher.
 
 
 
 
 
 
 Was sind Deine Aufgaben?
 
 
 
 
 
 <ul>
 <li>
 Du betreibst und skalierst unsere Kubernetes-Cluster &ndash; on-prem, in der Cloud und am Edge &ndash; mit Blick auf Verf&uuml;gbarkeit, Effizienz und Performance.
 </li>
 <li>
 Du baust und pflegst unsere CI/CD-Pipelines mit GitOps: Linting, Tests, Builds, Rollouts in Staging- und Produktionsumgebungen.
 </li>
 <li>
 Du sicherst unsere Infrastruktur ab &ndash; Netz-Segmentierung, Zugriffskontrolle, Secrets-Management &ndash; und machst Sicherheit zum Standard.
 </li>
 <li>
 Du entwickelst und wartest Kubernetes-Operatoren und Helm-Charts zusammen mit dem Backend-Team.
 </li>
 <li>
 Du automatisierst, wo der Schuh dr&uuml;ckt: manuelle Schritte, sich wiederholende Alerts, Config-Drift zwischen Umgebungen.
 </li>
 <li>
 Du begleitest die Backend-, Frontend- und Data-Science-Teams beim Deployment und beim Debugging in Kubernetes.
 </li>
 </ul>
 
 
 
 
 
 Was solltest Du mitbringen?
 
 
 
 
 
 <ul>
 <li>
 Mindestens drei Jahre praktische Kubernetes-Erfahrung, ideal mit gemischten On-Prem- und Cloud-Umgebungen.
 </li>
 <li>
 Sicher im Umgang mit Linux, Shell-Skripting und Infrastructure-as-Code (Ansible, GitOps, Helm oder vergleichbar).
 </li>
 <li>
 Praxis mit CI/CD-Pipelines &ndash; Du erkennst, warum ein Schritt flaky ist, und behebst die Ursache, nicht das Symptom.
 </li>
 <li>
 Gesp&uuml;r f&uuml;r Sicherheit: Networking, TLS, RBAC, Secrets, Hardening &ndash; ohne Compliance-Theater.
 </li>
 <li>
 Sehr gute Englischkenntnisse in Wort und Schrift.
 </li>
 <li>
 Bonus: Erfahrung mit Edge-Deployments, industriellen Netzen (OT/IT-Segregation) oder Airgap-Installationen.
 </li>
 <li>
 Bonus: Go-Grundlagen &ndash; Du beh&auml;ltst Deployments im Auge und sagst dem Backend-Team, wo es einfacher geht.
 </li>
 </ul>
 
 
 
 
 
 Was erwartet Dich?
 
 
 
 
 
 <ul>
 <li>
 Echte End-to-End-Verantwortung f&uuml;r unsere Infrastruktur &ndash; vom ersten Design bis zum Rollout in der Produktion.
 </li>
 <li>
 Das Team entscheidet &uuml;ber Architektur, Tooling und Testabdeckung selbst.
 </li>
 <li>
 Agentische Werkzeuge im Alltag: Codex, Claude Code und neue Entwicklungspraktiken &ndash; wir probieren fr&uuml;h aus und nutzen, was funktioniert.
 </li>
 <li>
 &Uuml;berwiegend vor Ort in
 
 Stuttgart, Ulm oder Berlin
 
 &ndash; Industrial-IoT-Projekte f&uuml;r Kunden weltweit, vom mittelst&auml;ndischen Fertiger bis zum DAX-Konzern.
 </li>
 </ul>
 
 
 
 
 
 Haben wir Dein Interesse geweckt?
 
 
 
 
 
 
 Bei DATATRONiQ bist Du kein kleines R&auml;dchen im Getriebe, Du gestaltest mit und bringst Deine Ideen ein. Du wirst die einzigartige M&ouml;glichkeit haben, an einem Produkt zu arbeiten, das das Potenzial hat, einen signifikaten Einfluss im industriellen Fertigungssektor zu haben. Wenn Du Deine F&auml;higkeiten einsetzen m&ouml;chtest, um anspruchsvolle Probleme zu l&ouml;sen und Du Dich aktiv bei uns einbringen willst, dann w&uuml;rden wir sehr gerne mit Dir sprechen!
 
 
 
 
 
 Nimm unter
 
 work@datatroniq.com
 
 Kontakt zu uns auf. Wir freuen uns auf Deine Bewerbung!
 
 
 
 
 
 
 Wir bitten Personalvermittlungsagenturen und Headhunter, von einer Kontaktaufnahme abzusehen.
 
 
</div>

Cloud & DevOps Engineer – Kubernetes, Edge & Cloud (Berlin)

Code Compass 🧭

<div>
 
 🤖 Machine Learning Engineer &ndash; Computer Vision &amp; Generative AI &ndash; 100%
 
 
 
 
 
 📍 Berlin, Germany | 💰 &euro;50,000 - &euro;70,000 + Benefits | 🌍 Hybrid Working
 
 
 
 
 
 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.
 
 
 
 
 
 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.
 
 
 
 
 
 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.
 
 
 
 
 
 ⚙️ Tech Stack:
 
 
 Python, PyTorch, TensorFlow, Computer Vision, Generative AI, Diffusion Models, GANs, Image Processing, Machine Learning, Docker, Cloud Platforms, Production ML Systems, Automation, AI Workflows
 
 
 
 
 
 🚀 About the Role:
 
 
 
 
 <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>
 
 
 
 
 ➕ Nice to Have:
 
 
 
 
 <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>
 
 
 
 
 💪 Benefits:
 
 
 
 
 <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>
 
 
 
 
 
 ✉️ 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.
 
 
</div>

Machine Learning Engineer

Talon.One LinkedIn Jobs

<div><div>ABOUT TALON.ONE: 


Talon.One is the most powerful incentives engine that unifies loyalty, promotions and gamification into one holistic platform. Backed by enterprise-grade security and scalability, Talon.One empowers companies to build personalized, profitable promotions and loyalty programs using any data.

Today, over 250 of the world's most-loved brands including Adidas, Sephora and Carlsberg work with Talon.One to drive deeper engagement and lasting loyalty with their customers.</div> 


ABOUT THE TEAM:

We're a passionate team, consisting of a Head of Data, a Data Engineer, an Analytics Engineer and a Senior Data Analyst dedicated to all things data. You'll be at the centre of our expanding Data team. We're enthusiastic about exploring new tools and technologies, and we thrive on brainstorming to solve challenges and apply best practices. Our team is set to grow soon, welcoming Data Engineers, Analytics Engineers, Analysts, and Data Scientists, all playing a pivotal role in Talon.One's development.

ABOUT THE ROLE:

As a Senior Data Engineer / Data Ops, you will be responsible for all engineering tasks related to the data infrastructure, data ingestion and DWH pipelines. You will own our data ingestion workflows (currently based on a Kafka stream) and will make sure that the data from our PostgresDB instances with a very high amount of data is ingested live into BigQuery. Besides, you will manage our overall data platform infrastructure, recommend improvement and make sure to always follow best practices. Finally, you will also of course be involved in the Data Engineering pipelines, assisting in the modelling of various data sources helping literally all departments within the company making better decisions. 


ONCE YOU ARE HERE YOU WILL: 

<ul>
<li>Own the data ingestion streams for all of our client's DBs, from Postgres to BigQuery</li>
<li>Own the overall data infrastructure: set up, maintain and improve our stack with new technologies for more efficiency and best practices. E.g. improve orchestration</li>
<li>Write DWH pipelines and improve our overall DWH code to apply best practices and bring more efficiency.</li>
<li>Be part of a data team composed of various profiles, be at the center of all infrastructure decisions and work closely with other stakeholders from the R&amp;D department, especially the DevOps team.</li>
</ul>
WHAT WE NEED YOU TO BRING TO THE TABLE:
<ul>
<li>3 -5 years of experience in the Data Engineering / Data Ops field, ideally working on some big data warehouse projects.</li>
<li>You are familiar with the modern data stack and are able to recommend, set up and maintain all different kinds of components from Data ingestion to automation.</li>
<li>You have experience with a wide range of tools and technologies within the modern data stack: Kafka, Airflow, Dagster, DBT Core, BigQuery, Lambda functions etc..</li>
<li>You have experience with general infrastructure / DevOps management technologies: ArgoCD, Kubernetes, Docker, Github, Postgres, GCP etc..</li>
<li>You always thrive for applying data engineering best practices: consistency checks, testing data reliability, monitoring on top of ingestion pipelines, no double business logic in the code.</li></ul> 


ABOUT TECHNOLOGY:
<ul>
<li>Kubernetes running on Google Cloud, ArgoCD, Terraform</li>
<li>PostgreSQL</li>
<li>Aiven Kafka Connect, Strimzi, Debezium</li>
<li>DBT cloud, GCP, BigQuery, Fivetran</li>
<li>We use Go for our API, core logic, and internal tooling</li>
<li>Cypress for automated end-to-end testing</li>
<li>Other odds &amp; ends in JavaScript and shell script</li></ul> 

<div>
WHAT'S IN IT FOR YOU:</div>
<ul><li>A Research and Development Department of 90+ of engineers, product managers and product designers in Berlin</li>
<li>Leaders with 8+ years of experience building our promotions engine</li>
<li>&euro;1,000 annual learning budget and free German language courses to boost your skills</li>
<li>30 days of annual leave, plus extra paid days for your birthday and moving day</li>
<li>Home office setup budget, a monthly home office allowance</li>
<li>Freedom to work from abroad for up to 90 days worldwide!</li>
<li>Mental health support with nilo.health and a discounted Urban Sports Club membership</li>
<li>20% company subsidy on your pension contributions</li>
<li>Subsidised BVG public transport ticket and a dog-friendly Berlin office where your furry friend is welcome</li>
<li>Lease your ideal bike through BusinessBike</li></ul></div>

Senior Data Engineer

Chapter 2

<div>Chapter 2 is partnering exclusively with a leading digital health startup at the forefront of AI-enabled cardiac telemonitoring. Working with institutions including Charit&eacute;, Mayo Clinic, and UCSF, they are building technology that detects early signs of heart failure deterioration and improves patient outcomes at scale.
They are looking for a Full Stack Engineer to join a small, high-ownership team and take end-to-end responsibility for significant product projects across their AI-enabled platform.
The Role
You will work closely with the Product Lead to scope, build, and ship substantial features across the full stack, from physician and patient interfaces on the frontend to APIs, data modelling, and ML integration on the backend. You won't be handed tickets. You'll shape projects from concept through to post-launch iteration, lead a small squad of engineers, and collaborate directly with clinical partners, sales, and founders.
What You'll Need<ul><li>
M.Sc. in a STEM field with 2+ years of full-stack experience, or 4+ years without</li><li>
Strong Python on the backend, TypeScript/React on the frontend</li><li>
A product and user-focused mindset, you care about UX and make design decisions, not just implementation decisions</li><li>
Comfort operating with high autonomy in a small team</li><li>
Fluent English</li></ul>
Nice to Have<ul><li>
Experience with ML/data pipelines, audio/signal processing, or clinical data</li><li>
Background in regulated software (MDR, ISO 13485, IEC 62304) or healthcare products</li><li>
DevOps and cloud infrastructure experience (GCP, CI/CD)</li><li>
German language skills</li></ul>
What's on Offer<ul><li>
Competitive compensation package with meaningful equity</li><li>
A mission that matters, technology with direct impact on patient outcomes</li><li>
A Berlin-Mitte office and a team built from some of the best engineering and clinical minds in digital health</li><li>
Urban Sports Club membership</li></ul>
If you are a full-stack engineer who wants to own real product work in a clinical environment that is genuinely changing how heart disease is managed, we'd like to hear from you.</div>

Full Stack Engineer

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