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we are looking for a Senior Backend Infrastructure Engineer to join our Backend Infrastructure team. This role is focused on designing, building, and improving the backend infrastructure that powers delivery platform. The engineer will work on scalable backend services, shared infrastructure components, internal frameworks, system reliability, performance optimization, and cross-team technical enablement. The ideal candidate is a strong backend engineer with solid experience in C# and .NET Core, distributed systems, databases, cloud environments, and production-grade backend services. The candidate should also be comfortable adopting modern AI-assisted development tools such as Cursor, Claude, and similar coding assistants to improve development quality, productivity, code review, and technical research. The engineer will work closely with backend product teams, DevOps, data engineering, mobile, and frontend teams to deliver reliable, secure, and high-performing systems.

What Will You Do
Design, develop, and maintain backend services using C# and .NET Core.
Build shared backend infrastructure, internal libraries, SDKs, reusable components, and service templates used by multiple engineering teams.
Support backend teams with architecture reviews, technical guidance, best practices, and performance improvements.
Help improve backend development velocity, code consistency, and production readiness across teams.
Work with relational and NoSQL databases such as SQL Server, PostgreSQL, MySQL, and MongoDB.
Design and optimize database queries, indexes, schemas, and data access patterns.
Build and maintain search and caching solutions using technologies such as Elasticsearch and Redis.

we are looking for a Data Software Engineer.
Full-stack engineer with a soft spot for geometry: build the data and product systems that power automation across frontend and backend.

We're looking for a Head of AI to own everything AI and ML at Sensos - from the agentic systems that power autonomous supply chain execution to the data science and modeling that turn our real-world data into an unfair advantage.
This is a founding leadership role. You'll be 100% hands-on from day one - researching, prototyping, and shipping models and AI capabilities directly into the platform - and over time you'll build and lead the data organization (data science, ML, and analytics). Put simply: you're the person who brings the "smart" into the platform.
We capture billions of real-world signals from IoT smart labels moving through global supply chains. That data is the raw material; your job is to turn it into prediction, detection, and trustworthy autonomous decisions.
What You'll Do:
Work with a wide variety of datasets - IoT sensor, GIS, time-series, tabular, and textual
Solve a range of business problems: time-series prediction, anomaly detection, scoring, and agentic flows
Design real-time inference pipelines
Develop and deploy ML models and LLM-based agents that serve tens of enterprise clients
Conduct exploratory data analysis, feature development, and correlation analysis
Take work all the way from prototype to production - you own it end-to-end
Partner closely with Product and Engineering to embed intelligence natively into the platform and shape the roadmap
Stand up the infrastructure: evaluation pipelines, model promotion, MLOps, and artifact management
Build, hire, and lead the data function (DS, ML, and analytics) as we scale
Lead patent registrations, technical presentations, and stakeholder discussions

Join our Storage Products group to develop advanced automation tools, testing infrastructure and tests for storage subsystems. You'll work with a collaborative team to build scalable lab automation, debug tools, and validation systems that ensure the excellence users expect from Apple devices. This role offers the opportunity to impact the entire design-to-implementation process while leveraging cutting-edge AI tools.
Develop version control software packages for large-scale validation labs Build lab automation infrastructure and debug tools for storage validation Design and implement test flows for storage modules and performance validation Collaborate across multiple teams to deliver innovative storage solutions Utilize advanced AI tools Work on Unix-based environment Apply deep understanding of storage behavior to real-world testing scenarios

At Infinidat, a Lenovo company, we help enterprises and service providers empower their data-driven competitive advantage at scale. We're looking for a top-notch Backend Engineer to join Infinidat's Big Data / AI Ops group. In this role, you'll design and build the services that power our data platform — developing scalable microservices, optimizing data pipelines, and working closely with large-scale storage and operational datasets.

Responsibilities:

* Design, develop, and maintain backend services and microservices in Python
* Build and optimize database queries and schemas, primarily on PostgreSQL and ClickHouse
* Develop and operate data-intensive services supporting big data and AIOps workloads
* Collaborate with cross-functional teams on architecture, performance, and reliability
* Write clean, well-tested, maintainable code and participate in code reviews

The Support Development Team builds and maintains internal applications that help Support Engineers work more effectively - automating repetitive tasks, streamlining workflows, and translating operational challenges into scalable technical solutions. As a Software Engineer on this team, you will own tools end-to-end, working closely with Support teams to understand their needs and deliver impactful solutions. The team's primary language is Python, with Go used on selected projects, Kubernetes-based infrastructure, and occasional frontend work in NextJS.

Responsibilities:

* Develop and maintain Python-based tools that enhance the efficiency and workflow of Support Engineers
* Work directly with Support Engineers to understand their challenges and translate them into actionable technical solutions
* Identify and integrate improvements into the existing tool ecosystem, continuously seeking modernization opportunities
* Troubleshoot tool-related issues and ensure minimal downtime for internal users
* Write and maintain clear documentation to support scalability, onboarding, and knowledge sharing
* Explore emerging technologies and propose innovative approaches to improve team solutions
* Contribute to Go-based projects and work within Kubernetes-based infrastructure as part of ongoing modernization

Looking for a CPU performance modeling architect to take responsibility over the performance aspects of new CPU instructions or modes of operation. The role includes but is not limited to:
Partners with lead SW and HW architects to co-invent optimal HW and SW solutions that come to address requirements. Influences the direction based on experiments and simulation data.
Models CPU functionality, performance and/or power in pre-silicon simulators
Defines and runs performance experiments to aid feature definition. Such experiments can be performed on a pre-silicon simulation environment or in a real system or on a combination of both or even in combination with analytical models.
Provides experimental/proof of concept for new features and implementation alternatives meeting performance constraints.
Analyzes the bottlenecks of current CPUs on workloads that reflect CPU future usages.
Potentially (in the future), lead a team doing above activities.
An adequately qualified candidate can also become the leader of definition for some features in addition to all the roles above.

We are forming a new team to explore AI Compilation Boundaries
This team will operate under a unique manner, as described below -
The environment
- Startup-like intensity
Small team
High ownership
High visibility
Merit is the essence, not titles
- Fast pace
Todays state of the art is tomorrows baseline
We aim to go beyond
- Incubator mindset
Take on hard challenges, dismantle, and regroup as novelty
- Elite technical culture
Strong experts
Strong standards
Strong execution.
Ratio beats seniority, not the other way around
- No artificial boundaries
We challenge assumptions, and question constraints, and leave no quarter
- Direct exposure to the latest and greatest AI technology
Real hardware
Real toolchains
Real large-scale AI applications
All pass through and impacted by our work
- First-principles thinking
out-of-the-box is a compromise
Do not accept that a box had to exist in the first place.

*Part-Time or Full-Time Opportunity

Responsibilities:
The research focuses on designing next-generation rack and SuperPod architectures that combine elasticity through resource pooling with optimal performance enabled by holistic co-design across applications, parallel programming models, interconnect fabrics, and compute, memory, and switch chip architectures. On the communication side, the work architects scalable, high-bandwidth, and low-latency scale-up fabrics across multiple chips at rack and SuperPod scale. On the memory side, it explores richer protocol semantics beyond traditional load/store operations to reduce unnecessary data movement and rethinks memory hierarchies to expose large-scale capacity with near-local latency. On the compute side, it analyzes the requirements of General Compute and Generative AI workloads and their parallel programming models to fully leverage large-scale system resources.
Investigate and prototype new architectural features, including but not limited to:
o Tiered Memory: Explore advanced memory organizations, including hardware-managed caching, to enable software-transparent, fine-grained promotion and demotion of cache lines across the memory hierarchy.
o Prefetching/Speculation: Evaluate and design existing and next-generation hardware prefetching and speculation mechanisms to effectively hide local and remote memory latencies at rack and SuperPod scale.
o Near-Memory/Network Processing: Develop support for key primitives executed at the memory and network layers to minimize unnecessary data movement across sparse, dense, and pointer-based data structures.
o Workload-Centric Co-Design: Study optimal parallelization strategies at rack and SuperPod scale for both General Compute and Generative AI workloads, and design dedicated hardware support for widely used parallel programming primitives such as RPCs and collective communication.
Write reports and papers on the research results and present them.

we are looking for a Software Development Team Leader Production Engineering.
As a Software Development Team Leader Youll lead a team of strong engineers building the core platform behind one of the largest real-time recommendation systems in the world, with full ownership from design to production and the freedom to move fast and solve complex infrastructure challenges.1.4 billion users. 950,000 requests per second. 140TB of fresh data every day. 800M metrics every minute (for monitoring). Thats the scale Taboolas Production Engineering team operates at - and were looking for a Software Development Team Leader to help drive what comes next.

We're looking for an Infrastructure Engineer to go deep into that layer: build and own core proxy and networking infrastructure, not configure someone else's.
You bring the same range as our core infrastructure engineers - proxies and networking, browser internals, and cloud-native systems (Kubernetes, Docker, AWS) - with the depth to design and debug at every layer of the stack, plus a genuine specialization in networking and proxy systems.
What Youll be Doing:
Build core proxy infrastructure - reverse proxies, IP rotation, session management, intelligent routing.
Own IP pool health - reputation scoring, monitoring, blacklist recovery - across residential, mobile, datacenter, and ISP proxies.
Implement geo-targeting at country, city, and ASN level.
Work at the protocol level (TCP/UDP, sockets, HTTP/HTTPS/SOCKS5, HTTP/2/3) and the Linux networking stack underneath.
Build monitoring, health checks, failover, and bandwidth/usage metering for the proxy fleet.
Deploy and scale on cloud-native infrastructure (Kubernetes, Docker, Terraform).
Use AI-assisted development tools daily.

we are looking for a Software Engineering Team Lead.
You will own the AI platform across all Sedric product lines - customer-facing policy configuration, internal tooling, and the architectural decisions that determine whether we scale reliably or expensively. Your responsibilities include:
Lead an engineering team - own output, growth, and retention
Build and evolve agentic systems that make high-stakes compliance decisions at scale: 99% accuracy, low cost, high throughput
Set guardrails, change management practices, and engineering procedures that meet the expectations of tier-1 financial institutions
Own customer-facing policy workflows and internal tooling for Technical Account Managers
Partner with the Head of R&D on technical direction and hiring