Lead research on long‑term memory and hyper‑personalization for intelligent agents, developing models, methods, and evaluation frameworks to advance AI capabilities.
Responsibilities
Define research directions and problem statements for long‑term memory and personalization.
Design persistent and hierarchical memory systems and reusable memory components.
Propose and validate novel methods for contextual personalization, memory selection, and experience accumulation.
Develop large‑model training and memory‑co‑optimization pipelines for agent tasks.
Create evaluation frameworks for long‑term interaction, computer use, and multi‑agent tasks.
Requirements
Master’s in Computer Science, Artificial Intelligence, Machine Learning, Mathematics or related field; PhD preferred
Research and develop innovative technologies for Agentic AI, Physical AI, World Models, and RSI-related initiatives, while enabling and optimizing state-of-the-art AI models on AMD platforms.
Responsibilities
Research and develop innovative technologies for Agentic AI, Physical AI, World Models, and RSI-related initiatives
Enable and optimize state-of-the-art AI models, harness, skills and workloads on AMD platforms
Apply an AI-native and data-driven approach to identify bottlenecks and prioritize optimization efforts
Build proof-of-concept systems and translate promising research into practical solutions
Stay informed of emerging AI research, software technologies, and hardware trends
Pay within range listed + Bonus + Benefits + Equity
Responsibilities
Build & lead the team that designs, trains, adapts, and evaluates reasoning and cognition models intended for deployment across unmanned platforms
Define the technical roadmap for single-agent and multi-agent models, including architecture choices, training methods, data strategy, and evaluation criteria
Advance model capabilities for interpreting context, reasoning over time, coordinating among agents, and adapting to incomplete or changing information
Drive training efforts, including self-supervised learning, imitation learning, reinforcement learning, and distillation
Establish rigorous evaluations in simulation and with real-world data
Requirements
Bachelor's degree
Master's degree
PhD
Nice to have: PhD in Computer Science or related field
Nice to have: optimizing and deploying ML models to resource-constrained hardware
Machine Learning Engineer Semi-Senior responsible for designing, implementing, and operating generative ML solutions for product catalog content generation and correction, and contributing to the group’s ML framework and MLOps practices.
Responsibilities
Design, implement, and operate ML and generative AI solutions in production
Generate and correct product catalog content from text and images
Contribute to the group’s ML framework and standardize MLOps practices
Develop prototypes and move them to end‑to‑end production
Apply MLOps standards such as CI/CD, monitoring, and retraining
Designing and developing full-stack AI applications for space robotic systems, command and control, and mission operations.
Compensation
Compensation level and base salary are competitively structured and thoughtfully determined based on factors such as relevant skills, experience, education, and the scope of the role.
Responsibilities
Design, develop, deploy, and maintain full-stack AI applications supporting robotic systems, command and control, and Mission Operations
Develop agentic AI systems capable of reasoning, tool use, information retrieval, workflow execution, and interaction with external software and services
Build AI tools for natural-language command and control, mission planning, telemetry analysis, anomaly investigation, mission summarization, and operator decision support
Develop backend services, APIs, data pipelines, and application interfaces that connect AI systems with operational data and robotic platforms
Build intuitive web-based interfaces that enable operators to interact with AI agents, robotic systems, telemetry, and mission data
Design and develop advanced AI algorithms for next-generation video analytics, leveraging modern AI models and architectures to solve complex video understanding challenges.
Responsibilities
Research and explore state-of-the-art AI models, Computer Vision techniques, and emerging technologies to identify new opportunities for advancing video analytics capabilities.
Develop algorithms and solutions for challenging video analytics and video understanding problems, leveraging Computer Vision, deep learning, LLMs, VLMs, and other relevant AI technologies.
Experiment and evaluate different models, algorithms, and approaches, build proof-of-concepts, and assess their performance and suitability for real-world applications.
Integrate algorithms and AI models into existing software systems, collaborating with software engineers to deliver complete product capabilities.
Optimize algorithms and model inference for performance, accuracy, and resource utilization, meeting production requirements and hardware constraints.
Requirements
Master's degree in Computer Science, mathematics, EE or a related field
Als AI Engineer bij Omoda Brands bouw je de AI-functionaliteit die klantgerichte toepassingen mogelijk maakt, zoals de AI Stylist en interne processen.
Locations (3)
Amsterdam, North Holland, Netherlands
Zierikzee, Zeeland, Netherlands
Zwolle, Overijssel, Netherlands
Responsibilities
Bouwt AI-functionaliteit die informatie begrijpt, antwoorden of content genereert en gecontroleerd acties uitvoert.
Combineert modellen, bedrijfsdata, retrieval en tools tot werkende toepassingen.
Ontwikkelt, deployt en beheert de services en API’s achter die toepassingen.
Zet evaluaties op en behandelt prompts en configuraties als geversioneerde, geteste onderdelen.
Bouwt logging, monitoring, foutafhandeling en terugvalopties vanaf het begin in.
Staff Research Engineer, Expressive Motion and Believability, DeepMind
Work with omni/omni-live to extract animation, fine tune models to generate real time behavior and expression in response to stimulus and internal motivation of the character.
Design, train and evaluate reinforcement learning and imitation learning algorithms for lifelike movement, gaze and posture, conditioned on the robot's affective and motivational state.
Work with animators to convert a library of hand-authored reference motion into style-conditioned policies, using adversarial motion priors and related techniques, so that character can be authored rather than hand-tuned.
Create scenario suites, batch simulation, video capture, and automated critics that assess behavior reads correct to an observer.
Agent Personalization and Memory EvolutionNewLenovo · Beijing, China
Senior Agentic AI EngineerNewAdvanced Micro Devices, Inc · Beijing, China
Senior Manager, Engineering - Hivemind Reasoningf7d8749a-72d6-407c-bf98-292ea630c73a · San Mateo, US
ML Engineer - Retail - Python, RAG, LLM´s, GCPOption · Santiago, Chile
Full Stack AI EngineerLunar Outpost · Golden, US
AI Algorithm Developer – Video Analytics & AIMilestone Careers · Modiin, Israel
Mid-Level AI EngineerClera-AI · San Francisco, US
Staff Research Engineer, Expressive Motion and Believability, DeepMindGoogle · San Francisco, US+1 more