Top Machine Learning Jobs in Bucharest

Design and implement AI/ML compiler technologies for embedded ARM and RISC-V hardware using C++ and LLVM. Develop MLIR and graph compiler features, optimize model lowering, fusion, tiling, scheduling, code generation, and inference performance. Collaborate with hardware architects, ML researchers, and global development teams to support automotive system-on-chip platforms. Analyze generated code with simulators and profilers while contributing to open-source compiler technologies.

Develop AI/ML compiler technologies for embedded ARM and RISC-V processors, optimizing models through graph compilation, lowering, fusion, tiling, scheduling, code generation, and performance analysis. The role involves C++ and LLVM/MLIR development, collaboration with hardware architects and ML researchers, compiler support for automotive SoCs, and contribution to open-source projects.

Design and implement AI/ML compiler technologies for embedded ARM and RISC-V processors using C++, LLVM, and MLIR. Optimize machine learning models through graph-level transformations, lowering, fusion, tiling, scheduling, code generation, and performance analysis. Collaborate with hardware architects, ML researchers, compiler consumers, and global development teams to deliver production compiler features for automotive SoCs.

Develop AI/ML compiler technologies for embedded hardware using C++ and LLVM, optimizing models for high-performance inference across semiconductor platforms. Build compiler features for ARM and RISC-V processors, including graph optimizations, model lowering, code generation, and optimization pipelines. Collaborate with architecture teams, internal tool users, and global development teams throughout design, development, and deployment.

Design and implement AI/ML compiler technologies for embedded ARM and RISC-V processors. Develop LLVM and MLIR compiler features, optimize graph-level workloads through lowering, fusion, tiling, and scheduling, and improve code generation performance across semiconductor platforms. Collaborate with hardware architects, ML researchers, compiler consumers, and global development teams. Analyze generated code using simulators and profilers while contributing production-quality C++ and Python code to compiler frameworks.

Architect and build core Decision Intelligence platform capabilities: heterogeneous data integration, semantic modeling, simulation and optimization services, scalable distributed backend systems, AI/ML and LLM-powered production deployments, data pipelines, and operational tooling. Provide technical leadership, own full lifecycle from design to production, improve reliability, observability, and drive engineering excellence.

Design and develop AI/ML compiler technologies for embedded ARM and RISC-V processors. Optimize machine learning models through graph-level transformations, lowering, code generation, fusion, tiling, and scheduling using C++, Python, LLVM, and MLIR. Collaborate with hardware architects, ML researchers, compiler teams, and global stakeholders to deliver high-performance inference tools for automotive SoCs. Analyze generated code with simulators and profilers, contribute to open-source projects, and support compiler features throughout design, development, and deployment.

Designs and implements AI/ML compiler technologies for embedded hardware using C++ and LLVM, optimizing models for high-performance inference. Develops compiler features for ARM and RISC-V processors, including compiler passes, lowering, graph optimization, code generation, and deployment workflows. Collaborates with architecture teams, internal users, and global development teams to deliver proprietary compiler capabilities. Requires exposure to machine learning, edge AI, embedded systems, ML frameworks, and open-source contribution.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Lead the architecture and development of a code-first agent garden serving over 40,000 engineers. Build agent frameworks, orchestration, MCP connectors, authentication, auditability, data pipelines, SDKs, APIs, and marketplace infrastructure. Establish software quality, testing, architecture, and DevOps standards while making key technical decisions. Mentor and coordinate junior and mid-level engineers, conduct technical reviews, and guide scalable AI/ML engineering practices.

Design, build, deploy, and maintain production-grade machine learning systems for a proactive AI platform. Own projects end to end across data curation, model training, evaluation, inference, monitoring, and optimization. Translate research into reliable systems, troubleshoot live production issues, improve performance iteratively, and balance latency, cost, safety, and reliability. Collaborate with research, engineering, and product teams while mentoring engineers and conducting technical reviews.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

The role involves reviewing AI-generated outputs, analyzing multimedia, and providing culturally relevant content feedback. Strong analytical skills and attention to detail are required.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

Lead large-scale ML projects overseeing their entire lifecycle. Influence technical decisions, design ML pipelines, and mentor other engineers.