The role

At Nebius, we’re building a next-generation AI compute platform for large-scale ML training and inference — from a few nodes to thousands of GPUs.
We’re looking for a Technical Product Manager to own product direction for Soperator — our Slurm-on-Kubernetes control plane for GPU clusters.
In this role, you will shape how ML engineers and research teams run, scale, and optimize distributed workloads in production.
If you care about systems that combine performance, reliability, and developer experience at the frontier of AI infrastructure, this role is for you.

Your responsibilities will include: 

• Own the full user journey across Soperator clusters: Slurm workflows, dashboards, alerts/notifications, node lifecycle, and training/inference capacity management.
• Define product direction end-to-end: problem discovery → solution design → delivery → adoption.
• Lead deep customer discovery through interviews, usage analytics, and workload analysis to uncover high-impact opportunities.
• Drive execution across platform teams: compute, networking, storage, observability, IAM and etc.
• Translate frontier ML and infrastructure ideas into practical product capabilities for real-world GPU clusters.
• Define success metrics, prioritize roadmap decisions with data, and ensure measurable customer/business impact.
• Lead the open-source strategy and execution for Soperator: shape public roadmap themes, prioritize OSS-facing capabilities, and ensure strong adoption in the community.  

We expect you to have: 

• 3–5+ years in Product Management, ML infrastructure/MLOps, distributed systems, or cloud platform engineering.
• Strong technical depth in distributed systems, cloud infrastructure, or ML platforms.
• Hands-on familiarity with large-scale ML training and orchestration tools (e.g., Slurm, Kubernetes, Ray).
• Track record of shipping technically complex products with multiple engineering teams.
• Strong communication and stakeholder management across engineering, research, and customers.
• Experience with product analytics, data-informed prioritization, and experimentation.
• High ownership, high learning velocity, and comfort operating in fast-moving AI infrastructure environments.

It will be an added bonus if you have: 

• Experience with GPU platforms and HPC primitives: InfiniBand/RDMA, topology-aware scheduling, high-throughput storage.
• Practical understanding of modern ML training stacks: PyTorch, DeepSpeed, FSDP/ZeRO, NCCL.
• Familiarity with efficiency and reliability metrics: Goodput, MFU, failure modes, preemption handling, health checks.
• Exposure to large-scale LLM training/inference systems.
• Experience in observability, performance tuning, or SRE/reliability engineering.
• Customer-facing technical experience (solutioning, support, architecture advisory).

About Nebius

Nebius AI is an AI cloud platform with one of the largest GPU capacities in Europe. Launched in November 2023, the Nebius AI platform provides high-end, training-optimized infrastructure for AI practitioners. As an NVIDIA preferred cloud service provider, Nebius AI offers a variety of NVIDIA GPUs for training and inference, as well as a set of tools for efficient multi-node training. 

Nebius AI owns a data center in Finland, built from the ground up by the company’s R&D team and showcasing our commitment to sustainability. The data center is home to ISEG, the most powerful commercially available supercomputer in Europe and the 16th most powerful globally (Top 500 list, November 2023).  

Nebius’s headquarters are in Amsterdam, Netherlands, with teams working out of R&D hubs across Europe and the Middle East. 

Nebius AI is built with the talent of more than 500 highly skilled engineers with a proven track record in developing sophisticated cloud and ML solutions and designing cutting-edge hardware. This allows all the layers of the Nebius AI cloud – from hardware to UI – to be built in-house, distictly differentiating Nebius AI from the majority of specialized clouds: Nebius customers get a true hyperscaler-cloud experience tailored for AI practitioners. We’re growing and expanding our products every day. 

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 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.

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 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

Token Factory is a part of Nebius Cloud, one of the world's largest GPU clouds, running tens of thousands of GPUs. We are building a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs.

Some directions we are currently working on, and which you can be a part of:

• Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).
• Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.
• Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.

We expect you to have:

• A profound understanding of theoretical foundations of machine learning and transformer architecture.
• Experience profiling GPU workloads using Nsight, PyTorch profiler, or similar tools
• Understanding of GPU memory hierarchy and compute/memory tradeoffs
• Familiarity with important ideas in LLM space, such as MHA, RoPE, KV-cache, Flash Attention, and quantisation
•  Understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.)
•  Strong software engineering skills (we mostly use Python)
• Deep experience with modern deep learning frameworks
• Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing
• Strong communication and leadership abilities

Nice to have:

• Experience working with open-source inference engines (vLLM, SGLang, TensorRT-LLM), including contributions
• Experience with kernel languages or DSLs such as Triton, Cute, CUTLASS, CUDA
• A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment.
• Strong engineering skills, including experience in developing large distributed systems or high-load web services.
• Open-source projects that showcase your engineering prowess
•  Excellent command of the English language, alongside superior writing, articulation, and communication skills.

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) 

Why Cast AI?

Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production.
The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation.
Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS.

Global team, diverse perspectives

We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead.

Unicorn momentum

In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started.

Join us as we build the future of autonomous infrastructure.

About the role

Throughput. Latency. KV cache utilization.

Move those three numbers in the right direction, and two things happen: customers get faster, cheaper inference, and our margins improve. That's the entire thesis of this role. Every kernel you tune, every quantization scheme you ship, every scheduler tweak you land shows up directly in a customer's p99 and on our P&L.
This is a high-impact seat. It is also a high-autonomy seat as you'll be given the room to lead the technical direction of inference optimization at Kimchi, not execute someone else's roadmap.

The problem: running LLMs in production is a moving target. The "right" model and serving configuration for a workload depend on traffic shape, sequence-length distribution, batch dynamics, GPU SKU, memory bandwidth, quantization tolerance, and a dozen other variables that shift week to week. Most teams pick a model once, over-provision GPUs, and absorb the cost. Kimchi is the system that makes that decision automatically - continuously matching workloads to the most cost-efficient, best-performing LLM and serving configuration on a customer's infrastructure. We're building the optimization layer between the model and the hardware, and we need engineers who understand both sides deeply.

Stack

Python; vLLM; SGLang; TensorRT-LLM; PyTorch; CUDA-adjacent tooling; Kubernetes; gRP; ClickHouse; PostgreSQL; GCP Pub/Sub; AWS / GCP / Azure; GitLab CI; ArgoCD; Prometheus; Grafana; Loki; Tempo.

Requirements:

• 5+ years building real ML systems, with a portfolio that shows depth in inference or training infrastructure (not just model training notebooks).
• Strong Python - production services, not scripts.
• Hands-on experience with at least one of vLLM, SGLang, or TensorRT-LLM, and a working mental model of why an inference engine performs the way it does on a given GPU.
• Fluency with quantization tradeoffs - you've measured quality regressions, not just compression ratios.
• Comfort with distributed systems: collective communication, sharding strategies, and the practical failure modes of multi-GPU and multi-node setups.
• A bias toward measurement. You instrument before you optimize, and you can tell the difference between a real win and a benchmark artifact.
• Self-direction. This role comes with a wide mandate; you should be excited by that, not unsettled by it.

Responsibilities:

• Push throughput. Continuous batching, speculative decoding, chunked prefill, kernel-level tuning across vLLM, SGLang, and TensorRT-LLM. Find the ceiling on each GPU SKU, then raise it.
• Cut latency. Attack TTFT and TPOT separately. Profile, identify the actual bottleneck (compute, memory bandwidth, scheduling, networking), and fix it - not the bottleneck you assumed.
• Get more out of the KV cache. Paged attention, prefix caching, eviction policies, cache reuse across requests, quantized KV. This is where a lot of the unrealized throughput lives, and it's an area you'll own.
• Quantize without regressing quality. INT8, INT4, FP8 across weights, activations, and KV. Empirical work: measure quality on real workloads, not just perplexity benchmarks.
• Shrink cold starts and memory footprint. Faster init, smarter weight loading, tighter memory accounting - the difference between a model that scales and one that doesn't.
• Scale across nodes. Distributed inference topologies, network-aware placement, checkpointing strategies that don't bottleneck on storage or interconnect.
• Set the technical direction. Decide what we benchmark, what we adopt, and what we build ourselves. Bring the team along with strong writeups and reproducible experiments.

What’s in it for you?

• Competitive salary (depending on the level of experience).
• Enjoy a flexible, remote-first global environment.
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology
• Equity options.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement. 
• Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.

Hiring process

• Screening call with Recruiter
• Hiring Manager interview
• Technical interview (system design)
• Live coding
• Culture Check interview with an executive

As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr.
Please note that Cast AI does not provide any form of visa sponsorship/work permit.

#LI-Remote

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.

Locations:

• Europe: Paris / UK / Germany / Remote (EU)

About the role

We are looking for a scientifically credentialed strategist to lead the post-sales journey for our pharmaceutical partners. In this role, you will be the bridge between our cutting-edge foundational models and the R&D pipelines of the world’s leading biopharma companies.

You will use your deep expertise in machine learning and computational biology to build credibility with stakeholders, identify new opportunities for model application within their pipelines, and translate customer feedback into our product roadmap.

What you'll be doing

As our Applied AI Scientist (Pharma Partnerships) you are the primary architect of value for our most critical pharmaceutical partnerships. You will operate at the intersection of computational biology, ML and commercial scale across two strategic domains:

1. Partnership Value Realisation & Growth

• Post-Signature Leadership: Orchestrate the partner journey immediately following contract execution. You will act as the Project Manager for the deployment, ensuring seamless onboarding and integration of our foundational models.
• Scientific & technical thought-leadership: Consult with client R&D teams (ranging from bench scientists and data science teams to Heads of R&D) to diagnose their specific therapeutic challenges. Identify key use cases where our AI foundation models can unlock high value to our customers research and therapeutic priorities.
• Commercial Adoption & Growth: Proactively identify opportunities to expand the scope of the partnership. Leverage your understanding of the client’s pipeline to suggest new use cases, therapeutic areas, or departments where our technology can drive value.

2. Product Intelligence & Roadmap Influence

• Voice of the Customer: Synthesize technical feedback and performance metrics from the field. Translate complex biopharma user requirements into actionable technical specifications for our internal Research and Product teams. Identify high value opportunities to enrich our model capabilities and biology applications offering.
• Gap Analysis: Collaborate with our machine learning engineers to identify data modalities or functional gaps in our current offering, directly influencing future product iterations.

What you'll bring

The successful candidate will have a ‘team-first’ attitude; be independent, curious, and detail-oriented; thrive in a dynamic, fast-paced environment; and be fun to work with. We value individuals who bring deep domain expertise in computational biology, ML and pharmaceutical R&D alongside strong hands-on business development skills.

Scientific & Technical Credibility

• Educational Background: PhD in Computational Biology, Machine Learning, Bioinformatics, Genomics, or a related field is required.
• Domain Expertise: Deep understanding of the biopharma R&D value chain (target discovery to clinical trials). You understand the specific pain points of drug developers and are able to translate ambiguous biopharma use cases into rigorous ML applications.
• Technical Fluency:
• Deep experience in biological data modalities, notably multi-omics (single-cell, transcriptomics, proteomics) and histopathology slides.
• Extensive knowledge in modern ML architectures and paradigms like Transformers (ViT), GNNs, foundation models with embedding-based prediction heads (e.g., ABMIL), interpretability methods, and model evaluation & validation principles.
• Proven experience in designing end-to-end ML solutions for complex biological problems, including problem framing, data strategy and model design & evaluation
• Strong fluency in Python and deep learning frameworks (PyTorch, JAX)

Relationship & Strategic Skills

• Stakeholder Management: Proven experience navigating complex, matrixed organizations (Big Pharma experience is a plus). You can present to a VP of R&D in the morning and troubleshoot with a bioinformatician in the afternoon.
• Consulting Mindset: You are not just a support agent; you are a trusted advisor. You have experience in roles such as Field Application Scientist (FAS), Solution Architecture, or Scientific Consulting.
• Feedback Synthesis: Ability to distill complex client complaints or requests into clear, prioritized product requirements.

How to stand out:

• Experience in a startup or innovative environment, showing adaptability and proactiveness.
• A strong existing network within Global Top 20 Pharma R&D.
• Experience specifically with "Foundation Models" or Generative AI in a biological context.
• Track record of high-impact publications (Nature, Cell, NeurIPS).

The candidate journey

To be considered, please submit your CV in English.

We believe in a transparent and collaborative interview process. We need to find a fit for both you and the company. Here is what you can expect after submitting your application:

• Screening: Once you have applied, the hiring team will review your application. If your experience and skills align with the role, you will be invited to a 30-minute introductory call with the Hiring Manager to discuss your background, motivations, and the position in more detail.
• Interviews: Following a successful screening, you will be invited to a series of interviews:
  • Case Study (60 min): You will prepare a mock kick-off document for a client engagement with us to evaluate the model on their desired use-cases, including suggesting interesting use cases for our model and outlining the project plan. We want to see how well you understand the potential use cases for foundation models in the clinical stage of pharma development cycles, and how you would manage a key strategic partner through the deployment and delivery process.
  • Scientific Deep Dive Presentation (30 min): You will briefly present a piece of scientific work to a small panel of our researchers and engineers, led by a senior member of our technical team, and answer Q&S after (10-15 min presentation, 15-20 min Q&A). The content is up to you: it can be your own past research, a relevant paper in the field, or extra points if it’s a specific Bio-AI use case you’ve worked on with a biopharma. The goal is to assess your technical fluency, your ability to facilitate scientific debate, and how you communicate complex concepts to experts.
  • Executive Interview (30 min): A comprehensive discussion with member(s) of our Senior Leadership. This session moves beyond technical competency to focus on long-term vision, values, and mutual potential. This is an opportunity for you to get to know the company better as well.
• Offer: Following the completion of all interviews, our hiring team will make a final decision and will be in touch to share the outcome. Please note that an offer is contingent upon the successful completion of a reference check.
• Onboarding: We are happy to have you joining the team! Once you have accepted and signed your offer, we will be in touch to begin the process of onboarding you.

Why this is a unique opportunity

• A collaborative and mission-driven work environment.
• Competitive salary and equity package.
• Flexible work arrangements, including remote options.
• Opportunities for professional growth and leadership development.
• Shape the future of biology and AI by contributing to groundbreaking work.

Software engineers and AI agents alike suffer from the same problem: finding that one person or place that will answer their tough, specific question. Many solutions promise to solve this with similarity search in vector databases. Unfortunately, finding the answer is often a puzzle with pieces to be collected across a myriad of contradictory sources and cannot be solved without surgical search and careful reasoning.

Spectrum collects data from an organization's code, docs, and issues, and organizes knowledge in a unified ontology that AI agents can efficiently search through and reason over. We aim to revolutionize the semantic layer space for software-building organizations and move beyond specs that fall out of sync with code, introducing a living spec – one that's extracted from the whole system and used to keep it aligned. Spectrum is meant to be the single source of truth for all product and architectural knowledge.

A resident of JetBrains' startup incubator, Spectrum enjoys startup speed and autonomy, and is backed by 25 years of developer tooling expertise. We are looking for a Senior ML Researcher to develop the core methods that make Spectrum possible – novel approaches to temporal ontology extraction, contradiction detection, and semantic alignment across heterogeneous software artifacts. You will help define and execute the research agenda, while also collaborating with JetBrains Research and external academic advisors.

Your responsibilities will include:

• Developing methods for LLM-based ontology construction from code, documentation, and issue trackers, including contradiction detection and reasoning over the resulting knowledge graph.
• Creating datasets, metrics, and benchmarks that drive measurable improvements across the system.
• Prototyping and validating research ideas, then collaborating with ML and software engineers to bring them to production.
• Collaborating with JetBrains Research and external academic advisors to shape our research agenda.
• Publishing research findings at top venues and representing Spectrum in the research community.

We expect you to have:

• A PhD (or equivalent research experience) in NLP, knowledge graphs, ontology learning, information extraction, or a closely related field.
• A strong publication record in at least one of the following areas: knowledge graph construction, ontology learning, information extraction, or NLP.
• Experience applying LLMs to structured knowledge extraction tasks.
• Experience designing and running rigorous experiments, including hypothesis formulation, ablation studies, and statistical evaluation.
• Strong Python and PyTorch skills, with the ability to implement research ideas from scratch.
• Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
• Proficiency in English, both written and verbal.

Our ideal candidate would also have:

• Experience with ontology engineering, semantic web technologies (OWL, RDF, SPARQL), or ontology alignment.
• A background in code analysis, developer tools, or software engineering research.
• Experience with knowledge graph embedding methods or graph neural networks.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• Actively contributed to relevant open-source projects.

What we offer:

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.
• Full support for publishing ML research and attending industry conferences.
• Collaboration with JetBrains Research and external academic advisors in the knowledge graph and ontology learning community.
• A motivated team that moves fast, with short feedback loops, rapid prototyping, and lots of ownership.
• A rare applied research role where your methods ship in a real product used by software teams.

Why join JetBrains? 

• Strong base salary. We offer competitive pay that reflects your skills and experience.
• Flexible work location. Enjoy the freedom to work from home or from the office.
• Remote work. Spend up to 30 days per year working remotely from abroad.
• Extra time off. More days to relax, recharge, and do the things you love.
• Medical insurance allowance. Enjoy peace of mind for you and your family
• Learning and development opportunities. Access to conferences, courses, and language classes.
• Language classes. Pick up the local language or sharpen your English skills.
• Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays.
• Mental health support. To help you feel your best, we provide easy access to professional mental health services.
• Sports benefit. Enjoy an on-site gym or sports club stipend.
• Internal events. Join company-wide celebrations and team gatherings.

*Some benefits may vary depending on location.

#LI-DNI

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.

About Black Forest Labs

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX — foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

Post-training is where a foundation model becomes a product. In this role, you'll own the post-training pipeline for our multimodal models end to end — from data strategy and reward modeling to preference optimization, distillation, and safety tuning — across image, editing, and video. You'll drive measurable gains in model quality, build the infrastructure that lets the whole research team iterate fast, and push the state of the art in what it means to align a generative model to human intent.

This is a Staff / Senior IC role. We're looking for someone who has shipped post-training for a frontier model before and wants to do it again.

What You'll Work On

• Own the full post-training pipeline end to end — from data curation and reward modeling through fine-tuning, preference optimization, distillation, safety tuning, evaluation, and deployment
• Advance techniques across the post-training stack: SFT, RLHF, RLAIF, DPO, preference learning, and reward modeling to align models with human intent and aesthetic judgment
• Work across modalities: text-to-image, image editing, multi-reference, and video post-training
• Build personalization and customization capabilities that let users adapt our models to their own creative style
• Design and maintain high-throughput fine-tuning and evaluation infrastructure to support rapid iteration across the research team
• Identify quality and alignment gaps through rigorous evaluation, then close them through targeted research and engineering

What We're Looking For

• You've owned post-training for a frontier generative model through release (SFT, preference optimization (DPO or RLHF), distillation, safety tuning) with measurable quality wins on human prefs or standard benchmarks
• Deep experience across the post-training stack, not just one slice: reward modeling, preference learning, RLHF/RLAIF, and personalization
• Comfortable working across modalities: text-to-image, image editing, multi-reference, and ideally video
• Strong PyTorch fluency; you write research code that others can build on
• Experience with distillation (LADD, DMD, consistency models, or similar) or with building high-throughput eval pipelines is a strong plus
• Bias toward shipping: measurable model-quality improvements that reach users, not just papers

How We Work Together

We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

About Black Forest Labs

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX — foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

We're building the foundation models that power the next wave of visual intelligence — and pretraining is where that work begins. This role sits at the center of our research effort. You'll shape training objectives, architectures, data strategies, and systems behind our joint image, video, and audio foundation models, with a direct line from your research to products used by millions.

This is a Staff / Senior IC role. We're looking for someone who has already led pretraining at the frontier and wants to do it again.

What You'll Work On

• Lead large-scale pretraining experiments for our multimodal (image, video, audio) foundation models (architecture, objective functions, scaling strategies)
• Develop and evaluate novel ideas across architecture, optimizers, and training algorithms.
• Contribute across the full stack: low-level GPU and systems optimizations, research code, and high-level model design
• Lead focused research projects independently and drive larger cross-team initiatives

What We're Looking For

• You've led or co-owned pretraining for a foundation model (image, video, LLM, or multimodal) that shipped to production or a major release
• Own architectural calls that move the model: attention patterns, modulation schemes, loss formulations, tokenization strategies
• Deep experience with large-scale distributed training: FSDP/TP/PP, multi-node runs at 500+ GPUs, debugging loss spikes, NaNs, throughput regressions, and silent correctness issues at scale
• Strong intuition for architecture and objective design — you've made calls on attention patterns, modulation schemes, or loss formulations that moved a real model
• Track record of shipping: top-venue publications (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV) paired with production impact, or unambiguous production wins at a frontier lab
• Deep Python and PyTorch proficiency; comfortable reading and modifying low-level training code
• Familiarity with visual generative models is a must

How We Work Together

We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

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.

Role Description

• As a Senior Applied Value Engineering Consultant - Public Sector you are pushing the envelope in solving business-critical problems. You will be working with our most strategic customers understanding their objectives and key challenges, and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks. With Celonis’ Process Intelligence (PI) platform we feed operational context to AI so it understands our customers’ business and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers. This position will focus on our customers across the Public Sector, you’ll spearhead our mission to make processes work for citizens, government agencies and the planet, creating efficient, resilient processes which will shape a modern, citizen-oriented state. 

Key Responsibilities:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Value Engineers stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Value Engineers specialize in domains (e.g. supply chain) and industries

Requirements:

• 5+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.
• Excellent communication and presentation skills in English and German. The role requires a high level of fluency in German.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)
• Security Certification (SIcherheitsüberprüfung) up to SÜ2

About Black Forest Labs

We’re the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’re creating the generative models that power how people make images and video—tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we’re just getting started.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

We're looking for infrastructure engineers who want to work at peta-to-exabyte scale. You'll build the data systems behind the largest training runs on thousands of GPUs, where fixing one bottleneck lets researchers train the next breakthrough model.

What You’ll Work On

• Scalable data loaders for training runs across thousands of GPUs
• Efficient storage and retrieval systems for petabyte-scale datasets
• Multi-cloud object storage abstraction
• Execute large-scale data migrations across storage systems and providers
• Debug and resolve performance bottlenecks in distributed data loading

Technical Focus

• Python, PyTorch DataLoader internals
• Object storage (e.g. S3, Azure Blob, GCS)
• Parquet for metadata
• Video: ffmpeg, PyAV, codec fundamentals

What We’re Looking For

• Built and operated data pipelines at petabyte scale
• Optimized data loading
• Worked with petabyte-scale video and image datasets
• Written processing jobs operating on millions of files
• Debugged distributed system bottlenecks across large fleets of machines

Nice to have:

• Experience streaming dataset formats (e.g. WebDataset)
• Video codec internals and frame-accurate seeking
• Distributed systems experience
• Slurm and Kubernetes for job orchestration
• Experience with object storage performance tuning across providers

How We Work Together

We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

Base Annual Salary (SF based role) : $180,000–$300,000 USD + Equity 

About Black Forest Labs

We’re the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’re creating the generative models that power how people make images and video—tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we’re just getting started.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

You'll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn't about following established recipes—it's about running the experiments that clarify which architectural choices matter and which are less impactful.

What You’ll Work On

• Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters
• Rigorously ablates design choices—running experiments that isolate variables, control for confounds, and produce insights you can actually trust—then communicating those results to shape our research direction
• Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously
• Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough

What We’re Looking For

You've trained large-scale diffusion models and developed strong intuitions about what matters. You know that at research scale, every design choice has tradeoffs, and the only way to know which ones are worth making is through careful ablation. You're comfortable debugging distributed training issues and presenting research findings to the team.

You likely have:

• Hands-on experience training large-scale diffusion models for image and video data, with practical knowledge of common failure modes and what matters most in training
• Experience fine-tuning diffusion models for specialized applications—upscalers, inpainting, outpainting, or other tasks where understanding the domain matters as much as understanding the architecture
• Deep understanding of how to effectively evaluate image and video generative models—knowing which metrics correlate with quality and which are just convenient proxies
• Strong proficiency in PyTorch, transformer architectures, and the full ecosystem of modern deep learning
• Solid understanding of distributed training techniques—FSDP, low precision training, model parallelism—because our models don't fit on one GPU and training decisions impact research outcomes

We'd be especially excited if you:

• Have experience writing forward and backward Triton kernels and ensuring their correctness while considering floating point errors
• Bring proficiency with profiling, debugging, and optimizing single and multi-GPU operations using tools like Nsight or stack trace viewers
• Know the performance characteristics of different architectural choices at scale
• Have published research that contributed to how people think about generative models

How We Work Together

We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process.

Everything we do is grounded in four values:

• Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
• Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
• Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
• Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

If this sounds like work you’d enjoy, we’d love to hear from you.

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.

At JetBrains, code is our passion. Ever since we started back in 2000, we have been striving to make the world’s most robust and effective developer tools. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

We are working on an ambitious new platform that provides AI capabilities to all JetBrains products. Our platform is based on models developed in-house for writing and coding assistance, as well as integration with our strategic partners.

We are looking for a Research Engineer who can contribute to training foundation models for coding tasks. You’ll be working on developing Large Language Models from scratch and deploying them into production environments where they will be accessible by end users across the globe.

We value engineers who: 

• Can plan projects and make decisions independently, consulting with others if needed.
• Identify customer needs and prioritize their tasks accordingly.
• Start with the simplest solutions and gradually add complexity as needed.
• Take sole responsibility for an entire subsystem.
• Have a passion for learning and a desire to stay up to date with the latest developments in the LLM field.

In this role, you will: 

• Work with stakeholders to convert business requirements into technical specifications.
• Train LLMs from scratch on a large GPU cluster.
• Collect and process pre-training and fine-tuning datasets.
• Support and improve existing subsystems.

We’ll be happy to have you on our team if you have: 

• Experience in design, deployment, and support of production ML systems.
• A strong theoretical background in NLP and transformer-based approaches.
• Proficiency with modern deep learning frameworks such as PyTorch and common libraries for NLP.
• Experience in distributed training of multi-billion parameter models.
• Attention to detail in everything you do and great communication skills.

We’d be especially thrilled if you have experience with: 

• LLM inference frameworks such as vLLM, DeepSpeed, TensorRT.
• LLM alignment techniques such as RLHF/RLAIF.
• MLOps tools and practices, including CI/CD for ML.
• K8s and Kubeflow.
• Scientific publications in the NLP field.

How we develop JetBrains AI: 

• A cluster of hundreds of NVIDIA GPUs as training infrastructure.
• Git for source control management.
• Python, PyTorch, and HuggingFace as an ML stack.
• Kubeflow and Weights & Biases for experiment tracking.
• TeamCity as a CI Automation system.

#LI-KP1

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.

We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.

You’ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into JetBrains products.

As part of our team, you will:

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
• Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
• Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
• Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.
• Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.
• Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.
• Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features. 

We’ll be happy to bring you on board if you have:

• Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
• Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
• Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.
• At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.
• Large-scale data and training pipelines, e.g. MapReduce-style clusters, multi-node GPU training, or workloads on the order of 1M+ CPU/GPU hours.
• Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.
• AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.
• Inference optimization and serving optimized models in production.

#LI-KP1

At JetBrains, code is our passion. Ever since we started, back in 2000, we've been striving to make the strongest, most effective developer tools on earth. Today, AI-powered coding agents are becoming a core part of how developers write Kotlin – and we want to make sure they write it well.

The Kotlin AI Value Stream team is responsible for how AI agents understand, generate, and improve Kotlin code across all platforms: Android, Kotlin Multiplatform, server-side, web, desktop, and others. We build the evaluation infrastructure, error analysis tools, and post-training pipelines that measure and improve agent behavior on real Kotlin developer tasks.

As a Research Engineer on this team, you'll own the end-to-end loop: Analyze how agents fail on Kotlin → build evals that capture those failures → research and implement methods to fix them → measure the improvement. Your work will directly shape how millions of developers experience Kotlin through AI coding agents.

As part of our team, you will:

Build tools for agentic error analysis

• Design and implement tooling to systematically capture, classify, and analyse errors that AI coding agents make when generating Kotlin code.
• Build observability pipelines over agentic traces – mining patterns from agent sessions in JetBrains IDEs, Junie, Claude Code, Cursor, and other coding agents.

Build evaluation pipelines

• Design, implement, and maintain evaluation pipelines that measure Kotlin code generation quality across dimensions, including correctness, idiomaticity, build success, framework usage, and test coverage.
• Build simulation environments where coding agents can be measured on realistic Kotlin developer tasks – from greenfield KMP projects and Gradle dependency management to migrating Spring applications from Java to Kotlin.
• Own evaluation infrastructure: metrics, experiment tracking, automated regression checks, and reproducible benchmarking.

Research methods for improving agent and model behavior on Kotlin

• Experiment with post-training techniques (SFT, DPO, GRPO) to improve how models handle Kotlin-specific patterns, idioms, and frameworks.
• Investigate context engineering approaches: CLAUDE.md/AGENTS.md files, compiler-as-verifier feedback loops, Kotlin LSP integration, and MCP-based tooling.
• Run experiments to measure impact: A/B comparisons, benchmark suites, and before/after analyses on real codebases.
• Collaborate with model providers (Anthropic, OpenAI, and Google) to translate Kotlin-specific findings into model improvements.

Build public Kotlin benchmarks

• Design and build open-source benchmarks that measure AI coding agent performance on Kotlin tasks and eventually become the standard reference for the ecosystem.
• Create task datasets covering the breadth of Kotlin usage: the server side (Spring, Ktor), multiplatform projects (KMP), build systems (Gradle), Android, library development, and others.
• Include both mined real-world tasks and carefully designed synthetic tasks that test specific Kotlin capabilities.
• Maintain and evolve benchmarks as models improve, ensuring they remain challenging, relevant, and contamination-resistant.

We'll be happy to have you on board if you have:

• Hands-on experience building evaluation or analysis pipelines for LLMs or AI coding agents in a research or production setting.
• Strong Python engineering skills (at least three years), with the ability to write clean, maintainable code in data-heavy and ML-adjacent codebases.
• Experience with data analysis at scale: querying large datasets (SQL/Athena), building data pipelines, and performing statistical analysis of experimental results.
• The ability to own projects end to end – from identifying a problem in agent traces to designing an eval, running experiments, and shipping a fix.
• A product-aware mindset: You care about how agents are actually used by developers and can translate real failure modes into evaluation and training work.
• Familiarity with Kotlin or a strong willingness to develop deep Kotlin expertise (you'll be living in Kotlin codebases daily).

Our ideal candidate would also have experience with:

• Post-training LLMs: SFT, RLHF, DPO, GRPO – either hands-on training or designing the data and reward pipelines that feed into training.
• Modern deep learning frameworks (PyTorch) and LLM training stacks (TRL, verl, Megatron, or similar).
• AI agent development: tool-using agents, multi-step coding workflows, agentic frameworks.
• Evaluation frameworks and tools: Inspect AI, Promptfoo, LM-evaluation-harness, or custom eval pipelines.
• Experiment tracking and observability: Weights & Biases, MLflow, Langfuse, or similar.
• The Kotlin ecosystem: Android, Gradle, KMP, Spring, Ktor – with an understanding of the developer workflows that agents need to support.
• Contributing to or maintaining open-source projects, especially benchmarks or evaluation tools.

Don't check every box? That's okay – if you're excited about this work and bring strong fundamentals, we'd love to hear from you. We're happy to talk and provide the training you need to grow into the role.

Why join JetBrains? 

• Strong base salary. We offer competitive pay that reflects your skills and experience.
• Flexible work location. Enjoy the freedom to work from home or from the office.
• Remote work. Spend up to 30 days per year working remotely from abroad.
• Extra time off. More days to relax, recharge, and do the things you love.
• Medical insurance allowance. Enjoy peace of mind for you and your family
• Learning and development opportunities. Access to conferences, courses, and language classes.
• Relocation support. We help make your move as smooth and stress-free as possible. 
• Language classes. Pick up the local language or sharpen your English skills.
• Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays.
• Mental health support. To help you feel your best, we provide easy access to professional mental health services.
• Sports benefit. Enjoy an on-site gym or sports club stipend.
• Internal events. Join company-wide celebrations and team gatherings.

*Some benefits may vary depending on location.

#LI-DNI

At Inceptive, you will drive forward development that could help billions of people. To accomplish this, you will be part of a collaborative, antedisciplinary team building our biological software.

The design space of biomolecules is unimaginably vast — far beyond what can be explored experimentally. Yet within this space lie molecules with properties essential for new medicines. Our machine learning models learn to design therapeutic biomolecules with specific, desirable functions.

We advance the state of the art in molecular design by training large-scale foundation models and developing cutting edge generative approaches. The models learn from diverse heterogeneous datasets and are refined through focused fine-tuning and feedback from experiments. Key to progress is a team that combines exceptional machine learning expertise with thorough domain understanding.

You will collaborate closely with other machine learning researchers and engineers, as well as computational and experimental biologists, to advance these models and translate their capabilities into real therapeutic designs.

Your Mission, should you choose to accept it

• Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise
• Develop, implement, train, and iteratively improve state-of-the-art models for biomolecule design
• Analyze, visualize, and communicate results to support team efforts in improving models and data
• Create, deploy, and refine tools for efficient, reliable machine learning experimentation and production
• Work with biologists to collect data for the training and evaluation of generative models of biomolecules
• Provide mentorship and technical direction to team members as appropriate

Qualifications

• 3+ years of hands-on experience developing ML models
• Demonstrated track record of implementing, training, improving advanced machine learning models
• Highly capable programmer fluent in Python ecosystem and PyTorch or similar deep learning framework
• Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET
• Readiness to travel several times a year for company retreats and business events
• We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations

Preferred technical skills

• PhD in AI/ML, computer science, computational biology, physics, or a related field, or equivalent practical experience (e.g., industry experience, research, or advanced technical expertise)
• Strong skills in designing, executing, and documenting machine learning experiments
• Practical experience with modern generative models
• Strong software engineering skills, in particular for data processing, evaluation of ML models, compute cluster orchestration
• Experience with large-scale model training, foundation models, model parallelism, multi-node training
• Experience with bio sequence data and datasets — various genomic and protein data, sequencing, functional assays, etc
• Knowledge of biochemistry, molecular/cell biology, and drug development

Compensation

$200K – $275K + Bonus + Equity

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 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.

Wir bei Capgemini Invent glauben, dass unterschiedliche Perspektiven den Wandel vorantreiben. Als innovative Transformationsberater vereinen wir unsere strategischen, kreativen und wissenschaftlichen Fähigkeiten und arbeiten eng mit unseren Kunden zusammen, um zukunftsweisende Lösungen zu entwickeln. Hilf uns dabei, den Wandel voranzutreiben, der auf die Herausforderungen unserer Kunden von heute und morgen zugeschnitten ist. Informiert und validiert durch Wissenschaft und Daten. Verstärkt durch Kreativität und Design. Und das alles auf der Grundlage von Technologien.

Deine Rolle

• Arbeite in einem Team aus erfahrenen KI-Experten im Umfeld unserer AI Labs, unserem Innovationslabor für KI-Anwendungen
• Dich erwarten innovative Projekte zur Daten- und KI-Transformation in verschiedenen Unternehmensbereichen aus allen Branchen
• Du entwickelst innovative Machine Learning & Advanced Analytics Anwendungen mit Business Impact von der Strategie über den Prototyp bis zur produktiven Lösung
• Du wirkst an der Weiterentwicklung unseres Capgemini AI Offerings mit und kannst eigene Ideen in unseren Labs und Hackathons umsetzen

Dein Profil

• Du hast einen exzellenten Hochschulabschluss (Bachelor/Master) in einem technischen oder naturwissenschaftlichen Studiengang (z.B. Betriebswirtschaftslehre, Wirtschaftsinformatik, Data Science oder Wirtschaftsingenieurwesen oder verwandte Studiengänge) und/oder hast ggf. bereits als Business Analyst Erfahrung gesammelt
• Du hast erste fundamentale Erfahrung in der Beratungsbranche gesammelt oder vergleichbare Aufgaben in anderen Kontexten mit Kunden bereits übernommen
• Du bringst erste praktische Projekterfahrung in AI Implementierung und Skalierung, mit Datenbanken und der agilen Software-Entwicklung
• Du möchtest sowohl strategisch Denken als auch technisch umsetzen
• Du beherrschst einen breiten Tech-Stack um eigenständig (Agentic) AI-Anwendungen zu implementieren (z.B. Keras, PyTorch, Numpy, Spark, Kafka, NoSql, AWS, Azure, GCP, Pyomo, GAMS, Linopy)
• Du bringst ausgeprägte Präsentations- und Moderationsfähigkeiten sowie sehr gute MS-Office-Kenntnisse mit
• Du bist stolz auf deine hohe Zuverlässigkeit, deinen Teamgeist und identifizierst dich mit den Werten und dem Ansatz von Capgemini
•  
• Du besitzt exzellente Deutsch (mindestens C1) - und Englischkenntnisse und zeigst Flexibilität sowie Reisebereitschaft

Was du bei uns schätzen wirst

• Werde Teil einer international führenden Unternehmensberatung, die durch innovative und wegweisende Projekte, globale Teams und digitales Know-How die Zukunft gestaltet
• Bekomme klare Perspektiven für deine Karriere und Freiraum für Eigeninitiative
• Erhalte individuelle Entwicklungsmöglichkeiten
• Erlebe unser kontinuierliches Engagement für Chancengleichheit, Vielfalt und Inklusion
• Profitiere von unseren Benefits zur optimalen Work-Life-Balance mit attraktiven Angeboten rund um Gesundheit & Wellbeing, Zusatzpaketen rund um Mobilität sowie modernen Offices

Über Capgemini

Capgemini ist ein globaler Business- und Technologie-Transformationspartner für Organisationen. Das Unternehmen unterstützt diese bei ihrer dualen Transformation für eine stärker digitale und nachhaltige Welt – stets auf greifbare Fortschritte für die Gesellschaft bedacht. Capgemini ist eine verantwortungsbewusste, diverse Unternehmensgruppe mit einer über 55-jährigen Geschichte und 340.000 Mitarbeitenden in mehr als 50 Ländern. Kunden vertrauen auf Capgemini, um das Potenzial von Technologie für die ganze Breite ihrer Geschäftsanforderungen zu erschließen. Capgemini entwickelt mit seiner starken Strategie, Design- und Engineering-Expertise umfassende Services und End-to-End-Lösungen. Dabei nutzt das Unternehmen seine führende Kompetenz in den Bereichen KI, generative KI, Cloud und Daten sowie profunde Branchenexpertise und sein Partner-Ökosystem. Die Gruppe erzielte 2024 einen Umsatz von 22,1 Milliarden Euro.

Wir freuen uns über deine vollstän­dige Be­wer­bung ausschließlich über unser Bewerbungsportal und unter Angabe deiner Ge­halts­vor­stel­lung, des frühest­mög­lichen Ein­tritts­zeit­punkts sowie deiner Standortpräferenz.

Die ausgeschriebene Vakanz ist ganzjährig zu besetzen.

Hier findest du weitere Informationen zu unserem Bewerbungsprozess.

Wende dich bei Fragen bitte an wioleta.jonas@capgemini.com.

Wir bei Capgemini Invent glauben, dass unterschiedliche Perspektiven den Wandel vorantreiben. Als innovative Transformationsberater vereinen wir unsere strategischen, kreativen und wissenschaftlichen Fähigkeiten und arbeiten eng mit unseren Kunden zusammen, um zukunftsweisende Lösungen zu entwickeln. Hilf uns dabei, den Wandel voranzutreiben, der auf die Herausforderungen unserer Kunden von heute und morgen zugeschnitten ist. Informiert und validiert durch Wissenschaft und Daten. Verstärkt durch Kreativität und Design. Und das alles auf der Grundlage von Technologien.

Deine Rolle

• Übernimm Verantwortung in einem Team aus erfahrenen KI-Experten im Umfeld des Industrial AI Labs, unserem Innovationslabor für KI-Anwendungen in der Industrie.
• Nutze deine Technologie- und Businessexpertise um die KI-Transformation von Industrieunternehmen wirkungsvoll voranzutreiben.
• Verantworte innovative (Teil-)Projekte zur strategischen Daten- und KI-Transformation entlang der industriellen Wertschöpfungskette insbesondere in Forschung- und Entwicklung, Einkauf, Logistik, und Produktion.
• Setze industrielle KI Use Cases eigenständig von der Konzeption bis zum Proof-of-Concept in unserem Industrial AI Lab um.
• Entwickle zukunftsfähige Daten und KI-Strategien für unsere Industriekunden
• Konzeptioniere und betreue innovative Daten und KI-Beratungsangebote für Industriekunden.
• Sammle erste oder vertiefe Führungserfahrung im Rahmen von Projekt- oder Teamleitungsaufgaben.

Dein Profil

• Abschluss in einem durch quantitative Inhalte geprägten Studiengang, wie zum Beispiel MINT, Wirtschaftsinformatik, (empirische) Psychologie, Statistik/Data Science.
• Beratungshintergrund, einschlägige Industrieerfahrung oder Promotion in einem relevanten Themenbereich.
• Mehrjährige Projekterfahrung in Transformations- oder Forschungsprojekten oder der Konzeption und Umsetzung innovativer AI-Lösungen.
• Idealerweise praktische Projekterfahrungen in mindestens einem der folgenden Bereiche:
  • Data Science und KI mit relevanten Technologien, wie z.B. Python Data Science Stack (Keras, PyTorch, Numpy, etc.), Big Data (Spark, Kafka, NoSql) oder entsprechenden Hyperscaler (AWS, Azure, GCP) Daten & KI Services.
  • Softwarearchitektur und -Engineering im Kontext von Enterprise-Systemen, insbesondere Design Patterns, DevOps, Security und Systemintegration.
  • Industrielle OT und IT Systeme und Technologien wie z.B. OPC-UA, Asset Administration Shell, industrielle Leittechnik (SCADA, DCS, PLS), PLM, CAD, CMMS oder MES.
  • Industriellen IoT- und Datenplattformen wie z.B. Insights Hub, Genix, Vantara, Thingworx oder Cumulocity IoT.
  • Operations Research / Decision Optimization inkl. Kenntnissen von relevanten Tools zur Modellierung (Pyomo, GAMS, Linopy, AMPL etc.) und Lösung (Gurobi, CPLEX, HiGHS, Google OR Tools etc.) von vorzugsweise ganzzahligen Optimierungsproblemen.
  • Modellierung und Simulation in Automotive oder Manufacturing & High Tech (Prozessindustrie, Life Science, Tier 1 Zulieferer, Aerospace).
• Spaß an der Kombination von strategischer Analyse und technischer Umsetzung.
• Ausgeprägte Präsentations- und Moderationsfähigkeiten sowie sehr gute MS-Office-Kenntnisse.

Was du bei uns schätzen wirst

• Innovative Projekte und internationale Teams sowie ein erfolgreiches Wachstum in digitalen Zukunftsthemen
• Die Aufgabe, Strategie- und Transformationsprojekte zu konzipieren und zum Erfolg zu führen
• Langjährig gelebte, auf Werte ausgerichtete Unternehmenskultur
• Transparente Karriereperspektiven und Weiterbildungsmöglichkeiten innerhalb eines weltweiten Expertennetzwerks
• Langfristig ausgerichtetes Work-Life-Balance-Modell

Über Capgemini

Capgemini ist ein globaler Business- und Technologie-Transformationspartner für Organisationen. Das Unternehmen unterstützt diese bei ihrer dualen Transformation für eine stärker digitale und nachhaltige Welt – stets auf greifbare Fortschritte für die Gesellschaft bedacht. Capgemini ist eine verantwortungsbewusste, diverse Unternehmensgruppe mit einer über 55-jährigen Geschichte und 340.000 Mitarbeitenden in mehr als 50 Ländern. Kunden vertrauen auf Capgemini, um das Potenzial von Technologie für die ganze Breite ihrer Geschäftsanforderungen zu erschließen. Capgemini entwickelt mit seiner starken Strategie, Design- und Engineering-Expertise umfassende Services und End-to-End-Lösungen. Dabei nutzt das Unternehmen seine führende Kompetenz in den Bereichen KI, generative KI, Cloud und Daten sowie profunde Branchenexpertise und sein Partner-Ökosystem. Die Gruppe erzielte 2024 einen Umsatz von 22,1 Milliarden Euro.

Wir freuen uns über deine vollstän­dige Be­wer­bung ausschließlich über unser Bewerbungsportal unter Angabe Ge­halts­vor­stel­lung, des frühest­mög­lichen Ein­tritts­zeit­punkts sowie deiner Standortpräferenz.

Die ausgeschriebene Vakanz ist ganzjährig zu besetzen.

Hier findest du weitere Informationen zu unserem Bewerbungsprozess.