Forward Deployed Engineer
The role focuses on solution architecture, customer delivery, and integrating AI agents into client systems, which constitutes applied AI engineering at an AI-first company.
Core-AI roles at companies genuinely doing AI · Berlin
71 roles
The role focuses on solution architecture, customer delivery, and integrating AI agents into client systems, which constitutes applied AI engineering at an AI-first company.
The role involves building and optimizing ML training pipelines, reinforcement learning infrastructure, and model evaluation systems at a foundation model company.
The role involves building and deploying ML-driven workflows and simulation pipelines for materials discovery at an AI-first company.
The role involves building agentic workflows, implementing eval harnesses, and managing LLM observability, which constitutes applied AI engineering for agentic systems.
The role involves end-to-end development, training, and productionization of time-series forecasting models and MLOps infrastructure for energy trading.
Builds and maintains specialized ML infrastructure, including training pipelines, model serving, feature stores, and GPU orchestration for production machine learning models.
The role focuses on model evaluation, uncertainty quantification, adversarial robustness, and safety research for deep learning models in high-stakes environments.
The role involves end-to-end development, evaluation, and deployment of LLM and classical ML models for core product features like extraction and anomaly detection.
The role involves building, scaling, and evaluating tabular foundation models, including developing benchmarks and agentic pipelines for model training and research.
The role involves architecting, training, and deploying 3D object detection models and managing the perception stack for autonomous ground handling vehicles.
The role involves training, optimizing, and deploying computer vision models for object detection and tracking on edge hardware.
The role involves developing, training, and evaluating 3D computer vision models, SLAM systems, and geometric deep learning pipelines for real-world deployment.
Forward-deployed engineer building and deploying RAG systems, AI agents, and data fusion pipelines for defense/intelligence customers at an AI-first company.
The role involves hands-on design, training, and deployment of ML models, managing the full ML lifecycle, and architecting ML-specific infrastructure.
The role focuses on building, debugging, and deploying agentic workflows and RAG systems, which constitutes applied AI engineering.
The role involves building agentic systems, implementing Bayesian optimization and active learning, and integrating ML models into closed-loop scientific discovery workflows.
The role involves hands-on ML modeling, benchmarking, and optimizing tabular foundation models for production deployment in complex customer environments.
Builds and optimizes specialized ML infrastructure for distributed training, GPU orchestration, and performance profiling of foundation models.
The role involves researching, scaling, and developing novel transformer architectures for tabular foundation models, which is core ML research and development.
The role involves training, testing, and shipping ML models, managing the full model lifecycle, and developing algorithms for recommendation systems.
The role focuses on building ML-specific infrastructure, distributed training pipelines, and deep learning frameworks for large-scale model training at an AI-first company.
The role involves designing, building, training, and deploying machine learning models for fraud detection and risk assessment in a production environment.
The role involves building, evaluating, and deploying production-grade LLM agents and agentic workflows for an AI-first company in the logistics sector.
The role involves building, evaluating, and hardening production AI agents, including orchestration, tool use, and developing robust evaluation frameworks for LLM systems.
The role involves designing, training, and deploying reinforcement learning-based controllers for robotic systems, including sim-to-real transfer and policy optimization.
The role involves researching, training, and deploying ML models for RF signal processing and electronic warfare systems on edge hardware.
The role involves end-to-end development, training, and productionization of credit risk ML models, including fine-tuning and deploying models in a production environment.
The role involves qualitative evaluation and RLHF-style feedback on AI coding agents, which is a core component of model alignment and performance assessment.
The role involves building, training, and deploying reinforcement learning agents for autonomous aerial platforms, covering the full pipeline from simulation to real-time hardware.
The role involves building, maintaining, and improving production AI agents using frameworks like LangGraph, which constitutes applied AI engineering.
The role involves building infrastructure for voice agents, evaluating models, and solving scaling challenges for AI-driven voice interactions at an AI-first company.
The role involves training, testing, and shipping recommendation models, developing algorithms for search insights, and building autonomous agentic systems.
Builds and maintains ML-specific infrastructure, including annotation tooling, training orchestration, and model evaluation pipelines for an AI-first company.
The role involves building, deploying, and monitoring predictive credit risk models and ML pipelines in a production environment.
The role involves building and orchestrating production-grade AI agents, RAG pipelines, and evaluation systems for an AI-first mental health platform.
The role involves rigorous evaluation, benchmarking, and experimental research on frontier LLMs and agentic systems, which is core ML research and evaluation work.
The role involves integrating reinforcement learning agents into autonomous aerial systems, building training infrastructure, and developing ML-specific inference pipelines for real-world deployment.
The role involves researching, designing, and training large-scale multimodal foundational models, including custom loss functions and distributed training loops.
The role focuses on building data pipelines, ingestion infrastructure, and quality systems specifically to prepare training data for ML models in materials science.
This is a forward-deployed engineer at an AI-first company building, deploying, and evaluating complex agentic workflows and RAG systems for enterprise clients.
The role focuses on building internal AI platform infrastructure, agent runtimes, and evaluation/observability tooling for AI systems, which constitutes core ML-specific infrastructure.
The role involves building document intelligence, RAG systems, and ML production pipelines for tax automation, which constitutes applied AI engineering.
The role involves training and fine-tuning ML force fields, developing ML architectures, and building distributed training/inference infrastructure for molecular simulation.
The role involves building, scaling, and evaluating voice AI agents and model performance, which constitutes core applied AI engineering at an AI-first company.
The role involves developing, training, and evaluating Vision-Language-Action (VLA) models for embodied AI in industrial robotics.
The role involves architecting and building agentic systems, designing evaluation harnesses, and managing context for LLM-based investigation agents in production.
The role involves building and deploying RAG systems, AI agents, and data fusion pipelines for defense customers, which constitutes applied AI engineering.
The role involves training, evaluating, and deploying ML models, building agentic systems, and managing production ML pipelines for a specialized domain.
The role involves developing, training, and optimizing ML models, maintaining ML pipelines, and deploying models for mobile applications.
Builds ML-specific evaluation infrastructure, simulator pipelines, and data flywheels for LLM/VLM model performance measurement and improvement.
The role involves building agentic workflows, RAG pipelines, and AI evaluation suites (evals) for an AI-integrated platform, moving beyond generic product engineering.
The role involves building ML infrastructure, model inference wrappers, evaluation pipelines, and deep integration with PyTorch models for tabular foundation models.
The role involves building RAG systems, agent frameworks, retrieval engineering, and model evaluation, which constitutes applied ML work.
The role involves designing, training, and deploying Vision-Language Action models and end-to-end learning systems for robotic autonomy.
Develops robotics state estimation systems using probabilistic frameworks, sensor fusion, and optimization-based estimators for autonomous heavy machinery.
The role focuses on deploying and configuring agentic AI solutions for financial clients, involving architecture design and production-grade AI agent implementation.
Forward-deployed engineer at an AI-first company building, deploying, and integrating agentic AI solutions and decision logic into client systems.
The role focuses on building ML-specific data pipelines, MLOps workflows, and model training infrastructure for embodied AI and robotics models.
Builds document intelligence, RAG systems, and ML-based prediction models for tax workflows, including evaluation and production monitoring of these AI systems.
The role involves building, fine-tuning, and deploying production ML models, designing ML infrastructure, and implementing inference systems for media content.
The role involves building, fine-tuning, and deploying production ML models, designing ML infrastructure, and managing MLOps pipelines for media content.
The role focuses on building and scaling agentic AI products, including model evaluation, guardrails, and monitoring stochastic systems in a production environment.
The role involves designing, training, and evaluating neural audio codecs and generative audio models, which is core ML research and model development.
The role focuses on building and optimizing ML training infrastructure, GPU orchestration, and distributed training pipelines for generative model development.
The role involves designing, training, and fine-tuning large-scale multimodal generative models, conducting ablation studies, and managing the full lifecycle of model development.
The role involves designing, developing, and refining Vision-Language Action Models for robotic autonomy, which is core applied ML and robotics research.
The role focuses on building and scaling ML-specific infrastructure for model serving and fine-tuning pipelines, which is core ML infrastructure work.
The role involves designing, training, and evaluating novel foundation model architectures, specifically focusing on tabular data and scaling laws.
The role involves designing and implementing ML/AI algorithms for robotic perception and navigation, specifically working with deep learning frameworks and sensor data.
The role involves training, evaluating, and deploying retrieval, ranking, and LLM models for search, which is core ML engineering work.
The role involves designing novel architectures, training foundation models, and conducting ML research to advance tabular machine learning.
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