The role

We seek an experienced Specialist Solutions Architect to support AI-focused customers leveraging Nebius services. In this role, you will be a trusted advisor, collaborating with clients to design scalable AI solutions, resolve technical challenges and manage large-scale AI deployments involving hundreds to thousands of GPUs.

You’re welcome to work on-site in Amsterdam or remotely from any other EU country.

Your responsibilities will include:

• Designing customer-centric solutions that maximize business value and align with strategic goals.
• Building and maintaining long-term relationships to foster trust and ensure customer satisfaction.
• Delivering technical presentations, producing whitepapers, creating manuals and hosting webinars for audiences with varying technical expertise.
• Collaborating with engineering and product teams to effectively prioritize and relay customer feedback.

We expect you to have:

• 3+ years of experience with cloud technologies in MLOps engineering, Machine Learning engineering or similar roles.
• Strong understanding of ML ecosystems, including models, use cases and tooling.
• Proven experience in setting up and optimizing distributed training pipelines across multi-node and multi-GPU environments.
• Hands-on knowledge of frameworks like PyTorch or JAX.
• Excellent verbal and written communication skills.

It will be an added bonus if you have:

• Expertise in deploying inference infrastructure for production workloads.
• Ability to transition ML pipelines from POC to scalable production systems.

Preferred tooling:

• Programming Languages – Python, Go, Java, C++
• Orchestration – Kubernetes (K8s), Slurm
• DevOps Tools – Git, Docker, Helm
• Infrastructure as Code (IaC) – Terraform
• ML Frameworks and Libraries – PyTorch, TensorFlow, JAX, HuggingFace, Scikit-learn

The role

We are seeking a highly skilled and customer-focused professional to join our team as a Solutions Architect specializing in Cloud infrastructure and MLOps. As a Cloud Solutions Architect, you will play a pivotal role in designing and implementing cutting-edge solutions for our clients, leveraging cloud technologies for ML/AI teams and becoming a trusted technical advisor for building their pipelines.

You’re welcome to work from any DACH country (Germany, Austria, Switzerland).

Your responsibilities will include: 

• Act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement. Conduct PoC, workshops, presentations, and training sessions to educate clients on GPU cloud technologies and best practices.
• Collaborate with clients to understand their business requirements and develop solution architecture that align with their needs: design and document Infrastructure as code solutions, documentation and technical how-tos in collaboration with support engineers and technical writers.
• Help customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI.
• Act as a single point of expertise of customer scenarios for product, technical support, marketing teams.
• Assist to Marketing department efforts during events (Hackathons, conferences, workshops, webinars, etc.)

We expect you to have: 

• 5+ years of experience as a cloud solutions architect, system/network engineer, developer or a similar technical role with a focus on cloud computing
• Strong hands-on experience with IaC and configuration management tools (preferably Terraform/Ansible), Kubernetes, skills of writing code in Python
• Solid understanding of GPU computing practices for ML training and inference workloads, GPU software stack components, including drivers, libraries (e.g. CUDA, OpenCL)
• Excellent communication skills, fluency in German or French preferred
• Customer-centric mindset

It will be an added bonus if you have: 

• Hands-on experience with HPC/ML orchestration frameworks (e.g. Slurm, Kubeflow)
• Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch)
• Solid understanding of cloud ML tools landscape from industry leaders (NVIDIA, AWS, Azure, Google) 

The team

Customer experience at Nebius GPU AI Cloud involves tackling customers’ challenges and directly impacting their success by solving real-world AI and ML problems at massive GPU cloud scale. You’ll not only resolve issues, but play a key role in shaping clients’ business success by optimizing their AI solutions. Working with advanced GPUs such as H200, B200 and GB200, as well as modern ML frameworks, you’ll influence the development of the Nebius AI Cloud and gain experience at the intersection of infrastructure and AI. With minimal bureaucracy, you’ll have the freedom to innovate, take ownership and drive change. Opportunities for growth are abundant in this vibrant and supportive professional community.

The role

We are seeking a highly skilled and customer-focused leader to join our team as a Cloud Solutions Architect Lead. As a team leader, you will play a pivotal role in customer success across EU region, both managing the efforts of Cloud Solutions Architects team (technical and process excellence, talent development) and building processes alongside your stakeholders. Player-coach mentality and proven track record of managing high expertise teams is a must. 

You’re welcome to work from our office in Amsterdam or remotely from any EU country.

Your responsibilities will include: 

• Lead and mentor a team of Solutions Architects, fostering a culture of technical excellence, customer empathy, and collaborative execution.
• Oversee and participate in technical engagements including PoCs, workshops, architecture reviews, and trainings.
• Drive consistency and quality across infrastructure design, IaC practices, technical documentation, and customer enablement content.
• Collaborate closely with Product and Development teams, representing customer needs and helping shape the roadmap.
• Guide customers in optimizing ML/AI/HPC pipelines for performance, scalability, and cost-efficiency on Nebius  cloud infrastructure.
• Build visibility and ownership culture amongst Solutions Architects.

We expect you to have: 

• 5+ years of experience in a technical role (Solutions Architect, DevOps Engineer, ML Engineer, or similar) with a focus on cloud-native systems and distributed computing.
• 2+ years of experience leading or mentoring customer facing engineering teams.
• Deep hands-on experience with Infrastructure as Code (Terraform, Ansible), Kubernetes, and cloud automation.
• Strong communication and stakeholder management skills across both technical and non-technical audiences.
• Passion for developing scalable, reliable cloud architectures and driving customer success.

It will be an added bonus if you have: 

• Experience with HPC or ML orchestration frameworks (e.g., Slurm, Kubeflow, Ray).
• Strong coding skills in Python and experience with GPU computing environments for ML/AI workloads.
• Proficient in GPU stack components (e.g., CUDA, cuDNN) and cloud-native patterns for ML training/inference.
• Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch, JAX).
• Understanding of cloud-based ML tools and offerings from NVIDIA, AWS, Azure, or Google Cloud.
• Experience participating in or leading technical contributions to conferences or open-source communities. 

The role

Nebius seeks a Key Customers Solutions Architect to support key and strategic Nebius GPU Cloud services customers. In this role, you will be a trusted technical advisor, helping clients design, deploy, and scale AI solutions while managing large-scale GPU workloads involving hundreds to thousands of GPUs. You will also collaborate with sales and product teams to drive growth and enhance customer satisfaction.

You’re welcome to work remotely from Europe.

Your responsibilities will include: 

• Serve as the primary technical point of contact, troubleshooting and resolving complex AI/ML.
• Guide customers in optimizing GPU performance for ML training and inference workloads, ensuring seamless integration and scalability.
• Partner with the sales team to identify new opportunities, promote the latest products, and deliver technical presentations.
• Act as a bridge to product teams, providing customer feedback, relaying feature requests, and ensuring alignment with customer requirements.
• Engage with internal and external stakeholders, negotiate solutions, and effectively drive alignment to address customer challenges.

We expect you to have:

• Experience: 5 - 10 + years in roles like Cloud Solutions Architect, Technical Account Manager, or Customer Engineer, with hands-on experience in cloud services and AI/ML workloads.
• Proficiency in Infrastructure as Code (IaC) tools like Terraform and Ansible.
• Experience with Kubernetes and Python programming.
• Solid understanding of GPU computing, including ML training, inference workloads, and GPU stacks (e.g., CUDA, OpenCL).
• Customer-centric approach with a proven ability to build trust and foster long-term relationships.
• Strong ability to explain technical concepts to technical and non-technical audiences.

It will be an added bonus if you have:

• Hands-on experience with HPC/ML orchestration frameworks (e.g., Slurm, Kubeflow).
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow).
• Familiarity with ML tools from NVIDIA, AWS, Azure, and Google Cloud providers.
• Strong project management skills, with the ability to prioritize tasks and deliver on deadlines.
• Proven experience mentoring technical teams and driving team growth.
• Expertise in stakeholder negotiation to support problem resolution and ensure seamless collaboration.

The role

We are looking for a Customer Engineer to support key and strategic Nebius GPU Cloud services customers. In this role, you will be a trusted technical advisor, helping clients design, deploy, and scale AI solutions while managing large-scale GPU workloads involving hundreds to thousands of GPUs. You will also collaborate with sales and product teams to drive growth and enhance customer satisfaction.

You’re welcome to work remotely from Europe.

Your responsibilities will include:

• Serve as the primary technical point of contact for troubleshooting and resolving complex AI/ML issues.
• Guide customers in optimizing GPU performance for ML training and inference workloads, ensuring seamless integration and scalability.
• Partner with the sales team to identify new opportunities, promote the latest products, and deliver technical presentations.
• Act as a bridge to product teams, providing customer feedback, relaying feature requests, and ensuring alignment with customer requirements.
• Engage with internal and external stakeholders, negotiate solutions, and effectively drive alignment to address customer challenges.

We expect you to have:

• Experience: 5+ years in roles like Solutions Architect, Technical Account Manager, or Customer Engineer, with hands-on experience in cloud services and AI/ML workloads.
• Proficiency in Infrastructure as Code (IaC) tools like Terraform and Ansible.
• Experience with Kubernetes and Python programming.
• Solid understanding of GPU computing, including ML training, inference workloads, and GPU stacks (e.g., CUDA, OpenCL).
• Customer-centric approach with a proven ability to build trust and foster long-term relationships.
• Strong ability to explain technical concepts to technical and non-technical audiences.

It will be an added bonus if you have:

• Hands-on experience with HPC/ML orchestration frameworks (e.g., Slurm, Kubeflow).
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow).
• Familiarity with ML tools from NVIDIA, AWS, Azure, and Google Cloud providers.
• Strong project management skills, with the ability to prioritize tasks and deliver on deadlines.
• Proven experience mentoring technical teams and driving team growth.
• Expertise in stakeholder negotiation to support problem resolution and ensure seamless collaboration.

WPP steht für kreative Transformation. Mit der Kraft von Kreativität gestalten wir eine bessere Zukunft für unsere Mitarbeitenden, unsere Kunden, unsere Gemeinschaften und unseren Planeten. Genau hier setzt WPP Media an: Als globales Mediennetzwerk von WPP vereinen wir die besten Plattformen, Talente und Partner, um in einer vernetzten Medienwelt neue Chancen für grenzenloses Wachstum zu schaffen.

Das bieten wir dir:

• Work-Life-Balance: Mit unseren flexiblen Arbeitszeiten & Remote Work gestaltest du deinen Alltag so, wie er zu dir passt. Außerdem kannst du Überstunden ausgleichen.
• Vacation mode on: Du erhältst 30 Urlaubstage plus einen flexiblen Tag – zusätzlich sind der 24. und 31.12. frei.
• Wellbeing first: Gesundheitsleistungen, attraktive Altersvorsorge sowie Mental Health Awareness Days stellen deine Gesundheit in den Mittelpunkt.
• Practice makes perfect: Entfalte dich mit unseren vielfältigen Weiterbildungen und nutze unsere internen Karriereprogramme für deine Entwicklung im WPP-Netzwerk.
• More than just a job: Dich erwarten unvergessliche Events, Mitarbeitendenrabatte und weitere Highlights wie das Job Ticket, Wellhub oder das Job Bike.
• Be yourself: Bei uns ist jede:r herzlich willkommen - Vielfalt und Chancengleichheit liegen uns am Herzen.

Das bewegst du:

Als Associate Director Data Science (Mensch) gestaltest du nicht nur die Zukunft unserer datengestützten Lösungen, sondern führst und entwickelst auch ein hochqualifiziertes Team von Data Scientists. Du bist die treibende Kraft hinter unserer Innovationsagenda und stellst sicher, dass wir durch datengetriebene Ansätze den maximalen Wert für unsere Kunden generieren und unsere Marktposition weiter ausbauen.

• Team Leadership & Entwicklung: Du führst, motivierst und entwickelst ein Team von Data Scientists und förderst eine kollaborative sowie leistungsstarke Teamkultur.
• Stakeholder Management & Beratung: Als zentraler Ansprechpartner für interne Fachbereiche sowie für Top-Kunden übersetzt du komplexe datenwissenschaftliche Erkenntnisse in klare, handlungsrelevante Empfehlungen.
• Projekt- & Portfolio-Management: Du überwachst das Projektportfolio deines Teams, stellst die Einhaltung von Zeitplänen sowie Budgets sicher und priorisierst Initiativen auf Basis ihres geschäftlichen Impacts.
• Innovation & Forschung: Aktiv beobachtest du Markttrends und wissenschaftliche Entwicklungen im Data-Science-Bereich und treibst den Transfer von Forschungsergebnissen in die Praxis voran.
• Qualitätssicherung & Governance: Die Etablierung und Überwachung von Qualitätsstandards für unsere Data-Science-Projekte gehören ebenso zu deinen Aufgaben wie die Sicherstellung der Einhaltung relevanter Daten-Governance-Richtlinien.
• Strategische Ausrichtung: Als treibende Kraft unserer Innovationsagenda stellst du sicher, dass wir durch datengetriebene Ansätze den maximalen Wert für unsere Kunden generieren und unsere Marktposition weiter ausbauen.

Das zeichnet dich aus:

• Umfassende Erfahrung: Du bringst mindestens 5+ Jahre Berufserfahrung im Bereich Data Science mit, davon mindestens 2-3 Jahre in einer leitenden Position mit Personalverantwortung.
• Exzellente Fachkenntnisse: Deine Expertise in Python (Pandas, Scikit-learn, TensorFlow/PyTorch), SQL sowie in der Entwicklung und Implementierung von Machine Learning Modellen ist fundiert und praktisch erprobt.
• Cloud-Expertise: Du verfügst über nachweisliche Erfahrung mit Cloud Computing Plattformen, idealerweise Google Cloud Platform (GCP), inklusive ML-Services und Data Warehousing (z.B. BigQuery).
• Führungskompetenz: Ausgeprägte Führungsqualitäten mit der Fähigkeit, Teams zu motivieren, zu coachen und zu Höchstleistungen zu entwickeln, zeichnen dich aus.
• Kommunikations- & Präsentationsstärke: Deine exzellenten Kommunikationsfähigkeiten ermöglichen dir sowohl die Präsentation komplexer Modelle vor technischen Teams als auch die strategische Beratung von Führungskräften und Kunden.
• Strategisches Denken & Geschäftssinn: Die Fähigkeit, komplexe geschäftliche Herausforderungen zu verstehen und datengetriebene Lösungen zu entwickeln, die einen messbaren Mehrwert schaffen, rundet dein Profil ab.

Darüber hinaus bist du: #selbständig #teamfähig #lösungsorientiert

Wenn du diesen Job spannend findest, dann ermutigen wir dich ausdrücklich zur Bewerbung, auch wenn du nicht jede oben gelistete Qualifikation aufweisen kannst.

Arbeiten bei WPP Media:

Als WPP Media gestalten wir die Zukunft der Medien und investieren dabei konsequent in unsere Mitarbeiter:innen. Jede:r Kolleg:in genießt die Vorteile des globalen Netzwerks von WPP Media und WPP, um sich weiterzuentwickeln und an der Spitze von Marketing und Werbung zu lernen. Vielfalt und Zusammenhalt leben wir durch zahlreiche Employee Resource Groups und regelmäßige interne Events - zum Austausch, zur Inspiration und zum gemeinsamen Feiern unserer Erfolge.
 
WPP Media ist ein Arbeitgeber, der Chancengleichheit fördert. Wir berücksichtigen alle Bewerber:innen für alle Positionen ohne Diskriminierung. Unser Ziel ist eine Kultur des Respekts und der Zugehörigkeit, in der jede:r die gleichen Möglichkeiten hat, sich einzubringen und beruflich zu wachsen.

Bitte lies unsere Datenschutzerklärung für Deutschland, Österreich und die Schweiz, um weitere Informationen darüber zu erhalten, wie wir die von dir bereitgestellten Daten verarbeiten. 

SHOULD YOU ACCEPT THIS CHALLENGE... We are looking for passionate, inspirational, hands-on experts for Everpure's fastest growing and cutting edge team - the Analytics & AI Field Solutions Architects. The Analytics & AI FSAs are highly motivated technical sales resources whose goal is to provide guidance, enablement, and support for all things Analytics & AI focusing on opportunities and partnerships across the DACH sales region.

This architect must understand the clients' overall business, how to position the value of a Everpure Solution, and be an authority on how Everpure exists in various high performance data pipelines.

The "Hunter" Mindset (Internal Strategy & Orchestration) This role requires a proactive mindset—we need an orchestrator, not a passive supporter. You will partner with account teams to review territory plans, identifying "hot topics" and data-heavy strategies within customers to generate AI pipeline. You are responsible for pushing the team internally, ensuring they are effectively qualifying and positioning Analytics & AI opportunities.

The position requires the ability to work cross functionally across Solutions, Marketing, Alliances, Engineering, Enablement, Sales, etc. to align initiatives and go to market campaigns while maintaining technical expertise in all new and upcoming solutions in the space.

What You Will Be Doing

• Content Creation: Creating field facing solutions content (Technical Walkthroughs, Deep Dives, Demos, Qualification Guides, etc.) for specific solutions around Analytics & AI.
• Field Enablement: Mainstreaming solution area and other enablement projects to better equip the field for the first stages of Analytics & AI opportunities.
• Evangelism: Webinars/Events presentations including internal and external content with Alliance Partners, Channel Partners, Vendors, etc.. You should be comfortable and impactful on stage, capable of inspiring the field and attracting customers to adopt Everpure Storage technology.
• Customer Engagement: Participate in customer facing calls, demos, POCS, EBCs, internal strategy/sizing sessions, and other related solution-focused activities.
• Technical Strategy: Staying abreast of advancements in both our internal product offerings (FlashArray, FlashBlade, Portworx), new and updated solutions briefs, and formalizing an enablement plan from those.
• Feedback Loop: Solutions Roadmap participation/influence, daily conversations with Solutions Architects and Product Management about field impact, deals, and feedback from the field on existing solutions, etc..
• Field Testing: Participation/oversight in field testing in order to document anecdotal knowledge about that application and platform, in order to bridge the gap between not having a solution and Solutions officially testing.
• Post-Sales: Post sales assists/troubleshooting with expert level knowledge around solutions.
• Collaboration: Partner with Sales Leaders for two-way feedback and communication. High degree of cross-functional engagement with other GTM and corporate functions.

What You Bring To The Team

• Experience: 5+ years of Pre-sales experience in IT covering both private and public sector accounts.
• Education: Bachelor's Degree in Data Science, Computer Science, or related field / equivalent experience.
• Language: Must speak German and English fluently.
• Technical Acumen (Builders, not just Users):
• We are looking for people who understand the underlying architecture of AI, not just users of tools. You must have strong technical acumen in the Analytics & AI space with deep interest and vision for the industry.
• Strong understanding of the infrastructure (servers, networking, storage, etc) that underpins a high performance environment.
• Knowledge of the Analytics & AI landscape and how they all play together at Everpure for a specific outcome for a customer. This includes various applications/technologies part of a larger data pipeline such as Splunk, Elastic Search, Kafka, Spark, Snowflake, Vertica, TensorFlow, PyTorch, etc..
• Container Knowledge (Beneficial): Experience with modern application stacks is highly desirable. Knowledge of Kubernetes, container orchestration, and container-native storage (e.g., Portworx) in the context of AI pipelines is a strong plus.
• Communication:
• Ability to explain complex concepts in a simple to understand distilled manner to teach account teams and partners about Everpure's Analytics & AI solutions.
• Excellent oral and written communications skills, as well as excellent presentation skills.
• Execution: Previous hands on experience executing PoC's and demonstrations required.
• Problem Solving: Strong technical and tactical problem solving skills and experience with complex engineering designs.
• Travel: Ability to travel up to 50% within the DACH region.
• Work Ethic: Strong work ethic, attitude and follow through ability.

About the position

You are expected to conduct and lead internationally competitive research in AI-driven semantic analysis and automated reasoning-flow modeling, with applications to educational materials, scientific documents, and research authoring. Your work will focus on developing advanced methods for semantic structure extraction, conceptual and argumentative flow reconstruction, rationale-aware content generation, metacognitive prompting, and adaptive personalization. Core research contributions will include designing algorithms for concept and structure extraction, building neural/graph hybrid models for pedagogical reasoning, implementing ontology-alignment methods for cross-domain transfer, and creating human-in-the-loop optimization pipelines that incorporate expert feedback—aligning with the multi-year research roadmap (WP1–WP3) .

As a postdoctoral researcher, you will take a leading role in designing experiments, shaping research directions, supervising junior researchers (including PhD students), and contributing to the development of end-to-end demonstrator systems capable of generating structured, rationale-rich, and learner-adaptive content. You will be responsible for coordinating data annotation efforts, overseeing the construction of evaluation frameworks, and driving publication-quality research outputs, including survey papers, methodology papers, and cross-domain transfer studies. Interdisciplinary collaboration is strongly encouraged, especially at the intersection of computer science, computational linguistics, cognitive science, and learning technologies, and you will have opportunities to initiate or co-lead joint projects with internal and external partners.

The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course development and supervising Bachelor’s, Master’s, and PhD theses. The university provides a supportive academic environment—including mentoring, administrative support, computational resources, and opportunities for technology transfer and industry engagement—enabling the postdoctoral researcher to establish or strengthen an internationally visible research profile.

Mandatory requirements

• PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a closely related field.
• Strong publication record in relevant top-tier venues (e.g., NeurIPS, ACL, ICML, ICLR, AIED, LAK, AAAI).
• Demonstrated expertise in at least two of the following areas:
  • semantic parsing or structured NLP
  • knowledge representation, reasoning graphs, or GNNs
  • natural language generation, explainability, or rationale modeling
  • ontology alignment, cross-domain transfer, or representation learning
  • personalization algorithms, metacognition, or educational AI
  • human-in-the-loop systems or RLHF
• Experience in leading research tasks, mentoring junior researchers, and coordinating multi-stage projects.
• Fluency in Python and modern deep learning frameworks (PyTorch/TensorFlow).
• Strong analytical, communication, and academic writing skills.
• Demonstrable ability to design rigorous experimental pipelines and validation methodologies.
• Responsible, independent, and proactive research personality with strong teamwork skills.
• Intercultural experience and readiness to work in an international research environment.
• Fluency with AI co-pilot assistants for coding and writing.
• Fluency in English, the primary language of research and instruction on campus.

Preferred qualifications:

• Experience in developing research prototypes, evaluation benchmarks, or large-scale datasets.
• Prior involvement in interdisciplinary or applied research projects.
• Potential to contribute to competitive funding applications.
• Experience with open-source releases or reproducible research pipelines.

Application Details

• Starting date: March, 2026
• Contract: Full-time (100%), TV-L E13
• Work mode: Hybrid

Application package must include: 

• Curriculum Vitae (CV);
• A detailed letter of motivation outlining research interests and career goals;
• 2 recommendation letters;

Applications to be reviewed on a rolling basis. Shortlisted candidates will be invited to interviews.

We are seeking a Sr. Engineering Manager to join our 200+ product development organization and lead our ML Foundations team. This team focuses on solving complex machine learning problems and developing proprietary models for a range of AdTech use cases across our product suite.

As part of the AI Platform organization, you will lead a multidisciplinary team of Data Scientists, ML Engineers, and Software Engineers working to accelerate AI adoption and development across Smartly’s Media, Creative, and Intelligence suite. The team ensures compliance with privacy, security, and ethical guidelines.

This is an opportunity to define the vision and strategy for ML and Data Science applications, drive execution on ambitious goals, and champion AI and ML excellence throughout Smartly.

Smartly is proud to be ranked as a leader in The Forrester Wave™ for Creative AdTech. This recognition reflects our commitment to transforming ad experiences through intelligent creativity, standing as the only platform honored by both Forrester and validated by PwC to be recognized for ad technology to power performance.

To get an idea of some of the work we do, see for example these blog posts:

How We Productized Bayesian Revenue Estimation with Stan

Optimizing Conversions with Predictive Budget Allocation

Here’s What You'll Do:

• Lead and grow the AI Foundations team, providing technical direction, strategic vision, and hands-on leadership.
• Drive the development of AdTech-specific ML models and foundational AI capabilities and explore novel approaches that can be applied across Smartly’s products. Optimize existing models for performance, efficiency, and scalability.
• Collaborate cross-functionally with other teams to ensure AI-driven features deliver measurable business and customer impact.
• Ensure compliance with privacy, security, and ethical AI standards in ML applications.
• Foster a strong engineering culture focused on innovation, quality, and continuous improvement.
• Mentor team members through coaching, 1:1s, and career development plans.
• Remove obstacles to enable your team to thrive and deliver exceptional results.
• Partner with recruiting to help scale the team by actively sourcing and closing the best talent. 
• Partner with other leaders in the AI Platform to shape Smartly’s long-term AI strategy and architecture.

Here's What We're Looking For…

• 7+ years of experience in building services and products.
• 5+ years of experience in applying machine learning to real-life problems.
• 3+ years of experience in leading teams.
• Competent with key technologies in ML; PyTorch, TensorFlow, Python, SQL.  
• Able to mentor, coach, and develop data scientists and machine learning engineers
• Proven experience in delivering scalable ML systems in production.  You will need to be hands-on when coaching the team members to new technical and professional heights, which can involve code reviews, pair programming, etc.
• A pragmatic mindset. We always aim to create value to our customers. We're also working on production systems and our customers' business depends on it.
• Curiosity and the drive to continuously learn through study and experimentation on the job.  
• Ability to drive both technical excellence and cross-functional collaboration.
• Understanding of security and compliance requirements when working with personal data.
• M.Sc. or Ph.D. in a relevant field (Mathematics, CS, AI, Physics, …).
• Excellent communication skills, written and spoken English and ability to work 3 days/week onsite at our Berlin office

What we offer you…

• Ownership Culture: Make a significant impact on our products with autonomy and ownership.
• Industry Collaboration: Collaborate with leading online marketers and platforms like Meta, Pinterest, Snap, TikTok, and Google.
• Professional Development: Access opportunities for growth through mentorship, training, and resources to enhance your skills.
• Wellbeing Focus: Prioritize your health with comprehensive healthcare packages and mental health services, plus a healthy work-life balance.
• Total Rewards: Everyone at Smartly is an owner with our equity options program and performance-based grants. We recognize success with compensation, benefits, and career development opportunities. 
• We work in a flexible hybrid work model, including the option to work abroad for up to 30 days.

Smartly is committed to being the best place to work for growth-minded individuals to thrive - explore more in our Culture Handbook!

What to Expect from Our Recruitment Experience:

• Meet your Recruiter: Get to know each other and learn about working at Smartly.
• Meet the Team Manager: Gain deeper insights into your future team.
• Meet the Team in Subject Matter Interviews: Engage in a case study and showcase your domain knowledge.
• Meet the Leadership in a Final Meeting: A brief discussion with our Chief Technical Officer.

Apply now and shape the future of digital advertising!

Why Engineering at Dataiku?

Dataiku’s on-premise, cloud, or SaaS-deployed platform connects many data science technologies, and our technology stack reflects our commitment to quality and innovation. We integrate the best of data and AI tech, selecting tools that truly enhance our product. From the latest LLMs to our dedication to open source communities, you'll work with a dynamic range of technologies and contribute to the collective knowledge of global tech innovators. You can find out even more about working in Engineering at Dataiku by taking a look here.

Here are some useful links so you preview what we do at Dataiku: Dataiku's Key Capabilities ; Dataiku's Github, you can also take a look at the Gallery, a public instance showcasing some example projects (note editing is very limited and will be regularly reset). 

Our product is called Dataiku DSS which stands for Dataiku Data Science Studio. If you’d like to know more about it, you can watch the demo here or try the free version here.

How you’ll make an impact

This position is either onsite/hybrid from our Berlin office or full remote from any part of Germany.

As a Fullstack Engineer, you’ll contribute to building Dataiku DSS core features by joining one of the following themes:

• Data Preparation: Develop tools for data integration, transformation, and cataloging, as well as Jupyter notebooks, SQL workbenches, and APIs.
  • Add new capabilities for data integration, transformation, and visualization using LLMs
  • Optimize workflows for large-scale datasets and improve database support
  • Strengthen developer tools like Jupyter notebooks and SQL workbenches
  • Tech stack: Java, Python, AngularJS, Angular

• AI & Machine Learning: Collaborate on next-gen AI features, from statistics and time series forecasting to LLM inference.
  • Build next-generation features like cross-provider LLM APIs or integration with the latest Machine Learning models
  • Collaborate with research teams on innovative machine learning experiments
  • Tech stack: Python, scikit-learn, TensorFlow, PyTorch, Java, Angular, AngularJS

• Data Consumption: build the experience that makes data accessible and actionable for everyone.
  • Enhance our collaborative Workspaces to organize and explore charts, dashboards, datasets and more
  • Create innovative interfaces that answer business questions and surface relevant insights using LLMs
  • Tech stack: Angular, AngularJS, Java, Python

• Data Visualization: elevate our data visualization capabilities with high-performance charting tools and dashboards.
  • Develop new chart types and optimize visualization performance
  • Enhance dash boarding capabilities to deliver fast and flexible experiences
  • Tech stack: React, AngularJS, Angular, D3.js, ECharts, Node.js, Java, Spring
    
    
• MLOps: Develop back-end capabilities for automating and monitoring ML model lifecycles while supporting cross-functional collaboration.
  • Build tools to automate retraining, monitoring, and deployment of ML models
  • Enhance collaboration features for ML stakeholders
  • Tech stack: Python, Java, Angular, Angular.JS, Kubernetes, Docker
    
    
• Platform: Focus on scaling and securing the platform while improving cloud integrations and supporting a wide array of data sources and engines.
  • Optimize processing engines for scalability and latency
  • Expand capabilities to support new databases and cloud platforms
  • Tech stack: Java, Python, Angular, Kubernetes, Spark, AWS, Azure, GCP
    
    
• AI Governance: Build features for AI compliance, connecting disparate systems and simplifying governance processes.
  • Develop customizable platforms for managing AI compliance and governance
  • Simplify policy enforcement and integration across disparate systems
  • Tech stack: Java, Angular, PostgreSQL

What you need to be successful 

• We’re stack agnostic, so all you need is a significant experience in software engineering,  building a real product
• We’re hiring at all levels (junior, mid, senior)
• A passion for combining backend performance with exceptional frontend user experience.
• You want to work in a fast-paced, high-growth environment that values diversity of talent, excellence of product, and exciting engineering challenges at scale

What does the hiring process look like?

• Initial call with a member of our Technical Recruiting team (45min)
• Video call with an Engineering Team Lead (1h30)
• Technical Assessment to show your skills (Home Test or Live Coding)
• Final Interviews with our VPs of Engineering (2h)   #LI-Remote

About the role:

• Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration.
• Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements.
• Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation.
• Partner with applied research to translate experimental improvements into production-ready systems.
• Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals.
• Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale.
• Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Deep experience with model evaluation, benchmarking, and error analysis for ML systems.
• Proficiency with ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face).
• Solid understanding of modern ML techniques, including transformer-based models and large-scale training.
• Experience building data pipelines and tooling for large-scale experimentation and quality analysis.
• Strong passion for improving real-world AI system quality, with a track record of delivering measurable, production-grade improvements.

About the role:

• Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration.
• Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements.
• Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation.
• Partner with applied research to translate experimental improvements into production-ready systems.
• Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals.
• Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale.
• Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Deep experience with model evaluation, benchmarking, and error analysis for ML systems.
• Proficiency with ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face).
• Solid understanding of modern ML techniques, including transformer-based models and large-scale training.
• Experience building data pipelines and tooling for large-scale experimentation and quality analysis.
• Strong passion for improving real-world AI system quality, with a track record of delivering measurable, production-grade improvements.

About the role:

• Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration.
• Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements.
• Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation.
• Partner with applied research to translate experimental improvements into production-ready systems.
• Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals.
• Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale.
• Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Deep experience with model evaluation, benchmarking, and error analysis for ML systems.
• Proficiency with ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face).
• Solid understanding of modern ML techniques, including transformer-based models and large-scale training.
• Experience building data pipelines and tooling for large-scale experimentation and quality analysis.
• Strong passion for improving real-world AI system quality, with a track record of delivering measurable, production-grade improvements.

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

About the Role

EnCharge AI is seeking a highly skilled and experienced AI Compiler Engineer to spearhead the efforts in developing and optimizing graph compilers tailored to cutting-edge AI and ML workloads. You will collaborate with hardware architects, and AI researchers to enhance performance, optimize computation graphs, and enable efficient model deployment on EnCharge’s Inference Accelerators. 

Responsibilities

• Architect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization.
• Work closely with ML researchers, hardware engineers, and software developers to design and deploy AI models, understanding and addressing hardware-specific challenges.
• Work on performance optimizations for neural network models, such as layer fusion, operator fusion, and graph-level transformations.
• Develop compiler optimizations and passes that convert high-level AI models (e.g., from TensorFlow, PyTorch) into intermediate representations (IR).
• Implement parsing, semantic analysis, and IR generation for deep learning frameworks.
• Research and integrate the latest advancements in compiler design, ML model optimizations, and hardware acceleration into graph compilers.
• Provide leadership, mentorship, and technical guidance to a team of engineers focused on graph compiler optimizations. 

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred).
• 3+ years in compiler development, with a strong focus on AI or ML graph compilers.
• Proficiency in AI graph compiler frameworks (e.g., MLIR, Torch-FX)
• Solid background in hardware architectures (e.g., GPUs, TPUs, ASICs) and optimization techniques such as fusion, quantization, and tiling.
• Familiarity with neural networks operators and code generation.
• Strong understanding of intermediate representations, code parsing, and semantic analysis in compiler design.
• Proficiency in C++, Python, or other programming languages commonly used in compiler development.
• Open-source contributions to AI software frameworks and libraries is a plus
• Demonstrated experience leading and mentoring engineering teams with successful project delivery. 

EnchargeAI is an equal employment opportunity employer in the United States.

The Role

The Lead Researcher in Quantum Machine Learning will be a member of Terra Quantum's AI Applied Research team. This team is at the forefront of conducting both fundamental and applied research in the field of quantum machine learning. Although the Lead Researcher will focus on innovating in quantum machine learning, the role involves translating research findings into practical algorithmic software solutions. The Lead Researcher is expected to closely monitor and analyse scientific and industrial trends and needs across short-term, mid-term, and long-term horizons, specifically within the quantum technology and machine learning sectors, to guide the development and implementation of hybrid quantum-classical algorithms.

Reporting directly to the Director of AI, the Lead Researcher in Quantum Machine Learning plays a pivotal role in driving excellence within their team. They are not only detail-oriented but also possess a remarkable capacity for enthusiasm. By demonstrating commitment and passion for the mission, they inspire their team members to contribute to making quantum technologies widely accessible and to effect positive change globally.

The Responsibilities

The Lead Applied Researcher should expect to work in the following Quantum Machine Learning Team activities.

• Fundamental research in quantum machine learning
  • Developing quantum machine learning algorithms for different data processing tasks, e.g. time series, routing and planning, image, graph data, or natural language processing
  • Keeping up with state-of-the-art approaches in quantum machine learning
  • Analysing parametrised quantum circuits in their capacity to learn via a hybrid quantum-classical optimization loop
  • Utilizing metrics of, e.g., the Fisher information matrix, the effective dimension, and Fourier terms accessibility for parametrized quantum circuit ansatz analysis
  • Improving the trainability of parametrised quantum circuits through layerwise batch-entropy regularization and similar techniques
  • Interpreting and explaining quantum machine learning models. Analysing the flow of information through different architectures of quantum neural networks
  • Researching and understanding where the quantum brings the benefit to machine learning
  • Developing data encoding and data processing techniques for different types of quantum computers
  • Assisting in writing patent draft applications
  • Writing research papers for scientific journals

• Efficient implementation of quantum machine learning algorithms
  • Executing quantum machine learning algorithms on QPUs, e.g. of QuEra, IonQ, Rigetti, and IBM Q
  • Adjusting and improving implementation of hybrid quantum neural networks for different QPUs
  • Exploring and testing the best ways to hybridize classical machine learning solutions with quantum machine learning
  • Understanding the way to implement an efficient interaction of hardware (CPU, GPU) and software (PyTorch, Pennylane)
  • Assisting in the development of our quantum machine learning SDK
  • Optimising, at least theoretically, the code of hybrid quantum-classical machine learning solutions for faster execution

• Supporting industry projects implementation
  • Bringing in novel ideas based on industry needs in time-series, routing and planning, GenAI, and natural language processing tasks
  • Brainstorming on possibilities of quantum machine learning algorithms applications to our client’s industry problems
  • Working on finding a theoretical or empirical advantage of using hybrid quantum-classical machine learning in industrial problems
  • Assisting in writing applied industry research papers for scientific journals

The Requirements

The Lead Applied Researcher is expected to have several qualifications depending on the area of activity in quantum machine learning.

• PhD in computer science, physics, mathematics, electrical engineering, or equivalent subject is required
• Publication record in quantum machine learning, or variational quantum algorithms is required
• Advanced expertise in one or more quantum programming languages (Qiskit, Q#, Pennylane, Cirq, Quipper, Scaffold, tket) is required
• Advanced experience working with variational quantum algorithms for machine learning is required
• Experience working with the classical machine learning algorithms is required
• Computational learning theory expertise is optional
• Expert knowledge in Python is optional
• Expert knowledge in Pytorch and TensorFlow is optional
• Experience in programming for GPUs is optional
• Experience in working with QPUs is optional
• Goal-oriented, analytical and the ability to work independently
• Flexible, proactive, and creative with the ability to work in the team
• Highly motivated and resilient with the ability to work interdisciplinary
• Proficiency in written and spoken English
• Applicants must have the legal right to live and work in the European Union or Switzerland. Unfortunately, we are unable to offer visa sponsorship for this role.

The Role

The Researcher in Quantum Machine Learning or Quantum Information will be a member of Terra Quantum's AI Applied Research team. This team is at the forefront of conducting both fundamental and applied research in the field of quantum machine learning. Although the Researcher will focus on innovating in quantum machine learning, the role involves translating research findings into practical algorithmic software solutions. The Researcher is expected to closely monitor and analyse scientific and industrial trends and needs across short-term, mid-term, and long-term horizons, specifically within the quantum technology and machine learning sectors, to guide the development and implementation of hybrid quantum-classical algorithms.

The Researcher in Quantum Machine Learning plays a role in driving excellence within their team. They are not only detail-oriented but also possess a remarkable capacity for enthusiasm. By demonstrating commitment and passion for the mission, they inspire their team members to contribute to making quantum technologies widely accessible and to effect positive change globally.

The Responsibilities

The Applied Researcher should expect to work in one and supporting in the other areas of the following Quantum Machine Learning Team activities.

• Fundamental research in quantum machine learning
  • Developing quantum machine learning algorithms for different data processing tasks, e.g. time series, routing and planning, image, graph data, or natural language processing
  • Exploring state-of-the-art approaches in quantum machine learning
  • Analysing parametrised quantum circuits in their capacity to learn via a hybrid quantum-classical optimization loop
  • Utilizing metrics of, e.g., the Fisher information matrix, the effective dimension, and Fourier terms accessibility for parametrized quantum circuit ansatz analysis
  • Improving the trainability of parametrised quantum circuits through layerwise batch-entropy regularization and similar techniques
  • Interpreting and explaining quantum machine learning models. Analysing the flow of information through different architectures of quantum neural networks
  • Researching and understanding where the quantum brings the benefit to machine learning
  • Developing data encoding and data processing techniques for different types of quantum computers
  • Writing research papers for scientific journals

• Efficient implementation of quantum machine learning algorithms
  • Executing quantum machine learning algorithms on QPUs, e.g. of QuEra, IonQ, Rigetti, and IBM Q
  • Adjusting and improving implementation of hybrid quantum neural networks for different QPUs
  • Exploring and testing the best ways to hybridize classical machine learning solutions with quantum machine learning
  • Understanding the way to implement an efficient interaction of hardware (CPU, GPU) and software (PyTorch, Pennylane)
  • Assisting in the development of our quantum machine learning SDK
  • Optimising, at least theoretically, the code of hybrid quantum-classical machine learning solutions for faster execution

• Supporting industry projects implementation
  • Bringing in novel ideas based on industry needs in time-series, routing and planning, GenAI, and natural language processing tasks
  • Brainstorming on possibilities of quantum machine learning algorithms applications to our client’s industry problems
  • Working on finding a theoretical or empirical advantage of using hybrid quantum-classical machine learning in industrial problems
  • Assisting in writing applied industry research papers for scientific journals

The Requirements

The Applied Researcher is expected to have several qualifications depending on the area of activity in quantum machine learning or quantum information.

• Currently pursuing or recently completed a degree in computer science, physics, mathematics,
  or equivalent subject
• Experience in one or more quantum programming languages (Qiskit, Pennylane, Cirq, etc.)
• Experience working with variational quantum algorithms for machine learning
• Experience working with classical machine learning algorithms
• Basic knowledge in Python, Pytorch and TensorFlow
• Experience in working with QPUs is optional
• Familiarity with quantum information theory is optional
• Familiarity with Tensor-Network techniques is optional
• Goal-oriented, analytical and the ability to work independently
• Flexible, proactive, and creative with the ability to work in the team
• Highly motivated and resilient with the ability to work interdisciplinary
• Proficiency in written and spoken English
• Applicants must have the legal right to live and work in the European Union or Switzerland. Unfortunately, we are unable to offer visa sponsorship for this role.

Role

As a Senior Research Scientist focused on Robotic Foundation Models, you will be responsible for the design and implementation of systems that enable the training, deployment, and utilization of large-scale AI models for robotics. You will guide technical direction, mentor engineers, and ensure the development of robust, scalable ML models  required to make robots significantly more capable in real-world environments. Your work will empower both internal teams and external partners by providing accessible tools and infrastructure for injecting advanced machine learning techniques into robotic applications.

How your work moves the mission forward

• Lead the design and implementation of infrastructure and architecture of large-scale robotic foundation models.
• Oversee the training and deployment of robust ML models for high-performance robotic applications.
• Empower ML researchers and engineers with accessible tools and infrastructure for developing and applying AI techniques in robotics.
• Collaborate with product and engineering teams to define infrastructure requirements and deliver successful solutions for partners and customers.

Skills you will need to be successful

• PhD in Computer Science / Engineering with a specialization in Machine Learning and / or Robotics.
• Experience collaborating on medium to large software projects for at least 4 years.
• Experience in programming Python / C++ and machine learning frameworks (Tensorflow, JAX).
• Demonstrable experience with offline Reinforcement / Imitation Learning in a robotics context.
• Proven ability to work with a team, setting directions and driving towards solutions.

Skills that will differentiate your candidacy

• Academic track record of publications in the field of offline Reinforcement / Imitation Learning in Robotics.
• Experience with Robotic Foundation Models / Vision-Language-Action Models.
• Experience with Online Reinforcement Learning and / or Computer Vision for Robotics.
• Experience with deploying ML services at scale, e.g., via cloud computing and containerization (Google Cloud, Docker, Kubernetes), databases (SQL, no-SQL), and service-oriented architecture frameworks.

Role

As a Senior Research Engineer focused on Data Engine, you will be responsible for the design and implementation of systems that enable the training, deployment, and utilization of large-scale AI models for robotics. You will guide technical direction, mentor engineers, and ensure the development of robust, scalable data pipelines and ML infrastructure required to make robots significantly more capable in real-world environments. Your work will empower both internal teams and external partners by providing accessible tools and infrastructure for injecting advanced machine learning techniques into robotic applications.

How your work moves the mission forward

• Lead the design and implementation of infrastructure for training and deploying large-scale robotic foundation models.
• Oversee the building and maintenance of data pipelines for collecting, processing, and feeding real-world robot data into machine learning workflows.
• Empower ML researchers and engineers with accessible tools and infrastructure for developing and applying AI techniques in robotics.
• Collaborate with product and engineering teams to define infrastructure requirements and deliver successful solutions for partners and customers.

Skills you will need to be successful

• Four-year degree in Computer Science.
• Experience collaborating on medium to large software projects for at least 4 years.
• Experience in programming Python / C++.
• Experience with machine learning (Tensorflow, JAX), cloud computing and containerization (Google Cloud, Docker, Kubernetes), database (SQL, no-SQL), and service-oriented architecture frameworks.
• Proven ability to work with a team, setting directions and driving towards solutions.

Skills that will differentiate your candidacy

• PhD in Computer Science / Engineering with a specialization in Machine Learning and / or Robotics.
• Academic track record of publications in the field of Machine Learning and / or Robotics.
• Experience with Robotic Foundation Models / Vision-Language-Action Models.
• Experience with deploying ML services at scale.

Role

As a software engineer focused on MLOps and deep learning infrastructure, you will design and build the foundational systems that empower robots with advanced machine learning capabilities. You will work within a cross-functional team of engineers and researchers to develop infrastructure that streamlines the training, evaluation, and deployment of large-scale AI models. Your efforts will provide accessible tools for injecting machine learning techniques into the Intrinsic stack, managing compute resources across cloud and on-premise environments, and ensuring that complex model lifecycles are efficient, reliable, and scalable for real-world industrial applications.

How your work moves the mission forward

• Design and implement scalable infrastructure for training and deploying deep learning models on top of a real-time robotic control stack.
• Help optimize data loading and training speed across 1000+ GPU training jobs.
• Build data pipelines that support distributed computing to process large volumes of robotics data for model training.
• Develop APIs and tools that allow internal and external researchers to easily integrate machine learning techniques into their workflows. This could involve leading efforts  to open source models and engage with the community.
• Optimize the allocation of compute resources, such as GPUs and TPUs, to reduce cost and latency during model development and create orchestration workflows to successfully run jobs on GKE.
• Create tools for model understanding and analysis to ensure reliability and traceability across the machine learning lifecycle.

Skills you will need to be successful

• Bachelor's degree in Computer Science, Robotics, Machine Learning, or a related technical field.
• 2 years of experience in software development with a focus on MLOps or machine learning infrastructure.
• Proficiency in programming with Python and C++.
• Experience with containerization and orchestration technologies, specifically Docker and Kubernetes.
• Experience with deep learning frameworks such as TensorFlow, JAX, or PyTorch.
• Experience with cloud computing platforms like Google Cloud Platform.
• Basic Front-end experience.

Skills that will differentiate your candidacy

• Master’s degree or PhD in Computer Science, Robotics, or a related field.
• Comprehensive knowledge of the entire image processing workflow, from initial sensor data acquisition (drivers, OS) up to the final application logic and decision-making.
• Experience with MLOps toolkits such as Kubeflow.
• Hands on experience with CUDA optimization or deep learning optimization. 
• Experience deploying machine learning models at scale in production environments.
• Familiarity with robotics systems or industrial automation hardware.
• Practical experience debugging distributed systems and optimizing network performance.
• Experience with accelerator orchestration (e.g., XManager).

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

About the Role

EnCharge AI is looking for an experienced AI Research Engineer to optimize deep learning models for deployment on edge AI platforms. You will work on model compression, quantization strategies, and efficient inference techniques to improve the performance of AI workloads. 

Responsibilities

• Research and develop quantization-aware training (QAT) and post-training quantization (PTQ) techniques for deep learning models.

• Implement low-bit precision optimizations (e.g., INT8, BF16).

• Design and optimize efficient inference algorithms for AI workloads, focusing on latency, memory footprint, and power efficiency.

• Work with frameworks such as PyTorch, ONNX Runtime, and TVM to deploy optimized models.

• Analyze accuracy trade-offs and develop calibration techniques to mitigate precision loss in quantized models.

• Collaborate with hardware engineers to optimize model execution for edge devices, and NPUs.

• Contribute to research on knowledge distillation, sparsity, pruning, and model compression techniques.

• Benchmark performance across different hardware and software stacks.

• Stay updated with the latest advancements in AI efficiency, model compression, and hardware acceleration. 

Qualifications

• Master’s or Ph.D. in Computer Science, Electrical Engineering, or a related field.

• Strong expertise in deep learning, model optimization, and numerical precision analysis.

• Hands-on experience with model quantization techniques (QAT, PTQ, mixed precision).

• Proficiency in Python, C++, CUDA, or OpenCL for performance optimization.

• Experience with AI frameworks: PyTorch, TensorFlow, ONNX Runtime, TVM, TensorRT, or OpenVINO.

• Understanding of low-level hardware acceleration (e.g., SIMD, AVX, Tensor Cores, VNNI).

• Familiarity with compiler optimizations for ML workloads (e.g., XLA, MLIR, LLVM). 

EnchargeAI is an equal employment opportunity employer in the United States.

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

About the Role

EnCharge AI is seeking an AI Runtime Engineer to develop and optimize the execution stack for our next-generation AI accelerator. In this role, you will work on low-latency, high-performance runtime software that enables efficient execution of deep learning models on specialized hardware. You will collaborate with hardware, compiler, and AI framework teams to deliver optimized AI inference and training performance across cloud and edge environments. 

Responsibilities

• Develop and optimize the AI runtime software stack for executing deep learning workloads on AI accelerators.
• Implement task scheduling, memory management, and kernel execution strategies for efficient computation.
• Optimize data movement between host and device using PCIe, DMA, shared memory.
• Design and implement high-performance APIs for AI Inference frameworks such as OpenVino, ONNX Runtime, vLLM
• Work on graph execution optimizations, including kernel fusion, pipelining, tensor tiling, and caching.
• Integrate runtime components with AI compilers (LLVM, MLIR, XLA, TVM) for optimized execution.
• Ensure scalability and reliability of the AI runtime for cloud-based and edge AI deployments. 

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
• 3+ years of experience in developing low-level runtime software for AI accelerators, GPUs, or HPC systems.
• Strong proficiency in C/C++ and low-level systems programming.
• Deep understanding of task scheduling, concurrency, and memory hierarchy.
• Experience with hardware-aware optimizations and dataflow architectures.
• Familiarity with deep learning execution frameworks (ONNX Runtime, TensorRT, TVM, OpenVINO).
• Experience with low-latency, high-throughput workload execution for AI models.
• Strong debugging and profiling skills for optimizing AI execution performance.
• Exposure to AI model deployment pipelines (Triton, TensorFlow Serving). 

EnchargeAI is an equal employment opportunity employer in the United States.

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

About the Role

EnCharge AI is seeking an LLM Inference Deployment Engineer to optimize, deploy, and scale large language models (LLMs) for high-performance inference on its energy efficient AI accelerators. You will work at the intersection of AI frameworks, model optimization, and runtime execution to ensure efficient model execution and low-latency AI inference.  

Responsibilities

• Deploy and optimize LLMs (GPT, LLaMA, Mistral, Falcon, etc.) post-training from libraries like HuggingFace
• Utilize inference runtimes such as ONNX Runtime, vLLM for efficient execution.
• Optimize batching, caching, and tensor parallelism to improve LLM scalability in real-time applications.
• Develop and maintain high-performance inference pipelines using Docker, Kubernetes, and other inference servers. 

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field.
• Experience in LLM inference deployment, model optimization, and runtime engineering.
• Strong expertise in LLM inference frameworks (PyTorch, ONNX Runtime, vLLM, TensorRT-LLM, DeepSpeed).
• In-depth knowledge of the Python programming language for model integration and performance tuning.
• Strong understanding of high-level model representations and experience implementing framework-level optimizations for Generative AI use cases
• Experience with containerized AI deployments (Docker, Kubernetes, Triton Inference Server, TensorFlow Serving, TorchServe).
• Strong knowledge of LLM memory optimization strategies for long-context applications.
• Experience with real-time LLM applications (chatbots, code generation, retrieval-augmented generation). 

EnchargeAI is an equal employment opportunity employer in the United States.