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 are seeking a customer-obsessed Solutions Architect to lead technical engagement with Nebius customers across Europe. This role is focused on presales excellence, strong technical storytelling, and driving adoption of Nebius AI Cloud across a wide range of industry segments.

As a Solutions Architect, you will act as a trusted technical partner to customers, helping them understand, evaluate, and adopt Nebius for their AI/ML and high-performance computing workloads. You will combine deep technical expertise with strong communication skills to clearly articulate Nebius’ value proposition and differentiate our platform in a competitive market.

You’re welcome to work remotely from Europe.

Your responsibilities will include:

• Own end-to-end technical presales engagements, from discovery through solution design, PoC execution, and production readiness
• Serve as a trusted technical advisor to customer stakeholders, including CTOs, Heads of AI/ML, and platform engineering teams
• Deliver compelling technical presentations, demos, and workshops that clearly communicate the value of Nebius across different customer segments
• Execute demos and PoCs to demonstrate performance, scalability, and cost efficiency of Nebius solutions
• Translate customer requirements into scalable, production-ready architectures and best practices
• Collaborate closely with Sales, Product, and Engineering to align customer needs with Nebius capabilities and roadmap
• Act as a technical advocate for customers internally, ensuring feedback is incorporated into product and go-to-market strategy
• Support regional go-to-market efforts, including events, meetups, webinars, and technical content creation
• Contribute to building repeatable technical playbooks and assets for the European market

We expect you to have:

• 2–5+ years of experience in technical roles such as Solutions Architect, Cloud Architect, Sales Engineer, or similar, with strong customer-facing exposure
• Proven experience in presales or customer-facing technical roles, including discovery, solution design, and PoC execution
• Strong ability to deliver technical pitches and clearly explain complex infrastructure and AI/ML concepts to both technical and business audiences
• Experience positioning cloud or infrastructure solutions and articulating value propositions in competitive environments
• Solid understanding of ML/AI workloads, including training and inference, and the underlying infrastructure requirements
• Experience with cloud platforms, Kubernetes, and distributed systems
• Hands-on experience with Infrastructure as Code (e.g., Terraform) and scripting/programming (Python preferred)
• Strong communication and storytelling skills, with the ability to adapt messaging across different customer segments
• Deep understanding of the European market, including customer expectations, buying behaviors, and local ecosystem
• Fluency in English plus another language preferred (both written and spoken), with the ability to engage confidently with local customers and stakeholders
• A highly customer-centric mindset, with the ability to balance technical depth and business value

It will be an added bonus if you have:

• Experience with GPU-accelerated workloads and AI infrastructure (NVIDIA ecosystem, CUDA, etc.)
• Hands-on experience with MLOps tools and frameworks (Kubeflow, MLflow, etc.)
• Familiarity with HPC environments and schedulers (e.g., Slurm)
• Experience contributing to regional go-to-market or developer/community initiatives

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

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 an inference platform that makes every kind of foundation model — text, vision, audio, and emerging multimodal architectures — fast, reliable, and effortless to deploy at massive scale. To deliver on that promise, we need an engineer who can make the platform behave flawlessly under extreme load and recover gracefully when the unexpected happens.

In this role you will own the reliability, performance, and observability of the entire inference stack. Your day starts with designing and refining telemetry pipelines — metrics, logs, and traces that turn hundreds of terabytes of signal into clear, actionable insight. From there you might tune Kubernetes autoscalers to squeeze more efficiency out of GPUs, craft Terraform modules that bake resilience into every new cluster, or harden our request-routing and retry logic so even transient failures go unnoticed by users. When incidents do arise, you’ll rely on the automation and runbooks you helped create to detect, isolate, and remediate problems in minutes, then drive the post-mortem culture that prevents recurrence. All of this effort points toward a single goal: scaling the platform smoothly while hitting aggressive cost and reliability targets.

Success in the role calls for deep fluency with Kubernetes, Prometheus, Grafana, Terraform, and the craft of infrastructure-as-code. You script comfortably in Python or Bash, understand the nuances of alert design and SLOs for high-throughput APIs, and have spent enough time in production to know how distributed back-ends fail in the real world. Experience shepherding GPU-heavy workloads — whether with vLLM, Triton, Ray, or another accelerator stack — will serve you well, as will a background in MLOps or model-hosting platforms. Above all, you care about building self-healing systems, thrive on debugging performance from kernel to application layer, and enjoy collaborating with software engineers to turn reliability into a feature users never have to think about.

If the idea of safeguarding the infrastructure that powers tomorrow’s multimodal AI energizes you, we’d love to hear your story.

Customer experience

Customer experience at Nebius 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 looking for a Partner Solutions Architect to serve as the technical interface between Nebius and our strategic technology partners, ranging from data platforms and MLOps vendors to AI frameworks and ISVs that build on GPU infrastructure. 

This is a hands-on engineering and integration role. You will design and develop integrated solutions, build reference implementations, enable partner engineering teams, and ensure joint customers succeed with combined offerings. You will influence Nebius’ product roadmap and drive deep technical collaboration across partner ecosystems.

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

Your responsibilities will include: 

• Own the technical relationship with strategic partners as the primary interface to Nebius engineering and product
• Design and architect high-impact integration solutions, delivering clear joint customer value
• Build and maintain reference implementations and production-grade integrations
• Define enterprise-ready integration patterns (networking, security, compliance, observability)
• Translate partner and customer feedback into actionable product and roadmap requirements
• Enable partner teams through technical documentation, training, and demo environments
• Provide technical leadership in joint customer deployments, architecture reviews, and complex troubleshooting

We expect you to have: 

• 5+ years in solutions architecture, partner engineering, or technical pre-sales at cloud or data platform companies
• Strong expertise in cloud infrastructure
• Hands-on experience with GPU-accelerated AI/ML workflows (training, inference, MLOps)
• Proven background in integration architecture (API design, data pipelines, security, observability)
• Experience with Python and IaC tools
• Excellent communication skills with the ability to engage technical and non-technical stakeholders

It will be an added bonus if you have: 

• Track record of building or scaling partner technical programs or ecosystems
• Experience working with or at Snowflake, Databricks, AWS, Azure, or GCP
• Background in ML lifecycle (training pipelines, fine-tuning, inference optimization, RAG

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) 

We are looking for a Staff Data Scientist to join Wolt’s mission in Finland, Sweden, or Germany!

About the role

The Selection Analytics Team at Wolt owns the data and infrastructure that helps all 32 of our country teams build a portfolio of high-quality restaurant and retail stores. Our overarching mission is to provide customers with the best selection by enabling sales teams across all markets to acquire the best merchants.

To do this, we work directly with country sales teams to help them identify and prioritize the highest potential merchant candidates. We also work with Wolt leadership by providing metrics to track selection quality, upon which they develop strategic initiatives and goals. This is a lean and mighty team that owns and operates the engine for merchant acquisition globally.

What you'll be doing

As a senior member of the Selection Analytics team, your role involves leveling up our approach to identifying and prioritizing better merchants. You’ll achieve this by:

• Developing and deploying production-level ML models
• Crafting metrics to unlock deeper insights into merchant selection quality for all markets
• Conducting experiments to understand the impact of selection on specific markets
• Work on self-serve tooling to track these insights.

This is also a rare, high-exposure experience where you will then use your findings to work directly with country leadership to help them uncover growth opportunities. When you join this team, you will be driving action from the frontlines, rather than just crunching numbers and creating dashboards.

Our humble expectations

• 5+ years of experience in data science, consulting, or a related quantitative role
• Fluent in Python & SQL
• Strong exploratory data analysis skills, willingness to look under the hood
• Exposure to experimental design (A/B testing) and key statistical methods
• Experience in feature engineering, model design, evaluation, diagnostics, and monitoring
• Knowledge of MLOps concepts and tools (e.g. MLflow, Flyte)
• Determination to initiate and lead/own strategic projects to completion with a cross-functional team
• Innate curiosity to understand how things work and a first-principles approach to problem solving

What we offer

• The online delivery platform that Wolt is building will offer you interesting and complex challenges together with the opportunities to create a big impact with your skills. Lots of geolocational and temporal data in real-time combined with differences in the economics and dynamics of the cities we operate in make Wolt both a challenging and so interesting company to practice analytics. 
• You can work onsite at our offices in Helsinki, Stockholm, or Berlin. Alternatively, you can work remotely anywhere in Finland, Sweden, or Germany. You have the chance to decide the ways of working — a hybrid, at the office, or remote, within the location above. 💙 
• You would get to work in a company culture where we take ownership beyond the obvious, do common things uncommonly well, we think big but stay humble, do right by people, we treat others kindly and justly, recognize that if we don’t learn, we won’t stay still but fall behind and keep in mind that Luke was Yoda’s greatest achievement.

Next steps

The position will be filled as soon as we find the right person, so make sure to apply as soon as you realize you really, really want to join us! 

For any further questions about the position, you can turn to the Product+ Talent Acquisition Partner - Zhanna Filintseva (zhanna.filintseva@wolt.com)

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 design, deploy, and maintain the ML infrastructure backbone that makes frontier AI research possible. This isn't abstract systems work—every decision you make directly impacts whether a multi-week training run succeeds, whether inference stays fast enough for production, whether researchers can iterate quickly or wait hours for resources.

What You’ll Work On 

You'll be the person who:

• Designs, deploys, and maintains cloud-based ML training clusters (Slurm) and inference clusters (Kubernetes) that researchers and products depend on
• Implements and manages network-based cloud file systems and blob/S3 storage solutions optimized for ML workloads at scale
• Develops and maintains Infrastructure as Code (IaC) for resource provisioning—because manual configuration doesn't scale and configuration drift breaks things
• Implements and optimizes CI/CD pipelines for ML workflows, making it easy for researchers to go from experiment to production
• Designs and implements custom autoscaling solutions for ML workloads where standard approaches fall short
• Ensures security best practices across the ML infrastructure stack without creating friction that slows down research
• Provides developer-friendly tools and practices that make ML operations efficient—because infrastructure that's hard to use doesn't get used

What We’re Looking For

You've built and managed ML infrastructure at scale and understand that supporting AI research is fundamentally different from traditional cloud infrastructure. You've been paged because a training run failed. You've debugged why storage became the bottleneck. You know the difference between infrastructure that works in demos and infrastructure that works when researchers depend on it for months-long experiments.

You likely have:

• Strong proficiency in cloud platforms (AWS, Azure, or GCP) with focus on ML/AI services—you know which services matter and which are marketing
• Extensive experience with Kubernetes and Slurm cluster management in production environments
• Expertise in Infrastructure as Code tools (Terraform, Ansible, etc.) and the discipline to actually use them
• Proven track record managing and optimizing network-based cloud file systems and object storage for ML workloads
• Experience with CI/CD tools and practices (CircleCI, GitHub Actions, ArgoCD, etc.) in ML contexts
• Strong understanding of security principles and best practices in cloud environments—without making security the enemy of velocity
• Experience with monitoring and observability tools (Prometheus, Grafana, Loki, etc.) that help you understand what's actually happening
• Familiarity with ML workflows and GPU infrastructure management—you understand what researchers need
• Demonstrated ability to handle complex migrations and breaking changes in production environments without losing data or breaking experiments

We'd be especially excited if you:

• Have experience building custom autoscaling solutions for ML workloads that standard tools can't handle
• Bring knowledge of cost optimization strategies for cloud-based ML infrastructure (because GPU hours add up)
• Are familiar with MLOps practices and tools
• Have experience with high-performance computing (HPC) environments
• Understand data versioning and experiment tracking for ML
• Know network optimization techniques for distributed ML training
• Have worked with multi-cloud or hybrid cloud architectures
• Are familiar with container security and vulnerability scanning tools

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: $180,000–$300,000 USD

We're based in Europe and value depth over noise, collaboration over hero culture, and honest technical conversations over hype. Our models have been downloaded hundreds of millions of times, but we're still a ~50-person team learning what's possible at the edge of generative AI.

About the Role

The ideal candidate will be naturally collaborative, articulate, extremely organized, have a solid technical understanding of Veeam products, and motivated by maximizing customer success and outcomes. Soft skills combined with technical skills are key in this role. You will partner with Customer Success Engineers (CSEs) and Account Executives (AEs) to drive customer outcomes across security-related products and use cases within the Veeam Data Platform (VDP). You’ll lead readiness checks, data modeling, and risk conversations with CISO/CIO stakeholders, while monitoring telemetry and maturity against the Veeam Data Resilience Maturity Model (DRMM) to optimize posture and identify expansion opportunities.

What You’ll Do

• Engage customers on security-related products, architectures, and risk topics across the Veeam Data Platform (VDP).  
• Run readiness checks and lead data modeling to validate solution design and accelerate decisions.  
• Monitor attack surfaces and vulnerabilities (including DRMM scoring), track telemetry or recurring inspection signals, report trends, and capture potential health checks.  
• Validate designs to de-risk adoption and accelerate time to value.  
• Identify and articulate expansion opportunities; review consumption trends and schedule checkpoint reviews (with or without AE coordination).  
• Engage CISO/CIO stakeholders for risk, status, and opportunity discussions; synthesize inputs from account health and CSE-led QBRs.  
• Support AEs on security- and AI-driven expansion motions; influence roadmap priorities with CSE counterparts.  
• Operate as a pooled resource covering Enterprise and Commercial-Named accounts (generally $100K+ ARR), typically at a 1 Security Success Engineer to 6–8 CSE coverage ratio.  
• Note: Not the primary owner for Onboarding motions nor directly responsible for Renewals

What You’ll Bring

• 5+ years of experience in engineering architecture for cybersecurity-related products (e.g., Security Engineer/Architect, Cloud Solution Architect, MLOps/ML Engineer).  
• Bachelor’s degree in Computer Science, Electrical Engineering, or a related technical field; advanced degree is a plus.  
• Relevant certifications (e.g., CompTIA Security+, CISSP, or equivalent).  
• Expertise in data security and governance, including DSPM/DLP; familiarity with AI/ML architectures.  
• Demonstrated ability to engage CISO/CIO stakeholders on risk, resilience, and modernization roadmaps.  
• Hands-on experience with solution design, POCs, telemetry monitoring, and maturity modeling (DRMM familiarity is a plus).  
• Strong communication, stakeholder management, and cross-functional collaboration skills.  
• VMCE certification (can be completed after joining)

What You’ll Get 

• Statutory benefits: healthcare coverage, occupational accident insurance, and unemployment cover included.
• State pension: contributions to the public pension system are covered in line with local rules (18.6% of salary, capped at €5,800/month).
• Accident insurance: 24/7 worldwide coverage, including disability and death benefits and rescue cost coverage.
• Supplementary pension plan: we contribute €302 net per month to a group pension plan; you can top it up, and your contributions are tax-exempt (you can also transfer an existing plan into ours).
• Time off: 30 days of annual leave.

Please note: If the applicant is permanently located outside of Germany, Veeam reserves the right to decline the application for the position. Remote work is only possible for employees located in Germany. 

#LI-MP1

Technical Account Managers are highly experienced Architects who bring both deep technical expertise and strong interpersonal skills to support a small portfolio of strategic clients (approximately 3 per TAM). They must be quick on their feet and able to put a positive spin on challenging customer situations, both in the boardroom with a client CTO and while sharing the command line with a client admin. They must be effective technically, both as communicators and doers. They must be capable of managing and maintaining a client relationship, while keeping a tight organizational watch over the technical aspects of their accounts.

How you'll make an impact

• Collaborate with the customer and the internal account team to jointly identify short and long-term priorities and develop the associated engagement plan
• Manage project milestones in partnership with Customer Success Managers and clients, contribute to deliverables, provide regular status updates and proactively identify and mitigate issues and risks
• Serve as a central point for client technical information and contribute to the account strategy alongside the broader account team
• Support the growth and effectiveness of the Field Engineering team through documentation, process improvements, and knowledge sharing
• Act as the primary technical advisor for client teams, providing guidance and hands-on support across a range of areas, including:
  • Dataiku platform architecture and deployment
  • Platform operations and upgrades
  • Best practices for platform usage
  • Security, data management, and compute resources
  • ML-Ops, monitoring, and scaling strategies
• Assist clients in integrating the product into their systems and troubleshoot technical challenges
• Capture client feedback and feature requests to inform the Product and Engineering teams
• Advise client tech leaders on complementary technologies and long-term technical strategy.
• Explore and support advanced use cases involving Dataiku, such as edge computing, deep learning, and MLOps

What you'll need to be successful

• 7+ years of experience in a customer-facing technical role
• Native or bilingual proficiency in German
• Strong communication and client relationship skills
• Experience supporting both pre- and post-sales engagements
• Proficiency in Linux system administration, including networking
• Experience with identity and access management tools (e.g., LDAP, Kerberos, Active Directory, IAM)
• Hands-on experience with cloud platforms (AWS, Azure, GCP)
• Hands-on experience with the Kubernetes ecosystem for setup, administration, troubleshooting and tuning
• Familiarity with the Hadoop and/or Spark ecosystems

How you'll stand out 

• Experience with Python
• Data-Science knowledge
• Basic knowledge of Java
• Familiarity with ML-Ops practices and tools

What does the hiring process look like? #LI-Hybrid #LI-AN1

• Initial call with a member of our Technical Recruiting team
• Video call with the TAM Hiring Manager
• Technical Assessment to show your skills (Home Test)
• Debrief of your Tech Assessment with TAM Team members
• Final Interview with the VP Field Engineering

#LI-JR1

About Buynomics:  

Buynomics is the next-generation SaaS company in pricing and product optimization. It employs large-scale simulation technologies to digitize customers and predict their purchase behavior - in any environment. Our clients use Buynomics to make data-driven decisions about their current and new products, overall portfolio structure, pricing, promotions, trade terms, and more, 100x faster and more precisely than every other solution.

Our mission is to enable transparent, customer-centric commercial decisions. We’re building the operating system for commercial strategy, providing companies with a single source of truth for all market-facing questions related to pricing, products, and promotions.

About the role:  

Buynomics is building a modular, service-oriented platform to power advanced commercial decision-making for enterprise customers.

As we expand from multi-tenant SaaS into enterprise single-tenant deployments, we are looking for a Lead Platform Engineer to design and build our multi-cloud Kubernetes foundation.

This role focuses on:

• Secure single-tenant deployments in customer-controlled environments (Azure-first)
• Kubernetes-based service architecture across clouds
• Scalable infrastructure for distributed compute and ML workloads
• Infrastructure automation, security, and operational excellence

You will operate as a senior technical leader, defining the deployment architecture that enables our next-generation platform to run reliably across AWS, Azure, and GCP.

Why this role:

• Greenfield architecture: You are not inheriting legacy — you are designing the deployment platform from scratch
• Enterprise impact: Your work deploys into Fortune 500 customer environments across industries
• Real autonomy: You make architectural decisions, choose tooling, and own the platform roadmap
• AI-native engineering culture: We are all-in on AI-assisted development — you will work alongside a team that ships with AI daily
• Growth path: As enterprise deployments scale, this role grows into leading a dedicated platform team

What you'll do:  

• Design and implement cloud-agnostic, single-tenant deployment architecture (Azure-first, with AWS and GCP support)
• Build and maintain reusable Infrastructure-as-Code modules (Terraform)
• Define and standardize the Kubernetes platform, including workload isolation, auto-scaling, and security policies
• Implement GitOps-based CI/CD and automated environment provisioning
• Enable scalable execution of compute-heavy workloads (e.g. async job queues, ML training pipelines)
• Design secure networking patterns (private endpoints, VNet/VPC design, DNS, IAM/RBAC, zero-trust principles)
• Establish observability standards (structured logging, metrics, distributed tracing, alerting)
• Own cost governance across customer environments: cost attribution, right-sizing, and spend optimization
• Collaborate with engineering teams to define clean service deployment contracts
• Define platform standards, architectural decision records, and operational runbooks
• Ensure platform compliance with SOC 2, ISO 27001, and GDPR through automated policy-as-code guardrails
• Champion AI-assisted infrastructure workflows — we use AI coding tools across engineering and expect platform tooling to follow suit

What you'll need:

• 6+ years of experience in platform engineering, infrastructure, or DevOps/SRE roles
• Strong production experience in Azure (AKS, Azure AD, Managed Identity, Private Networking)
• Solid experience with at least one additional cloud provider (AWS preferred; GCP a plus)
• Hands-on production experience operating Kubernetes at scale (cluster lifecycle, upgrades, multi-tenancy)
• Deep expertise in Infrastructure-as-Code (Terraform), including module design, state management, and CI integration
• Strong knowledge of cloud networking (VPC/VNet, DNS, private routing, firewall configurations)
• Experience designing CI/CD and GitOps workflows
• Experience operating distributed or high-concurrency workloads
• Understanding of compliance frameworks (SOC 2, ISO 27001, GDPR) and ability to implement compliant infrastructure patterns
• Strong system design skills and ability to operate at Staff-level technical depth
• Ability to navigate ambiguity and move initiatives forward independently

Nice-to-Haves:

• Experience deploying software into customer-controlled enterprise cloud environments (single-tenant / BYOC models)
• MLOps experience (model lifecycle, training pipelines, experiment tracking with MLflow, artifact versioning)
• Databricks integration experience (workspace provisioning, Unity Catalog, job orchestration)
• GPU workload optimization and scheduling
• Experience building Internal Developer Platforms using tools like Backstage, Crossplane, or Humanitec
• Service mesh technologies (Istio, Linkerd) and zero-trust networking patterns
• Policy-as-code tooling (OPA/Gatekeeper, Kyverno)
• FinOps practices and multi-environment cost governance

What we offer:

• Dynamic Environment: Join a truly international and diverse team where your ideas matter.
• Flexible Work Model: Enjoy a remote setup with offices located in Cologne.
• Learning Budget: Take advantage of our employee development and education budget to enhance your skills.
• Local Language Classes: Participate in language lessons to support your integration and personal development.
• Urban Sports Club Membership
• Deutschlandticket
• Personal and professional growth opportunities
• Regular company events 

To ensure a smooth and efficient process and that no applications are overlooked, please apply directly to the role. While we'd love to respond to everyone who reaches out, applying ensures that your application is properly tracked and considered.

Buynomics is proud to be an equal-opportunity employer. This means that we don’t discriminate based on race or ethnic origin, color, the language(s) you speak, where you (or your parents) are from, or whether or not you consider yourself to have a disability. Neither will your age, gender, gender identity, sexual orientation, religion, beliefs, or political opinions play a part in your application with us. We’re a diverse team in so many ways, and we love it that way.

#LI-Remote

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.

Spectrum is a resident of JetBrains' startup incubator, with startup speed and autonomy, and backed by 25 years of developer tooling expertise. We are looking for a top-class ML Engineer who will help us shape the future of software development. You will own our AI and ML engineering stack and help define the research agenda for our team. Your technical vision and design decisions will directly shape the product and determine its success.

Your responsibilities will include:

• Designing and building the ML/LLM solution for data ingestion, knowledge extraction, retrieval, and subsequent reasoning.
• Creating the datasets, metrics, and pipelines that drive measurable improvements across the system.
• Architecting and improving agents for context retrieval, knowledge extraction, and data alignment, which includes prompt engineering, model selection, and inference optimization.
• Establishing MLOps practices, including orchestration, observability, and experiment tracking.
• Collaborating with the engineering team on system design and with JetBrains Research on the research agenda.
• Defining hiring criteria, growing the ML team, and shaping the ML team culture.

We expect you to have:

• A proven track record as an ML/AI Lead.
• At least five years of experience in ML/AI systems, with at least two years focused on LLMs and generative AI.
• A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches.
• Hands-on experience with:
• Prompt engineering and LLM pipeline design, including evaluation.
• Agentic frameworks such as LangChain, LlamaIndex, LangSmith, smolagents, or an equivalent.
• Vector databases and retrieval-augmented generation (RAG) patterns.
• Deploying and scaling LLM-powered applications using APIs (e.g. OpenAI or Anthropic) or open-source models.
• Strong Python skills – Kotlin knowledge would be a plus.
• 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 have:

• Experience with ontologies, knowledge graphs, or graph-based reasoning.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• The ability to think strategically about product-led AI, beyond just training models in isolation.
• A background in code analysis, developer tools, or software engineering research.
• Experience with multi-agent systems or complex agentic workflows.
• Actively contributed to relevant open-source projects or publications.

What we offer

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.

#LI-KP1

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 AI/ML Engineer to build and evolve the ML-powered systems at the heart of our product.

Your responsibilities will include:

• Designing and building the ML/LLM solution for data ingestion, knowledge extraction and retrieval, and subsequent reasoning.
• Creating datasets, metrics, and pipelines that drive measurable improvements across the system.
• Architecting and improving AI agents, which will involve prompt engineering, model selection, and inference optimization.
• Contributing to our MLOps practices, including orchestration, observability, and experiment tracking.

We expect you to have:

• At least four years of experience building ML/AI systems in production environments, with hands-on LLM experience.
• A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches.
• Hands-on experience with:
• Prompt engineering and LLM pipeline design, including evaluation.
• Agentic frameworks such as LangGraph, Pydantic AI, or an equivalent.
• Retrieval-augmented generation (RAG) patterns and vector databases.
• Deploying and scaling LLM-powered applications using APIs (e.g. OpenAI or Anthropic) or open-source models.
• Strong Python skills – Kotlin knowledge would be a plus.
• 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 ontologies, knowledge graphs, or graph-based reasoning.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• A background in code analysis, developer tools, or software engineering research.
• Experience with multi-agent systems or complex agentic workflows.
• Actively contributed to relevant open-source projects or publications.

What we offer:

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.
• A motivated team that moves fast, with short feedback loops, rapid prototyping, and lots of ownership.
• Opportunities to publish research at top venues in collaboration with JetBrains Research.

#LI-DNI

A career driven by you. Join AutoScout24 Group and experience life in the fast lane. We’re not just leading the market; we’re creating it. We’re constantly innovating, evolving and transforming things for our customers. We’re driven, ambitious and determined to do our best work, together. For a career that doesn’t stand still, where you can put your ideas in the driving seat and change the way the world moves, join our international community. 

As Principal Data Scientist, you will define and drive the data science strategy across AutoScout24, with a strong focus on AI, machine learning, and Generative AI. You will work closely with product, engineering, and business leaders to turn data into scalable products that create clear business impact. You will lead senior data scientists and act as a technical role model across the company.

WHAT YOU’LL DO 

• define and own the data science and AI strategy across the organization
• lead development and scaling of predictive and generative AI models for marketplace and fintech use cases
• partner with product and business leaders to align ML initiatives with company goals
• design scalable ML systems and cloud-based workflows for training and deployment, with a strong focus on AWS
• build and operate production-ready ML solutions using AWS services (e.g. SageMaker, S3, Lambda, EMR, etc.)
• ensure model quality, monitoring, governance, and compliance in production
• mentor and develop data scientists and act as senior technical advisor 

YOU COME WITH 

• 10+ years of experience in data science, machine learning, or applied AI
• strong hands-on skills in Python, SQL, and common ML frameworks
• proven experience with GenAI and LLMs in real products
• strong experience with AWS and its ML/data ecosystem (e.g. SageMaker, S3, EKS, Lambda, IAM)
• solid background in cloud platforms, MLOps, and model deployment
• ability to translate complex topics into clear business decisions
• experience leading senior teams and shaping technical strategy 

A culture of growth. With over 50 nationalities represented, we’re proud to welcome people of all backgrounds to our growing community, and we’re always finding new ways to help people feel welcomed and included. Here, you’ll have the tools, training and support you need to work in a way that suits you, learn new skills and reach your potential. 

For a career where you can drive our business and shape your future, apply now. 

A career driven by you. Join AutoScout24 Group and experience life in the fast lane. We’re not just leading the market; we’re creating it. We’re constantly innovating, evolving, and transforming things for our customers. We’re driven, ambitious, and determined to do our best work together. For a career that doesn’t stand still, where you can put your ideas in the driving seat and change the way the world moves, join our international community. 

As Senior GenAI Data Scientist, you’ll join our Data Science organisation and drive the development of next-generation AI solutions across the company. You will take ownership of building agentic GenAI systems, integrating knowledge into models, and setting up strong evaluation and monitoring frameworks that ensure we deliver reliable, scalable, and high-value AI products. This position is permanent and located in Munich (Germany) or Toronto (Canada).

What you’ll do:

• Design and build agentic GenAI systems using modern frameworks such as LangChain, AutoGen or Atomic Agents, taking the lead on translating product needs into scalable AI solutions and delivering production-ready implementations.
• Develop and maintain knowledge integration pipelines, including RAG architectures, custom model fine-tuning, vector search, and other grounding methods to ensure our AI systems have the right context for accurate, relevant outputs.
• Define evaluation strategies, build ground-truth datasets, and create automated test and monitoring setups to ensure stable long-term model performance and proactively detect issues such as drift or hallucination.
• Collaborate with engineering, data, and product teams to integrate AI components into platforms, workflows, and customer-facing products, designing clean APIs, robust pipelines, and scalable deployment patterns.
• Act as a hands-on expert for GenAI topics, mentoring peers, guiding solution architecture, and driving best practices in coding, testing, observability, and documentation across the organisation.
• Experiment with new tools, models, and architectures, identify high-impact AI opportunities, and build prototypes that can scale into production-ready solutions. 

What you’ll bring: 

• A degree in computer science, data science, engineering, or a related field.
• Experience building agentic GenAI systems in production (e.g., LangChain, AutoGen, Atomic Agents, or similar frameworks).
• Strong coding skills in Python and experience with robust software engineering practices.
• Practical experience with RAG pipelines, vector databases, embeddings, or fine-tuning workflows.
• Strong understanding of common challenges in AI development (ground-truth evaluation data, monitoring, integration complexity, model drift).
• Experience deploying AI systems in a cloud environment and working with modern ML stacks.
• Familiarity with MLOps, prompt engineering, or LLM-based application design.
• Ability to explain complex AI topics in clear and actionable language to technical and non-technical stakeholders.
• High motivation, ownership mentality, and a strong drive to build impactful AI products.
• Fluency in English; German is a plus.

A culture of growth. With over 50 nationalities represented, we’re proud to welcome people of all backgrounds to our growing community, and we’re always finding new ways to help people feel welcomed and included. Here, you’ll have the tools, training and support you need to work in a way that suits you, learn new skills and reach your potential. 

For a career where you can drive our business and shape your future, apply now. 

Location: We are flexible! Come and join us either in Hamburg, Berlin or Barcelona. 

Freenow by Lyft empowers smarter mobility decisions helping people to move freely and cities to thrive.

The Freenow marketplace is a real-time, two-sided platform connecting millions of riders and drivers. Its efficiency and scalability are powered by a sophisticated ecosystem of Machine Learning systems spanning key domains such as Pricing, Allocation, and Marketplace Payment&Risk.

In this role, you’ll help advance our MLOps practices, build the services and the infrastructure that scales ML models from research to real-time production and observe how they impact the daily lives of our users and our business.

Be ready to work in a multinational, diverse, highly motivated and collaborative team of passionate developers who strive for excellence and like to have fun doing it.  Are you ready for your next ride?

YOUR DAILY ADVENTURES WILL INCLUDE:

• Developing and maintaining high-performance, distributed systems that serve ML models at scale with low latency
• Collaborating with Data scientists and Software Engineers to solve business-critical problems in real-time.
• Streamlining online serving for classical and deep learning models to accelerate A/B testing cycles and reduce time-to-market
• Enhancing our MLOps platform capabilities to support AI state of the art
• Implementing ML Monitoring and Observability best practices, including automated data drift detection and self-healing system triggers
• Contributing to the evolution of our MLOps stack and practices.

Our Techstack: Python | Airflow | Java | Kubernetes | Kafka | Databricks |AWS & more

TO BE SUCCESSFUL IN THIS ROLE:

• M.S or B.S in Computer Science, Machine Learning, Mathematics or equivalent technical background with related field
• 3+ years of professional experience deploying, scaling and monitoring ML models in production environment 
• Proficiency in Python. Experience in JVM based programming language (Java, Scala, Kotlin) or GO is highly desirable
• Proven experience with large-scale data processing systems and modern data warehousing 
• Strong grasp of classical ML and Fundamental understanding of modern ML algorithms (e.g. DNN, RL, transformers ) and mathematical optimizations e.g LP, Convex Optimization
• Team player, and an avid learner, with a proven capacity to collaborate seamlessly with diverse teams, including those in data, tech, and product, as well as other stakeholders.
• Clear Communication in English. We are a very diverse team with people from all over the world and English is our language of communication, besides Python.
• Passionate about harnessing Artificial Intelligence and have the will to play an essential role in the future of urban transportation.
• Experience with streaming technologies and real-time data processing (e.g., Apache Kafka, Apache Flink, Feature Stores) is a plus.

BENEFITS & PERKS IN A NUTSHELL:

DIVERSITY, EQUITY & INCLUSION:

Freenow is an equal opportunity employer and we consider qualified applicants regardless of race, religion, national origin, gender, gender identity, sexual orientation, disability or age.
We want you to grow and evolve, bring your true self to work. 

ABOUT US:

Freenow by Lyft empowers smarter mobility decisions, helping people to move freely and cities to thrive. Through our multi-mobility app, we feature broad options for everyone across 9 European markets and over 180 cities. Millions of passengers can access services including taxis, private hire vehicles, carsharing, car rental, e-scooters, e-bikes, e-mopeds, and public transport within a single app. 

In July 2025 Freenow was acquired by Lyft, a global mobility platform, which connects riders and drivers for billions of rides across North America and Europe. Together, Freenow and Lyft are creating a more caring and connected world, with transportation for everyone. We are a global, diverse, highly motivated, and collaborative team that strives for excellence and likes to have fun. Ready for your next ride?

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

CSQ227R43

We’re hiring for multiple roles within our Professional Services team. Depending on experience and scope, this position may be offered as Senior Solutions Consultant or Resident Solutions Architect

You may know this role as a Big Data Solutions Architect, Analytics Architect, Data Platform Architect, or Technical Consultant. The final title will align to your experience, technical depth, and customer-facing ownership.

As a Data and AI Solution Architect (Professional Services) (Internal Title - Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium-term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• Extensive experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 10% of the time
• [Preferred] Databricks Certification but not essential

About Us: 

Sauce Labs is the world’s largest full-lifecycle, test automation platform, and the company behind Selenium. Trusted by 80% of the world’s top ten largest financial institutions and over 300,000 enterprise users, Sauce Labs provides the only AI platform capable of turning business intent into autonomous testing and quality assurance. With a proprietary dataset of 8.7 billion test runs, Sauce Labs empowers the Fortune 2000 to bridge the gap between AI-driven code generation and enterprise-grade software quality. Learn more at saucelabs.com.

The Role:

We are seeking an innovative and experienced AI Architect to join our engineering leadership team. This is a strategic role that will be instrumental in designing and building the next generation of AI-powered features for our continuous testing platform. You will be responsible for architecting scalable and robust AI solutions that transform how our customers gain insights from their test data and production environments, and how they create tests.

Responsibilities:

• Define AI Architecture: Lead the design and architecture of cutting-edge AI/ML solutions for new product offerings, ensuring scalability, performance, quality and reliability within a cloud-native environment.
• AI-Powered Insights (Test & Production):  Architect AI systems to derive actionable insights from vast quantities of test run logs and analytics data. This includes identifying patterns, anomalies, and performance trends.
• Production Error Reporting Integration: Design AI solutions that integrate with our existing error reporting product to analyze production issues for mobile and web applications, providing deeper understanding and predictive capabilities.
• Unified Data Intelligence:  Develop architectures for combining insights from both test runs and production data, creating a holistic view of application quality and user experience.
• Automated Failure Analysis & Remediation:  Architect AI models and systems that can automatically identify failures in test runs, diagnose root causes, and suggest potential fixes or optimizations.
• AI-Assisted Test Case Generation:  Design AI-driven capabilities to assist in the intelligent generation of test cases, improving test coverage and efficiency.
• Leverage Cloud AI Platforms:  Work extensively with Google Cloud Platform (GCP) services, specifically focusing on leveraging and integrating with our self-hosted versions of Gemini Flash models for text generation and understanding.
• Data Strategy & Pipeline:  Collaborate with data engineering teams to define and optimize data pipelines, ensuring efficient data flow from sources (Snowflake, with an eye towards Google BigQuery) to AI models.

Enhance Delivery Process: Help identify the areas of opportunity for delivery process improvements through AI tools and validate the feasibility.

• Technical Leadership:  Provide technical leadership and mentorship to engineering teams, guiding them in the implementation and deployment of AI/ML solutions. Take part in AI governance projects.
• Research & Innovation:  Stay abreast of the latest advancements in AI/ML, evaluating new technologies and methodologies to continuously enhance our product offerings.
• Cross-Functional Collaboration:  Work closely with product management, engineering teams, and senior leadership to translate business requirements into technical AI solutions and roadmaps.

Required Skills:

Education & Experience:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 7+  years of experience in software development and architecture of production-grade systems.

• Extensive AI/ML Architecture Experience:  Proven experience in designing, building, and deploying large-scale AI/ML systems and platforms in a production environment.
• Cloud Expertise (GCP Preferred):  Deep hands-on experience with Google Cloud Platform (GCP) services relevant to AI/ML (e.g., Vertex AI, BigQuery, Cloud Storage, Dataflow).
• Generative AI Experience:  Practical experience with large language models (LLMs), particularly with models like Gemini Flash, and understanding of their deployment and fine-tuning (especially with regard to prompt engineering, agent orchestration, context windows management and output validation)
• Big Data & Analytics:  Strong background in working with large datasets, data warehousing (Snowflake, BigQuery), and analytics platforms.
• Software Engineering Foundation:  Solid understanding of software development best practices, scalable system design, and microservices architectures.
• Problem-Solving & Innovation:  Exceptional analytical skills with a track record of solving complex, ambiguous problems using AI/ML techniques.
• Leadership & Communication:  Excellent communication and presentation skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences, including senior leadership.
• Hands-on Capability:  While an architectural role, a willingness to be hands-on with prototyping, proof-of-concepts, and guiding implementation.
• Domain Knowledge (Highly Desired):  Familiarity with software testing, quality assurance, DevOps, or application performance monitoring.

Nice to Haves:

• Experience with MLOps practices and tools for managing the ML lifecycle.
• Contributions to open-source AI/ML projects or relevant publications.
• Advanced degree in Computer Science, AI, Machine Learning, or a related quantitative field.

We are a hybrid workplace that recognizes the importance of flexibility while valuing in-person collaboration and relationship building. As a result, Saucers located near an office location must be able and willing to come into the office.

Please note our privacy terms when applying for a job at Sauce Labs.

Sauce Labs is proud to be an Equal Opportunity employer and values diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender identity/expression/status, sexual orientation, age, marital status, veteran status or disability status.

Security responsibilities at Sauce

At Sauce, we will commit to supporting the health and safety of employees and properties, partnering with internal stakeholders to learn and act on ever-evolving security protocols and procedures. You’ll be expected to fully comply with all policies and procedures related to security at the department and org wide level and exercise a ‘security first’ approach to how we design, build & run our products and services.

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. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs. The ML Workflows Engineering team is dedicated to removing infrastructure challenges, streamlining machine learning operations (MLOps), and enabling teams to focus on the innovative work that matters most – building impactful ML models and intelligent agents. As part of the team, you'll play a key role in designing tools, automation, and pipelines that make machine learning development seamless and intuitive.

By integrating cutting-edge MLOps practices and engineering excellence, we aim to maximize productivity and remove the complexity of ML infrastructure so that our teams can push the boundaries of what’s possible in AI.

As part of our team, you will:

• Build tools, automation, and workflows to simplify infrastructure-heavy tasks, empowering AI teams to focus on experimentation and solving core challenges.
• Develop robust monitoring, logging, and tracing systems to ensure the performance and reproducibility of ML workflows in production.
• Design, implement, and maintain end-to-end machine learning pipelines to enable the seamless development, training, and deployment of ML models and intelligent agents.
• Work with large-scale distributed systems, including GPU clusters, to support training, fine-tuning, and evaluation of ML models.
• Collaborate with product and development teams to transform high-level goals into concrete, scalable, and maintainable systems.
• Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive and focused on innovation.

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

• Hands-on experience with modern MLOps tooling, including Kubernetes, Cloud providers (GCP and AWS), and ML orchestration frameworks.
• A solid understanding of the ML lifecycle from idea to the customer-facing application.
• 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 customer-centric mindset – you care about how ML engineers are actually working and can translate their needs into actionable, scalable, and maintainable architectural decisions.
• Experience with modern CI/CD systems, like GitHub Actions or JetBrains TeamCity.
• At least three 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 ZenML, Dagster, and Airflow.
• Developing infrastructure components and services using cluster solutions like Kubernetes.
• The development of Python-based backend services.
• Creating and maintaining ML pipelines, including legacy ones.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.

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

• LLM inference frameworks such as vLLM, DeepSpeed, and TensorRT.
• Writing and maintaining Python libraries used by internal (or external) ML engineers.
• A strong theoretical background in NLP and transformer-based approaches.
• Writing code in Java and/or Kotlin.

#LI-HYBRID
#LI-MR1

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Eligible locations:

• USA
• Canada
• UK
• Portugal
• Spain
• France
• Italy
• Germany
• Netherlands
• Poland
• Czech republic
• Israel
• UAE

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 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.

JetBrains is evolving beyond standalone developer tools toward a unified, AI-native platform for software development.

AI is no longer just an assistant inside the editor – it is becoming an active participant in how software is planned, built, reviewed, and operated across teams and organizations. This shift introduces new challenges that cannot be solved at the level of individual tools alone: governance, security, cost control, observability, and coordinated work between humans and autonomous agents.

Our goal is to build a platform that enables companies to adopt AI in software development in a structured, scalable, and economically efficient manner without locking them into closed ecosystems. This platform will serve as the execution and governance layer for AI-driven development, deeply integrated with developer tools but designed to work across teams, products, and environments.

We are looking for an experienced ML leader who has created products with an ML backbone, weaving together research, technical excellence, and strong product focus.

We are seeking a professional who excels in three key areas: technology, product vision, and business operations. This role involves extensive cooperation with products across the company – both AI-native and just integrating AI.

In this role, you will: 

• Identify, prioritize, and champion high-impact AI projects that align with JetBrains' product strategy and deliver measurable business value.
• Lead a team of about 50 people across multiple AI initiatives, including hiring and developing team leads, ML engineers, and MLOps specialists.
• Set the technical direction for JetBrains’ ML roadmap,  translating cutting-edge AI advancements into practical product innovations.
• Ensure projects deliver on commitments and create measurable value.
• Set technical standards, architectural direction, and best practices for ML development.

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

• The ability to balance long-term R&D with short-term product needs.
• A track record of shipping production ML systems that users actually use.
• Experience building business cases for technical projects.
• A deep understanding of modern ML infrastructure, LLMs, and AI tooling.
• Outstanding stakeholder management skills and are comfortable working with senior leaders.
• The ability to thrive in a fast-paced environment, effectively managing multiple projects simultaneously.
• Excellent management and communication skills.
• Attention to detail in everything you do.

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

• Distributed training of multi-billion parameter models.
• CI systems, workflow automation, and experiment tracking.
• The Kotlin programming language.

#LI-KP1 #LI-HYBRID

About us

At Ada, we envision a world where everyone gets the healthcare they need. Yet, millions remain undiagnosed and untreated due to the complexity of identifying rare and underrecognized conditions, even when effective treatments exist

We’re changing that. Powered by leading AI and built by a team of physicians and clinical scientists, Ada helps people get answers faster, identifying those at risk and guiding them to the right care. Every successful journey makes us smarter and able to help more people.

If you’re passionate about transforming healthcare and ensuring no one goes undiagnosed, join us and be part of what’s next.

About the role

As our Senior AI Engineer, you will play a key role in advancing the development, validation, and supporting tooling of our patented hybrid AI architecture. Our core technology combines Probabilistic Graphical Models (PGMs) with Generative AI to deliver explainable, transparent, and clinically robust medical reasoning. In this role, you will help shape how these components evolve and scale, ensuring they meet high standards for performance, reliability, and regulatory readiness.

Reporting to our Director of AI, you will operate at the intersection of medical intelligence and software engineering—translating complex requirements into sophisticated technical solutions. You’ll collaborate closely with AI researchers, engineers, and medical experts, contributing to both the strategic direction of the platform and the hands-on implementation of production-grade systems.

This is a remote role, open to candidates based in the UK or Germany.

What you’ll do

• System Evolution: Refine and scale our patented hybrid engine, focusing on the interface between LLM-based reasoning and probabilistic logic.
• Engineering Excellence: Write and deploy high-availability production code in Java and Python, ensuring adherence to security and scalability standards.
• Evaluation & Tooling: Maintain and extend our internal evaluation and validation frameworks, including synthetic data generation, automated metric tracking, and model explainability.
• Clinical Collaboration: Work with Medical Experts and AI Engineers to translate complex medical requirements into technical specifications.
• Documentation: Provide technical documentation of AI components for stakeholders and regulatory compliance (e.g. SaMD).

What you’ll bring

• A Master’s degree in a quantitative field (Computer Science, Statistics, Mathematics, Physics, or similar). A PhD is a plus but is not essential.
• 3+ years of (industry) experience in AI/ML or algorithmic development.
• Proficiency in Python (mainly for AI/Logic) and Java (mainly for backend services).
• Practical experience with classic ML and algorithms, such as Probabilistic Graphical Models (PGMs).
• Hands-on experience with LLMs (ideally including RAG and Agents). Bonus points for exposure to their validation in production.
• Solid grasp of modern DevOps (Containerization, CI/CD, Automated Testing) and AI-assisted development (e.g., Cursor, Claude Code).
• A high degree of autonomy and a preference for collaborative peer reviews and documentation.
• Experience working in cross-functional teams, with a strong foundation in mathematics, logic, or statistical modelling to support rigorous problem-solving would be advantageous.
• Exposure to the healthcare sector or other regulated environments (e.g., ISO 13485, MDR, SaMD), along with familiarity in areas such as Kotlin, advanced data visualisation, or MLOps would be highly beneficial.

What’s awaiting you at Ada

• Flexible working hours to maintain a healthy work/life balance
• 28 days holiday, plus bank holidays to unplug, rest and recharge for our UK employees (30 days plus public holidays in Germany).
• Corporate health insurance and pension scheme (UK).
• Support to set up your home office space, with the option for a co-working space subscription for our remote employees.
• Parental benefits (Plus Nursery Benefits for the UK).
• Employee Assistance Programs to help you look after your emotional wellbeing and deal with life’s challenges.
• Stay active and healthy with a discounted ClassPass membership (or Urban Sports Club membership for our employees in Germany).
• Bike Leasing Scheme (Germany).
• Online Academy for learning and development opportunities.

At Ada, inclusivity isn't just a goal – it's our foundation. 

As a proud equal opportunity employer, we embrace diversity in all its forms. We encourage applications from every corner of society, regardless of race, color, religion, sex, pregnancy status, national origin, age, physical and mental abilities, marital status, sexual orientation, gender identity, gender expression, genetic information, and any other characteristic protected by law. 

Join us in shaping a more inclusive tomorrow.

Please note that we do not accept applications sent via email. All applications need to be completed using the online form.

 All your information will be kept confidential according to GDPR guidelines.

*Any commercial activities concerning this advertisement will not be appreciated.

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).