(junior) fullstack engineer (m/f/d) for a new product

data scientist

data engineer

ai engineer

ai research engineer

analytics engineer

applied scientist

Audible

<div>
 
 Description
 
 
 
 At Audible, we believe stories have the power to transform lives. It&rsquo;s why we work with some of the world&rsquo;s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us.
 
 
 
 About This Role
 
 
 
 As an Applied Scientist, you will solve large complex real-world problems at scale, draw inspiration from the latest science and technology to empower undefined/untapped business use cases, delve into customer requirements, collaborate with tech and product teams on design, and create production-ready models that span various domains, including Machine Learning (ML), Artificial Intelligence (AI) and Generative AI, Natural Language Processing (NLP), Reinforcement Learning (RL), real-time and distributed systems.
 
 
 
 About You
 
 
 
 Your work will focus on inventing or adapting scientific approaches, models, and algorithms driven by customer needs at the project level. You will develop components and/or end-to-end solutions that are deployed into production or directly support production systems, delivering consistently high-quality work that meets both scientific and engineering best practices. You will develop reusable science components and services that resolve architecture deficiencies and customers&rsquo; pain points, while making technical trade-offs for long-term/short- term. You will work semi-autonomously to deliver solutions, contribute to research papers at peer-reviewed venues when appropriate, and document your work thoroughly to enable others to understand and reproduce it. Your decision-making will consistently incorporate robust, data-driven business and technical judgment. You will collaborate with other scientists to raise the bar of both scientific and engineering complexity for the team and to foster valuable scientific partnership opportunities to help/guide science decisions. We work in a highly collaborative, fast-paced environment where scientists, engineers, and product managers work to test and build scalable foundational capabilities, as well as customer facing experiences. You will have the opportunity to innovate and think big within your projects scope, implement optimization services and algorithms, and influence the experiences of millions of customers. We are looking for a results-oriented Applied Scientist with deep knowledge in ML, NLP, Deep Learning, GenAI, and/or large-scale distributed computation.
 
 
 As an Applied Scientist, you will...
 
 
 <ul>
 <li>
 Understand use cases across the business and adopt/extend/design/invent solutions/models that are scalable, efficient, and automated for difficult problems that are not well defined
 </li>
 <li>
 Work closely with fellow scientists and software engineers (at Audible and Amazon) to build and productionize models, deliver novel and highly impactful features
 </li>
 <li>
 Review models of peers for the purpose of reducing and managing risk to the business, while improving customer experience
 </li>
 <li>
 Design, develop, and deploy modeling techniques and solutions for Content Understanding, Recommendations, GenAI-based product features, by employing a wide range of methodologies, working from simple to complex
 </li>
 <li>
 Contribute to initiatives that employ the most recent advances in ML/AI in a fast-paced, experimental environment
 </li>
 <li>
 Push the boundary of innovation
 
 
 </li>
 </ul>
 
 About Audible
 
 
 
 Audible is the leading producer and provider of audio storytelling. We spark listeners&rsquo; imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.
 
 
 
 Basic Qualifications
 
 
 
 <ul>
 <li>
 3+ years of building models for business application experience
 </li>
 <li>
 PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
 </li>
 <li>
 Experience in patents or publications at top-tier peer-reviewed conferences or journals
 </li>
 <li>
 Experience programming in Java, C++, Python or related language
 </li>
 <li>
 Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
 </li>
 <li>
 MSc + 5ys of relevant experience, or PhD +1 year in one of the following disciplines: Machine Learning, Computer Science, Computer Engineering, Data Science, Applied Math, or a related quantitative field
 </li>
 <li>
 3+ years of experience in Deep Learning, Natural Language
 </li>
 <li>
 Processing/Understanding, GenAI and/or Reinforcement Learning
 </li>
 <li>
 Proficiency in Python, SQL, and other scripting languages
 </li>
 <li>
 Experience employing and innovating with LLMs/GenAI to solve complex problems
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience using Unix/Linux
 </li>
 <li>
 Experience in professional software development
 </li>
 <li>
 Experience with building Recommendation Systems
 </li>
 <li>
 Machine Learning Pipeline orchestration with AWS (SageMaker, Batch, Lambda, Step Functions) or similar cloud-platforms
 
 
 </li>
 </ul>
 Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
 
 
 m/w/d
 
 
 Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner.
 
 
 
 
 Company
 
 - Audible GmbH - B38
 
 
 Job ID: A10450357
</div>

Applied Scientist

Zalando

<div><div>Ort</div><div>
Germany - Berlin</div><div>
Vertrag</div><div>
Full time</div><div>
Job-Kategorie</div><div>
Applied Science &amp; Research</div></div>
 <div><div>Junior Applied Scientist (all genders) - B2B Data Science and Algorithms
The ZEOS department is responsible for all partner-facing Zalando Logistics Solutions. We provide a holistic approach to delivering the fulfillment solutions that meet our partner&rsquo;s needs by unifying these services under a single umbrella. We aim to provide our partners with a profitable fulfillment experience, and we see that to do this, Machine Learning, Operations Research, and data-driven solutions will play a pivotal role.
We are seeking a highly motivated Junior Applied Scientist who is fueled by the desire to kickstart their career by building innovative and impactful ML/Optimization systems for our B2B logistics partners. You will join an established team of Senior Applied Scientists and Machine Learning Engineers, working in a cross-functional setup with Product Managers, Data Engineers, and Software Engineers.
The team&rsquo;s focus is helping our B2B partners improve inventory health and order fulfillment efficiency. With guidance and mentorship from senior team members, you will contribute to building various ML/DL forecasting models (demand, returns, lead-times), stochastic inventory optimization solutions, recommendation services, and emerging Agentic AI systems. You will play a key role in a cross-functional team where your eagerness to learn and grow will directly translate into value for our partners!
WHY YOU SHOULD BE INTERESTED&hellip;<ul><li>
Learn and Impact from Day One: You won't be stuck doing busywork. You will be an active scientific partner, working alongside experienced scientists and product teams to define problems and build solutions. Your fresh perspective will help identify new opportunities to create value for our partners.</li><li>
Tackle Complex Scientific Problems with Expert Guidance: This is your chance to bridge the gap between academia and industry. You'll tackle cutting-edge challenges in stochastic inventory optimization, multi-echelon demand forecasting, and recommender systems while receiving the mentorship needed to succeed.</li><li>
Explore Emerging Tech like Agentic AI: As we explore opening up MCP servers to our partners, you will assist in defining how we measure their performance. You will contribute to implementing evaluation frameworks (e.g., LLM-as-a-judge, safety guardrails) to ensure autonomous systems act reliably.</li><li>
Grow into End-to-End Ownership: You will have the opportunity to follow ideas from initial research and prototyping to production. You will learn how to define success metrics, collaborate with engineers to deploy scalable services, and monitor real-world impact on partner KPIs.</li><li>
Drive Impact at a Multi-Merchant Scale: Your contributions will help build platform-level services that scale across hundreds of diverse partners, shaping the future of a more sustainable and efficient e-commerce logistics network.</li></ul> 
WE&rsquo;D LOVE TO MEET YOU IF&hellip;<ul><li>
Educational background in a quantitative field; Masters degree or higher preferred.</li><li>
Up to 2 years of hands-on experience (including internships, working student roles, or significant academic/thesis projects) applying scientific methods to solve complex problems.</li><li>
A solid theoretical foundation and practical exposure in at least one of the following areas:</li><li>
Machine Learning or Deep Learning for time-series forecasting (e.g., LGBM, ARIMA, Prophet, Transformers).</li><li>
Operations Research and Optimization (e.g., stochastic inventory models, linear/integer programming, Monte Carlo simulations).</li><li>
Machine Learning Engineering basics (e.g., understanding of git, batch processing, basic software testing).</li><li>
Agentic AI &amp; MCP evaluation frameworks</li><li>
Proficiency in SQL and Python, with some exposure to working with datasets.</li><li>
Strong communication skills with the ability to explain scientific concepts clearly and a desire to learn how to communicate effectively with business stakeholders.</li><li>
A collaborative, growth-oriented mindset. You have a passion for learning by doing and are not afraid to ask questions.</li><li>
You thrive in our team's core value: &ldquo;High Challenge, High Support&rdquo;! We cherish an open, direct feedback culture and are here to help you develop into a world-class scientist.</li></ul>
INCLUSIVE BY DESIGN
At Zalando, our vision is to be the leading pan-European ecosystem for fashion and lifestyle e-commerce - one that is inclusive by design. We only assess candidates based on qualifications, merit, and business needs. We welcome applications from people of all gender identities, sexual orientations, personal expressions, racial identities, ethnicities, religious beliefs, and disability statuses. We only want to know why you're great for this role, so please avoid including your picture, age, and marital status in your CV as well.
We want to provide you with a great candidate experience. Please feel free to inform us of any accommodations you may need, so we can best support and assist you throughout the hiring process.
do.BETTER - our diversity &amp; inclusion strategy: https://jobs.zalando.com/en/our-culture/diversity-and-inclusion
OUR OFFER<ul><li>
Zalando provides a range of benefits, here's an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer.</li><li>
Employee shares program</li><li>
40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, discounts from external partners</li><li>
2 paid volunteering days per year</li><li>
27 days of vacation a year to start for full-time employees</li><li>
Family services, including counseling and support</li><li>
Health and wellbeing options (including Wellhub, formerly Gympass)</li><li>
Mental health support and coaching available</li><li>
Drive your development through our training platform and biannual peer-to-peer review</li></ul></div><div>
Recruiter
Casimir Udegbunam
casimir.udegbunam@zalando.de</div><div>
 Nach der Pr&uuml;fung werden unsere Recruiter*innen &uuml;ber eine offizielle Zalando E-Mail-Adresse (@zalando.de) Kontakt aufnehmen.
In einigen F&auml;llen arbeiten wir auch mit einer Auswahl von Headhunter*innen und Agenturen zusammen, um bestimmte Positionen zu besetzen. Bitte beachte, dass weder Zalando noch unsere Rekrutierungspartner*innen irgendeine Art von Bezahlung verlangen, um sich f&uuml;r eine Stelle zu bewerben oder an einem Vorstellungsgespr&auml;ch teilzunehmen.</div></div>
 <div></div><div><div><div><h2 class="jobSectionHeader">&Uuml;ber Zalando</h2>
Zalando wurde 2008 in Berlin gegr&uuml;ndet und ist die f&uuml;hrende europ&auml;ische Technologieplattform f&uuml;r Mode und Lifestyle. Wir verbinden 62 Millionen aktive Kund*innen mit mehr als 7.000 Marken in 29 M&auml;rkten. Unser Gesch&auml;ft basiert auf einer einzigartigen, KI-gest&uuml;tzten Daten- und Infrastrukturplattform. F&uuml;r unsere Kund*innen bietet unser Multi-App-Ansatz &ndash; bestehend aus Zalando, ABOUT YOU und Lounge by Zalando &ndash; ein inspirierendes, hochgradig personalisiertes Einkaufserlebnis. F&uuml;r unsere Partner bauen wir das Betriebssystem f&uuml;r den E-Commerce. &Uuml;ber ZEOS, Tradebyte und SCAYLE &ouml;ffnen wir unsere Logistik-, Software- und Servicekapazit&auml;ten f&uuml;r Marken und Einzelh&auml;ndler*innen zur nahtlosen, grenz&uuml;berschreitenden Skalierung ihres Gesch&auml;fts.</div></div></div>

Junior Applied Scientist (all genders)

Amazon

<div>
 
 Description
 
 
 
 Are you interested in how to build AI reasoning systems that give provably correct answers? Are you excited by science at the interface of classical AI reasoning and Large Language Models (LLMs)? Would you like to apply your technology to serve operations customers better?
 
 
 Amazon Robotics is looking for a talented Applied Scientist. You will innovate on combining language models (LMs) with classical AI reasoning. You will work with a team of scientists and engineers to achieve this. You will publish your results in papers at leading venues in AI. You will be part of a larger team and have the opportunity to work on problems such as: using LMs to generate plans, using AI reasoning to verify plan correctness, learning efficient reasoning strategies, self-improving models. You will work on basic science and on business problems in robotics, automation and fulfillment across our operations.
 
 
 Key job responsibilities
 
 
 
 In This Role You Will
 
 
 
 <ul>
 <li>
 Work closely with other scientists and engineers, and be part of Amazon&rsquo;s diverse global science community.
 </li>
 <li>
 Publish your research in top-tier academic venues and hone your presentation skills.
 </li>
 <li>
 Be inspired by challenges and opportunities to invent new techniques in your area(s) of expertise.
 
 
 </li>
 </ul>
 A day in the life
 
 
 You'll meet regularly with your technical lead and your team on your ideas, get guidance and feedback, work together on architectures and algorithms, author papers, build AI systems, all with the aim of delivering results for your operations customers. You'll work closely with other scientists to review your plans and results. You'll meet with engineers to implement your ideas at scale.
 
 
 
 About The Team
 
 
 
 The Veritas team is a science team working at the boundary between language models and classical AI reasoning. We work across on customer problems in fulfillment, automation and robotics. We focus on high quality research science informed by practical problems.
 
 
 
 Basic Qualifications
 
 
 
 <ul>
 <li>
 Experience in building models for business application
 </li>
 <li>
 Experience in patents or publications at top-tier peer-reviewed conferences or journals
 </li>
 <li>
 Experience programming in Java, C++, Python or related language
 </li>
 <li>
 Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
 </li>
 <li>
 Experience implementing algorithms using toolkits and self-developed code
 </li>
 <li>
 Publication record in top tier venues in generative AI reasoning or classical planning
 </li>
 <li>
 PhD in a relevant field (reinforcement learning, neurosymbolic AI, LLMs for formal reasoning)
 </li>
 <li>
 Experience in reinforcement learning or neuro-symbolic AI
 </li>
 <li>
 Practical experience with PyTorch, the HuggingFace ecosystem, SageMaker, and RL tools
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience using Unix/Linux
 </li>
 <li>
 Experience in professional software development
 </li>
 <li>
 Experience using AWS tools and services - including AWS batch, Boto, S3, EC2 etc
 </li>
 <li>
 Strong skills in experimental design/statistical analysis
 </li>
 <li>
 Strong software engineering skills
 
 
 </li>
 </ul>
 Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
 
 
 m/w/d
 
 
 Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner.
 
 
 
 
 Company
 
 - Amazon Development Center Germany GmbH
 
 
 Job ID: A10376861
</div>

Applied Scientist, Amazon Robotics

Siemens

<div><div><div>Transform the everyday</div><div><div><div>
Siemens builds the systems the physical world runs on: factories, power grids, buildings, trains, hospitals. Industrial and physical AI is a major opportunity in applied AI, and one of the harder ones to get right. There is a generation of AI-powered products to build.
We are an applied science organization building the science behind them. The work sits at the intersection of machine learning research, real world data, and production systems running in industrial environments.
As Senior Principal Applied Scientist, you set the scientific direction for the pod. You decide which problems are worth solving with ML, what methods to apply, how to evaluate them, and how to move them from a research result to a model that runs in production. You are accountable for the science: the rigor, the evidence, and the outcomes.
This is a senior individual contributor role. Your impact comes from owning the hypotheses, the evaluation, and the path from research to production, and from raising the scientific bar across the team.<div> 
As part of our team, you will have</div></div></div><ul><li>
An attractive remuneration package</li><li>
Appealing Siemens pension benefits</li><li>
Access to employee share plans</li><li>
30 days of paid vacation and a variety of flexible work schedules that allow time off for you and your family</li><li>
2 to 3 days of mobile working per week as a global standard</li><li>
Up tp 30 days workation per year in certain countries</li></ul>
Since each of over 300,000 team members feels that other benefits are particularly important, and we cannot list our entire benefit portfolio here, you can find more information here. 
 
The individual benefits are subject to regulatory, contractual, or corporate conditions. 
You&rsquo;ll make an impact by<ul><li>
Set the applied science roadmap for the pod across the core AI capabilities the product depends on, for example multimodal perception, computer vision, language and agentic reasoning, time series modeling, control</li><li>
Convert product and system requirements into clear research questions, hypotheses, and success metrics</li><li>
Design and own the evaluation and benchmarking frameworks for generative and predictive models, including offline metrics, online experimentation, and robustness testing in industrial conditions</li><li>
Lead applied research projects end to end, from literature review and method selection through experimentation, ablation, and productization</li><li>
Work with engineers to take models into production grade pipelines: data readiness, optimization, inference, observability</li><li>
Influence architectural and system decisions with scientific evidence and tradeoff analysis</li><li>
Identify and de-risk scaling challenges: data quality, model drift, latency, throughput, cost, safety</li><li>
Mentor scientists and engineers on experimentation rigor, reproducibility, and documentation</li><li>
Champion responsible and trustworthy AI: bias detection, model risk management, human in the loop controls</li></ul></div></div><div><div> 
This is how you'll win us over<ul><li>
Education</li><li><ul><li>
Master&acute;s degree or an equivalent qualification in Computer Science, Machine Learning, or a related field</li></ul></li><li>
Experience &amp; Skills</li><li><ul><li>
10+ years in applied machine learning, AI research, or data science, with a track record of models that shipped to production and made an impact</li><li>
Strong foundation in machine learning theory and practice across training, evaluation, and deployment</li><li>
Demonstrated experience setting the science direction for a portfolio of work and shipping it through to production with engineering teams</li><li>
Proficiency in Python and modern ML frameworks and toolchains</li><li>
Track record of building evaluation and benchmarking that the team can run a roadmap against</li><li>
Clear written and verbal communication, with the ability to explain complex ML concepts to engineers, product managers, and senior leaders</li></ul></li><li>
Preferred Qualifications:</li><li><ul><li>
Experience setting science direction across multiple capability areas at the same time</li><li>
Experience bringing applied research into real world products in industrial or physical domains: manufacturing, automation, robotics, energy, mobility, infrastructure, healthcare</li><li>
Breadth across multimodal ML, generative AI, retrieval, agentic workflows, control, or planning</li><li>
Scientific ML for physical systems: surrogate modeling, operator learning, physics-informed ML, geometry-aware ML, differentiable simulation, AI for semiconductor/EDA</li><li>
Experience standing up evaluation, online experimentation, or production monitoring as practices, not just for a single model</li><li>
Publications, patents, open source contributions, or significant internal technology transfers that the field can point to</li><li>
Experience hiring and mentoring senior or staff level scientists</li><li>
Experience working with globally distributed research, product, or engineering organizations</li></ul></li><li>
Ways of working: You set scientific direction, lead with autonomy, and raise the bar for rigor and collaboration across teams</li><li>
Languages: business proficiency in English</li></ul><div>
You are much more than your qualifications, and we believe in the potential of every single candidate. We look forward to getting to know you!</div></div></div><div><div> 
Your individual personality and perspective are important to us. We create a working environment that reflects the diversity of the society and support you in your personal and professional development. Let&rsquo;s get to know your authentic personality and create a better future together with us. As an equal-opportunity employer we are happy to consider applications from individuals with disabilities. 
About us
To transform the everyday, you need to think outside the box. That&rsquo;s why we at Siemens are looking for innovators who aren&rsquo;t afraid to push boundaries to join our diverse team of tech experts. Got what it takes? Then help us create lasting, positive change!
www.siemens.de/careers &ndash; if you would like to find out more about jobs &amp; careers at Siemens.
FAQ &ndash; if you need further information on the application process.</div></div></div>

Senior Principal Applied Scientist (f/m/d)

Weierstraß-Institut für Angewandte Analysis und Stochastik

<div>The Weierstrass Institute for Applied Analysis and Stochastics (WIAS) is an institute of the Forschungsverbund Berlin e.V. (FVB). The FVB comprises seven non-university research institutes in Berlin which are funded by the federal and state governments. The research institutes are members of the Leibniz Association. 
WIAS invites applications for the position of
 Head of a Research Group (f/m/d)
(Ref. 26/16)
in the Weierstrass Group on &ldquo;Data-driven Control and Optimization&quot;
to be filled at the earliest possible date. 
The position is embedded in the Weierstrass Group on data-driven control and optimization with application areas including quantitative biomedicine, modern materials or sustainable energy.
We are looking for a highly motivated early-career scientist with a PhD and an outstanding record in applied mathematics with an emphasis on optimization. A research orientation combining theory and algorithms for optimization with partial differential equations with mathematically founded artificial intelligence, in particular machine learning techniques, is of special interest.
What we offer:<ul><li>
WIAS Berlin is a premier research institution known for its strength in partial differential equations, mathematical modeling, nonlinear optimization, and applied mathematics in general.</li><li>
Payment according to the collective agreement for the public service (TV&ouml;D Bund) including the usual social benefits (30 vacation days in a 5-day week, state pension plan); subsidy for the public transport company ticket).</li><li>
Mobile work.</li><li>
A certified (Audit berufundfamilie) family-friendly work environment.</li><li>
The successful candidate will receive careful mentorship both from the supervisor and from other peers through a dedicated mentorship program.</li><li>
Berlin is one of the most culture-rich and diverse international cities in the world. It offers endless opportunities to enjoy life outside work, while being very affordable compared to other major cities. Neither the job nor living in Berlin requires German language (although WIAS offers free German courses). We highly welcome international applications. Scientifically, Berlin offers a rich landscape with numerous opportunities for research, as well as job prospects in academia and industry.</li></ul>
Technical queries should be directed to Prof. Dr. M. Hinterm&uuml;ller (Michael.Hintermueller@wias-berlin.de).
The position is remunerated in accordance with the TV&ouml;D Bund pay scale. The successful candidate will be appointed on a full-time basis (39 hours per week) at WIAS for an initial period of three years, with the possibility of renewal following a positive evaluation. The position comes with funding for research personnel as well as a dedicated budget for scientific travel and visiting researchers. The research group will be integrated into WIAS&rsquo;s flexible research platform.
WIAS is committed to fostering diversity and equal opportunities and particularly encourages women to apply. Applicants with disabilities will be given preferential consideration in cases of equal qualification. If you have any questions or want to provide us with information related to diversity, inclusion or disabilities, please feel free to contact our Diversity Officer, Sara Krummenacher (sara.krummenacher@wias-berlin.de).
How to apply:
Please upload your complete application package (including cover letter, curriculum vitae, certificates) via our applicant portal as soon as possible by clicking the &ldquo;Apply Online&rdquo; button.
The advertisement is open with immediate effect and will remain open until the position will be filled.
We are looking forward to your application!</div>

Head of a Research Group (f/m/d)

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>

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