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HomeCompaniesAgile Robots SeSenior AI Research Engineer (m/w/d) Foundation Models

Senior AI Research Engineer (m/w/d) Foundation Models

Agile Robots Se · Germany, Munich (HQ) · Hybrid · Active · Personio

Job facts

FieldValue
CompanyAgile Robots Se
TitleSenior AI Research Engineer (m/w/d) Foundation Models
Normalized title-
Department / teamAI Solutions / Artificial Intelligence & Data
LocationGermany, Munich (HQ)
Work modelHybrid / Hybrid
Employment typeFull Time
Salary-
Statusactive
ATS providerPersonio
Posted / first seen2026-03-03 / 2026-05-30
Changed / last seen2026-05-30 / 2026-06-06

Related slices

PageWhat it containsOpen
Company jobsActive postings from Agile Robots Se.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through Personio.Open
Provider filtered searchThe same provider as a filtered job collection.Open
Department jobsActive postings in AI Solutions.Open
Work model jobsActive Hybrid postings.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyAgile Robots Se
Sourcebcb1fbae-6077-4ee3-833d-67baf488bf90
ATS providerPersonio

Description

About the role The AI Research Division of Agile Robots  is looking for a Senior AI Research Engineer focused on learning-based robot policies and multimodal foundation models for embodied intelligence. The role centers on designing representations and objectives that map perception and language to robot behavior under real-world uncertainty, with ownership of model-level decisions and behavioral outcomes. Your Responsibilities Policy Design: Design and train vision- and language-conditioned robot policies using imitation learning, diffusion-based approaches, or transformer architectures, with clear linkage between representation, objective, and resulting behavior. Model Architecture: Define and adapt model architectures, inductive biases, and training objectives for embodied learning under uncertainty beyond standard fine-tuning workflows. Behaviour Analysis: Analyze policy behavior and failure modes under distribution shift, embodiment constraints, and real-world noise, and refine models to improve robustness and generalization. Learning Pipelines: Own end-to-end learning pipelines from data assumptions through training, evaluation, and controlled real-world validation in collaboration with robotics teams. Integration Collaboration: Collaborate closely with robotics, perception, and platform teams to ensure learned policies align with action spaces, timing constraints, and system-level requirements. Research Awareness: Track and critically evaluate advances in robot learning, multimodal foundation models, and vision-language-action systems, assessing their applicability to embodied intelligence. Essential Skills Education: Master’s or PhD in Computer Science, Robotics, AI, or a related technical field. Robot Learning: Hands-on experience designing and training learning-based policies for robotics using imitation learning, reinforcement learning, or hybrid approaches. Multimodal Models: Experience adapting or designing transformer-based, diffusion-based, or vision-language-action models for embodied tasks. Modelling Depth: Ability to reason about representations, objectives, inductive biases, and behavioral trade-offs, including explaining why a model is structured in a particular way. Behavioural Ownership: Demonstrated responsibility for model-level outcomes, including debugging policy failures and improving robustness in real-world or semi-real environments. ML Engineering: Strong Python skills and hands-on experience with modern ML frameworks such as PyTorch or TensorFlow, including implementation of training loops and experimentation workflows. Robotics Context: Understanding of how learned policies interact with perception outputs, action spaces, and physical constraints in robotic systems. Beneficial Skills Large-Scale Training: Experience with distributed training, GPU clusters, or large-scale experimentation workflows. Predictive Components: Experience leveraging predictive or world-model elements to improve policy robustness or long-horizon behavior. Embodied Evaluation: Experience validating learned policies on real robotic systems or in high-fidelity simulation with sim-to-real considerations. Research Output: Publications, patents, or deployed systems in robotics, multimodal learning, or embodied AI. What we offer Dynamic high-tech company combined with financial soundness and world class investors. Join an interdisciplinary, international team with 60+ different nationalities in a collaborative work environment. Lots of development opportunities in the context of our continued growth. Challenging tasks and impactful projects alongside experts that enable professional and personal growth. Corporate Benefits Program that covers health, mobility and learning with 100 € net per month. Modern office facilites with a rooftop terrace overlooking Munich, free drinks & fruits, and regular company events contribute to a good working environment.

Full job record

Job ID4e8b8a697a61d0609a2b5cbdeef176bd134bcac2
Org IDcbcab16d-d77f-4aae-95e4-f537194009c8
Source IDbcb1fbae-6077-4ee3-833d-67baf488bf90
Board IDbcb1fbae-6077-4ee3-833d-67baf488bf90
Providerpersonio
Provider Job Key2551914
TitleSenior AI Research Engineer (m/w/d) Foundation Models
Normalized Title
Statusactive
Activeyes
Location TextGermany, Munich (HQ)
DepartmentAI Solutions
TeamArtificial Intelligence & Data
Employment Typefull_time
Workplace Typehybrid
Remote Policyhybrid
Country
Region
City
Salary Raw
Salary Min
Salary Max
Salary Currency
Salary Period
Source URLhttps://agile-robots-se.jobs.personio.de/job/2551914?language=en
Apply URLhttps://agile-robots-se.jobs.personio.de/job/2551914?language=en
First Seen At2026-05-30 06:05:02Z
Last Seen At2026-06-06 07:54:12Z
Last Checked At2026-06-06 07:54:12Z
Last Changed At2026-05-30 06:05:02Z
Inactive At
Source Posted At2026-03-03 14:09:41Z
Source Updated At
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=personio/board=agile-robots-se.de/date=2026-06-06/2026-06-06T07-54-11-954Z-784a826e6f3211779d98f861e26fa61d9ac5e980c0b6fc37cf918f969809edb8.json
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Extensions
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Native Structured
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    {
      "name": "About the role",
      "value": "<strong><span style=\"font-family:Arial, Helvetica, sans-serif;font-size:14px;\">The AI Research Division of Agile Robots </span></strong><span style=\"font-family:Arial, Helvetica, sans-serif;font-size:14px;\">is looking for a Senior AI Research Engineer focused on learning-based robot policies and multimodal foundation models for embodied intelligence. The role centers on designing representations and objectives that map perception and language to robot behavior under real-world uncertainty, with ownership of model-level decisions and behavioral outcomes.</span><br><br>"
    },
    {
      "name": "Your Responsibilities",
      "value": "<ul><li style=\"font-family:Arial;font-size:14px;\"><strong>Policy Design:</strong> Design and train vision- and language-conditioned robot policies using imitation learning, diffusion-based approaches, or transformer architectures, with clear linkage between representation, objective, and resulting behavior.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Model Architecture:</strong> Define and adapt model architectures, inductive biases, and training objectives for embodied learning under uncertainty beyond standard fine-tuning workflows.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Behaviour Analysis:</strong> Analyze policy behavior and failure modes under distribution shift, embodiment constraints, and real-world noise, and refine models to improve robustness and generalization.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Learning Pipelines:</strong> Own end-to-end learning pipelines from data assumptions through training, evaluation, and controlled real-world validation in collaboration with robotics teams.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Integration Collaboration:</strong> Collaborate closely with robotics, perception, and platform teams to ensure learned policies align with action spaces, timing constraints, and system-level requirements.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Research Awareness:</strong> Track and critically evaluate advances in robot learning, multimodal foundation models, and vision-language-action systems, assessing their applicability to embodied intelligence.</li></ul>"
    },
    {
      "name": "Essential Skills",
      "value": "<ul><li style=\"font-family:Arial;font-size:14px;\"><strong>Education:</strong> Master’s or PhD in Computer Science, Robotics, AI, or a related technical field.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Robot Learning:</strong> Hands-on experience designing and training learning-based policies for robotics using imitation learning, reinforcement learning, or hybrid approaches.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Multimodal Models:</strong> Experience adapting or designing transformer-based, diffusion-based, or vision-language-action models for embodied tasks.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Modelling Depth:</strong> Ability to reason about representations, objectives, inductive biases, and behavioral trade-offs, including explaining why a model is structured in a particular way.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Behavioural Ownership:</strong> Demonstrated responsibility for model-level outcomes, including debugging policy failures and improving robustness in real-world or semi-real environments.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>ML Engineering:</strong> Strong Python skills and hands-on experience with modern ML frameworks such as PyTorch or TensorFlow, including implementation of training loops and experimentation workflows.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Robotics Context:</strong> Understanding of how learned policies interact with perception outputs, action spaces, and physical constraints in robotic systems.</li></ul>"
    },
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      "name": "Beneficial Skills",
      "value": "<ul><li style=\"font-family:Arial;font-size:14px;\"><strong>Large-Scale Training:</strong> Experience with distributed training, GPU clusters, or large-scale experimentation workflows.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Predictive Components:</strong> Experience leveraging predictive or world-model elements to improve policy robustness or long-horizon behavior.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Embodied Evaluation:</strong> Experience validating learned policies on real robotic systems or in high-fidelity simulation with sim-to-real considerations.</li><li style=\"font-family:Arial;font-size:14px;\"><strong>Research Output:</strong> Publications, patents, or deployed systems in robotics, multimodal learning, or embodied AI.</li></ul>"
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    }
  ],
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  "recruitingCategory": "Artificial Intelligence & Data"
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