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senior applied ai / ml data engineer (f/m/x) Jobs in Munich

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OMEGGA

<div><h2 class="jobSectionHeader">Our Mission</h2>
At Omegga, we're on a mission to reinvent how AI and spectroscopy can drive positive change for animals, people and the planet. We started with a clear objective: eliminate chick culling on a global scale through early, non-invasive in-ovo sex detection. This outdated practice still impacts billions of lives, and we're here to change that. We're not building for the niche, we're building for the global standard.
Today, our systems are already in use, and the focus is on scaling performance, robustness, and deployment across an international customer base. For us, scaling means more than growth. It means changing an industry for the better.
We are looking for people who take ownership, think in systems, and want to help turn a working product into the industry benchmark. If that sounds like you, we'd love to hear from you.<h2 class="jobSectionHeader"> 
What We Are Looking For</h2>
We are looking for a Deep Learning Engineer with strong research depth and hands-on implementation skills to push our model performance forward.
You will work on deep learning architectures and iterate rapidly: from hypothesis and experiments to evaluation, monitoring, and integration into our algorithm stack. Your work will directly improve detection quality and robustness in a real-world, noisy environment.<h2 class="jobSectionHeader"> 
Your Mission: What You'll Do</h2><ul><li>
Advance our deep learning models through research iteration: develop and test architectural improvements, training objectives, and optimization techniques. You will own the loop from idea experiment conclusion next iteration.</li><li>
Build rigorous evaluation and benchmarks: define evaluation sets, establish clear metrics (precision, recall, accuracy, calibration), and create repeatable benchmark runs so improvements are measurable and comparable over time.</li><li>
Own monitoring of model quality: set up monitoring for model performance and data shifts, define alerting signals, and build lightweight reporting that makes regressions visible early.</li><li>
Partner cross-functionally to turn findings into impact: work with data/engineering teams to improve datasets and labeling strategies, and with product/ops stakeholders to align on what &ldquo;good&rdquo; looks like in practice.</li></ul><h2 class="jobSectionHeader"> 
Your Profile: Qualifications &amp; Requirements</h2><ul><li>
MSc in Computer Science or a related field with 4+ years of applied deep learning experience, or a PhD &mdash; paired with a proven track record of taking research from idea to working system.</li><li>
Strong understanding of optimizing modern neural architectures, with demonstrated depth in transformer architectures and practical experience adapting them to specific domains (e.g., attention variants, efficiency improvements, robustness, training stability).</li><li>
Ability to architect problem-specific models &mdash; adapt modern architectures, losses, and training objectives to the structure of the task, treating published work as a starting point rather than reaching for an off-the-shelf model</li><li>
Strong Python deep learning stack experience (e.g. PyTorch, Tensorflow), including training pipelines, experimentation discipline, and reproducibility.</li><li>
Real depth in the mathematical core of modern ML: probability, optimization, information theory, statistical inference. You can derive a loss function rather than just import one.</li><li>
Solid experience with experiment tracking and model evaluation tooling (e.g. Weights &amp; Biases or similar), and a strong bias for measurement-driven progress.</li><li>
Fluency in English, German is a plus</li></ul><h2 class="jobSectionHeader"> 
Nice to have</h2><ul><li>
Experience with domain adaptation and evaluation in imbalanced settings (rare events, high cost of misses/false alarms).</li><li>
Familiarity with deployment-adjacent concerns: model packaging, performance constraints, and continuous evaluation in changing real-world conditions.</li><li>
Experience working with sensor/time-series or industrial data, where edge cases and dataset shifts are the norm.</li><li>
Experience with agentic AI development workflows to speed up experimentation (analysis, ablations, test scaffolding) while maintaining careful review and scientific rigor.</li><li>
Proven ability to deliver in fast-paced, high-ambiguity environments.</li></ul><h2 class="jobSectionHeader"> 
Why Us</h2>
Mission &amp; Technical Challenge: At Omegga, you work on problems that are both technically demanding and globally relevant. We build deep-tech solutions with real-world impact, pushing the boundaries of what&rsquo;s currently possible.
Relocation Support: To make your move to Munich smooth, we offer a fully furnished apartment within walking distance of the office for up to 6 months.
Work Environment: Located in Munich&rsquo;s Werksviertel, we combine a focused, high-performance culture with flexibility. Up to 50% remote work, depending on role and execution needs.
Compensation: A competitive, market-aligned salary that reflects your contribution, with clear linkage to impact.
Time Off: 28 vacation days per year, plus December 24th and 31st off.</div>

Senior Deep Learning Engineer (m/f/d)

Luma Vision

<div>LUMA Vision is a start-up based in Dublin (Ireland) and Munich (Germany) developing innovative imaging technologies to advance healthcare and solve global unmet clinical needs. We apply rigorous engineering and design to create new hardware, software and data-science technologies towards clinical procedures. Our focus is on developing sophisticated ultrasound cardiac imaging and navigation systems that provide essential real-time visualisation for advanced electrophysiology and structural heart procedures, leveraging diverse technologies to transform patient care.
To drive our platform development, we are looking for a Medical Deep Learning Computer Vision Engineer to join our growing team in our quest to produce high-quality medical device software. You will become part of a dynamic group of innovators, shaping the future of imaging in healthcare. 
Your role with us:
As a Medical Deep Learning Computer Vision Engineer, you will be responsible for working across research, technology development, and clinical application development. Your expertise makes you ready to own technical modules and processes end-to-end, working collaboratively within the team on both classical computer vision and artificial intelligence (AI) towards shipping production-quality code in Python and, where performance demands it, C++ and CUDA.
Your technical work will span from low-level signal processing to advanced deep learning and contribute to large-scale dataset preparation and curation. You will engage across research, hardware, clinical, and regulatory teams and help guide junior engineers through code reviews and technical design. You are comfortable using GIT and CI/CD to collaborate on and deliver code that is tested, documented, and ready for a regulated medical device environment. 
What you offer:<ul><li>
BSc or MSc degree in computer science, biomedical engineering or a related field.</li><li>
3+ years of professional experience in image processing, computer vision or deep learning for real-world applications</li><li>
Strong proficiency in Python with demonstrated ability to write production-grade code.</li><li>
Professional experience in deep learning development for computer vision.</li><li>
Proven ability to read, implement and critically evaluate state-of-the-art approaches from literature.</li><li>
Expertise with 3D (and ideally 4D) data. Familiarity with medical imaging is highly preferred.</li><li>
Experience working in a regulated environment or a strong appreciation for software quality practices including testing, documentation and validation.</li><li>
Hands-on, collaborative problem-solving mindset and excellent communication skills in English.</li></ul> 
Nice-to-Have:<ul><li>
Experience with C++ and/or CUDA.</li><li>
Experience with medical imaging (cardiac focus and/or ultrasound a plus)</li><li>
Exposure to MLOps and model deployment.</li><li>
Secure data handling with GDPR compliance for sensitive (healthcare) data.</li></ul> 
What we offer:<ul><li>
Ownership of meaningful work, your code will directly impact patient care and clinical outcomes.</li><li>
A growing and collaborative multinational team with a strong focus on team culture and individual development.</li><li>
Access to global network of clinical, technical and business innovators.</li><li>
Competitive salary with participation in employee participation scheme</li><li>
Flexible hybrid working with ergonomic offices in Dublin and Munich, both easily reachable by public transport.</li></ul>
LUMA Vision is an equal opportunity employer and welcomes applications from all qualified individuals regardless of ethnicity, sex, disability, religion/belief, sexual orientation or age.
Would you like to become part of our team?
We would love to hear from you!</div>

Medical Deep Learning Computer Vision Engineer (m/w/d)

osapiens

<div>
 
 Department:
 
 osapiens Terra
 
 
 
 Location:
 
 M&uuml;nchen
 
 
 
 Description
 
 
 
 
 About the role
 
 
 
 As a Machine Learning Engineer (m/f/x), you will work on the machine learning systems powering our geospatial analytics platform. You will contribute to the development, deployment, and improvement of production ML models that analyze satellite imagery and detect environmental risks at scale. You will collaborate closely with product managers, software engineers, and geospatial experts to build scalable ML solutions that support real-world sustainability and compliance use cases. Your work may include improving existing deforestation detection models, developing new deep learning approaches for remote sensing data, and building reliable tooling and infrastructure for model training and deployment.
 
 
 
 Your Responsibilities
 
 
 
 <ul>
 <li>
 Develop, train, and improve machine learning models for geospatial and satellite imagery analysis
 </li>
 <li>
 Contribute to the full ML lifecycle, including experimentation, evaluation, deployment, monitoring, and maintenance
 </li>
 <li>
 Work on production systems that process large-scale satellite and geospatial datasets
 </li>
 <li>
 Collaborate with ML engineers, backend engineers, product teams, and geospatial analysts to deliver reliable analytics products
 </li>
 <li>
 Improve model performance, scalability, and robustness across different geographies and datasets
 </li>
 <li>
 Build and optimize data pipelines, tooling, and workflows for efficient ML development
 </li>
 <li>
 Apply modern deep learning and statistical techniques to remote sensing and environmental data
 </li>
 <li>
 Support rapid experimentation while maintaining production reliability
 </li>
 <li>
 Contribute to technical discussions, code reviews, and engineering best practices
 
 
 </li>
 </ul>
 
 You May Be a Good Fit If You
 
 
 
 <ul>
 <li>
 Have a strong quantitative background in computer science, engineering, mathematics, remote sensing, or a related field
 </li>
 <li>
 Have strong programming skills in Python and hands-on experience with modern deep learning frameworks such as PyTorch or TensorFlow
 </li>
 <li>
 Hands-on experience with MLOps tools such as Weights &amp; Biases, cloud infrastructure including Amazon Web Services, and/or high-performance computing environments
 </li>
 <li>
 Feeling comfortable developing, training, and optimizing machine learning models in production environments
 </li>
 <li>
 Communicate clearly and collaborate effectively across technical and non-technical teams
 </li>
 <li>
 Have experience working with large datasets and distributed data processing workflows
 </li>
 <li>
 Work effectively with AI-assisted development and coding tools
 </li>
 <li>
 Are fluent in English (C1+). German skills are a plus
 
 
 </li>
 </ul>
 Nice to have
 
 
 <ul>
 <li>
 Experience with computer vision tasks such as segmentation, classification, change detection, or time-series analysis
 </li>
 <li>
 Experience working with remote sensing or satellite data (SAR, optical, LIDAR)
 </li>
 <li>
 Familiarity with geospatial data processing libraries and tools
 </li>
 <li>
 Experience deploying ML systems into production environments
 </li>
 <li>
 Understanding of environmental, climate, or sustainability-related use cases
 </li>
 <li>
 Experience translating complex business or regulatory requirements into data-driven solutions
 
 
 </li>
 </ul>
 
 Join us for this and more...
 
 
 
 <ul>
 <li>
 The opportunity to work on meaningful technology with real-world environmental impact
 </li>
 <li>
 A highly technical and collaborative team environment
 </li>
 <li>
 Modern ML infrastructure and large-scale geospatial datasets
 </li>
 <li>
 Ownership and growth opportunities based on your experience and interests
 </li>
 <li>
 Flexible working environment in our Munich office near Sendlinger Tor
 </li>
 <li>
 Competitive compensation and benefits
 </li>
 </ul>
</div>

Machine Learning Engineer (m/f/x) - Geospatial AI

JetBrains

<div>
 At JetBrains, code is our passion. Ever since we started, back in 2000, we&rsquo;ve been striving to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.
 
 
 We&rsquo;re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you&rsquo;ll be responsible for the models, training loops, and evaluation pipelines that power these agents.
 
 
 You&rsquo;ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into JetBrains products.
 
 
 
 As Part Of Our Team, You Will
 
 
 
 <ul>
 <li>
 Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
 </li>
 <li>
 Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
 </li>
 <li>
 Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
 </li>
 <li>
 Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.
 </li>
 <li>
 Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.
 </li>
 <li>
 Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.
 </li>
 <li>
 Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features.
 
 
 </li>
 </ul>
 We&rsquo;ll be happy to bring you on board if you have:
 
 
 <ul>
 <li>
 Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
 </li>
 <li>
 Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
 </li>
 <li>
 Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.
 </li>
 <li>
 The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
 </li>
 <li>
 A product-aware mindset &ndash; you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.
 </li>
 <li>
 At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.
 
 
 </li>
 </ul>
 
 Our Ideal Candidate Would Have Experience With
 
 
 
 <ul>
 <li>
 ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.
 </li>
 <li>
 Large-scale data and training pipelines, e.g. MapReduce-style clusters, multi-node GPU training, or workloads on the order of 1M+ CPU/GPU hours.
 </li>
 <li>
 Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.
 </li>
 <li>
 AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.
 </li>
 <li>
 Experiment tracking and observability using tools like Weights &amp; Biases, MLflow, Langfuse, or similar.
 </li>
 <li>
 Inference optimization and serving optimized models in production.
 
 
 </li>
 </ul>
 
 We are an equal opportunity employer
 
 
 
 We know great ideas can come from anyone, anywhere. That&rsquo;s why we do our best to create an open and inclusive workplace &ndash; one that welcomes everyone regardless of their background, identity, religion, age, accessibility needs, or orientation.
 
 
 
 We process the data provided in your job application in accordance with the Recruitment Privacy Policy.
 
 
 
</div>

Research Engineer (Agentic Models)

Abbott

<div>About Abbott
Abbott is a global healthcare leader, creating breakthrough science to improve people&rsquo;s health. We&rsquo;re always looking towards the future, anticipating changes in medical science and technology.
In Germany, Abbott has more than 4,000 employees working in manufacturing, research and development, logistics, manufacturing, sales and marketing. They are located at Abbott`s German headquarters in Wiesbaden and its sites in Hanover, Neustadt am R&uuml;benberge, Wetzlar, Eschborn, Cologne, Jena and Hamburg.
Working at Abbott
At Abbott, you can do work that matters and help people to live a healthier and full life, grow your career, and learn, be your true self. You will have access to:
<ul><li>Career development with an international company where you can grow the career you dream of</li><li>
An attractive benefits package (e.g. attractive Abbott Pension Plan, a company bike, employee stock purchase program)</li><li>
A company recognized as a great place to work in dozens of countries around the world and named one of the most admired companies in the world by Fortune.
</li><li>A company that is recognized as one of the best big companies to work for as well as a best place to work for diversity, working mothers, female executives, and scientists.
</li><li>A challenging position in a fast-growing crisis independent industry</li><li>
To become part of a dynamic, highly educated, highly skilled, and motivated team</li><li>
Flat hierarchies, open appreciative mentality, and efficient, constructive lines of communication</li><li>
Multi-national environment, where we foster the development of our talents within the enterprise</li></ul>
The Opportunity
This position works out of our Munich location in the Laralab GmbH.
Your Tasks:<ul><li>
Develop algorithms combining deep learning, computational geometry, and medical imaging for heart.ai, our cloud-based software supporting cardiac interventions.</li><li>
Contribute to 3D segmentation, modeling, and measurement pipelines for clinically relevant planning workflows.</li><li>
Design, train, and validate DL models and geometry methods for integration into the heart.ai platform.</li><li>
Collaborate with Product and Engineering teams to translate clinical needs into robust algorithm designs.</li><li>
Support work with Clinical, Regulatory, and Quality teams to ensure compliance with medical device development standards.</li><li>
Assist with and contribute ideas for improving algorithm accuracy, robustness, and computational efficiency while contributing to research and exploration of new methods.</li></ul>
Your Qualification:<ul><li>
BSc or MSc in Computer Science, Biomedical Engineering, Applied Mathematics, or a related technical field.</li><li>
Experience in developing deep learning models and computational geometry algorithms, especially deriving measurements from 3D segmentations.</li><li>
Solid software development skills, particularly in Python, and ideally experience with TypeScript and cloud products.</li><li>
Background in medical image analysis (CT/MRI/echo), including 3D data processing, mesh handling, and geometric feature extraction.</li><li>
Experience or displayed interest in algorithm optimization, numerical stability, and rigorous validation methodologies.</li><li>
Ideally first experience collaborating with engineering, product, clinical, and quality teams; ideally within medical or regulated environments.</li><li>
Strong communication skills, structured problem‑solving capabilities, and a high sense of ownership.</li></ul>
Follow your career aspirations to Abbott for diverse opportunities with a company that can help you build your future and live your best life. Abbott is an Equal Opportunity Employer, committed to employee diversity.
We ask for your understanding that we only consider online applications via our online application portal. Applications by email or post cannot be processed. Original documents will not be returned. 
Connect with us at www.abbott.com, on LinkedIn at https://www.linkedin.com/company/abbott</div>

Software Engineer (m/f/d) Deep Learning and Algorithms

<div><h3 class="jobSectionHeader">Description</h3><div> 
This 4-6 months internship position is part of osapiens Terra, a specialized team in Munich integrating remote sensing data and artificial intelligence algorithms in sustainability software solutions. We build enterprise software for deforestation free supply chains. Our SaaS solution ensures compliance with the EU Deforestation Regulation (EUDR) which affects thousands of European companies and their suppliers. For this, we track the supply chain end-to-end, from farmer to retailer, while leveraging satellite data and AI to detect deforestation. 
 
Our algorithm combines several methods, from deep learning, to custom ML algorithms, and basic statistics. We carefully engineer our methodology to ensure generalization across geographies, robustness, and explainability.</div><h3 class="jobSectionHeader"> 
Your Responsibilities</h3><div> 
We are looking for a Machine Learning Engineer to improve our algorithms, scale our architecture and optimize a production ML pipeline. You will... 
</div><ul><li>Design and implement geospatial analytics using state-of-the-art deep learning techniques and statistics</li>
<li>Research cutting-edge methods from Computer Vision and Artificial Intelligence and apply them to remote sensing</li>
<li>Engineer and operationalize prototype-stage methods to full-blown enterprise-grade production systems</li>
<li>optimize and scale data and analytics pipelines</li>
<li>Analyze, interpret, and monitor performance from production data analytics pipelines</li></ul> 
<h3 class="jobSectionHeader">
Your Experience</h3> 
<ul>
<li>
Current studies in engineering, mathematics, computer science or similar.</li>
<li>Programming skills in Python and proficiency with typical data engineering and deep learning frameworks, as well as familiarity with GIT and unix-based systems.</li>
<li>Track record of solving quantitative problems with algorithms and data</li>
<li>Experience in working with geospatial data (e.g., multi-spectral satellite imagery, geojsons, tif formats, etc.) is a plus</li></ul><div></div><h3 class="jobSectionHeader"> 
Join us for this and more...</h3> 
<ul>
<li>
A purpose-driven mission tackling complex sustainability challenges while working alongside global industry pioneers at a fast-growing unicorn company
</li><li>A high-impact internship with responsibility from day one and direct collaboration with experienced leaders.</li>
<li>Hands-on project ownership and the chance to design processes that scale across our global customer base.</li>
<li>A modern Munich office with flexible hybrid work options, open team culture, and flat hierarchies.</li>
<li>Regular team events and knowledge-sharing sessions.</li>
<li>And of course &ndash; free drinks and snacks to keep you energized during your day.</li></ul><div></div><h3 class="jobSectionHeader"> 
About osapiens</h3><div>
osapiens develops holistic Software-as-a-Service solutions that help global companies ensure transparency, efficiency, and trust across their entire value chain. Through its cloud platform, the osapiens HUB, the company leverages innovative technologies, including artificial intelligence, to strengthen businesses while promoting human rights, ecological responsibility, and sustainable corporate governance. 
</div><div>Founded in 2018 and headquartered in Mannheim, Germany, osapiens works with around 2,500 companies in over 50 countries across industries such as consumer goods, automotive, fashion, pharmaceuticals, and medical products. The company is backed by Goldman Sachs and Decarbonization Partners, reinforcing its commitment to responsible, sustainable growth and has reached unicorn status in January 2026.</div></div>

Internship: Machine Learning (m/f/x)

techtree

About the RoleA leading frontier AI company is looking for an Applied AI Engineer to join their Solutions team in Munich.This is a customer-facing technical role, but the balance is heavily technical: roughly 20% involves presales support, onboarding, and client guidance - the other 80% is designing, developing, and deploying AI solutions for B2B enterprise clients.Your stakeholders are technical. You won't be simplifying things for non-technical audiences - you'll be working directly with data scientists, software engineers, and CTOs on complex, high-impact implementations.This is not a consultancy role. You'll operate with the mindset of a startup CTO embedded in a world-class AI team: owning projects end-to-end, shipping working solutions, and collaborating closely with researchers and product engineers on the company's most challenging customer problems.What You'll Do<ul><li>Deploy AI solutions into production across various industries, delivering measurable business impact</li><li>Build state-of-the-art GenAI applications - from consumer products to industrial use cases</li><li>Develop complex implementations involving fine-tuning, advanced RAG pipelines, agentic workflows, and custom LLM applications</li><li>Participate in presales calls to understand client needs and provide technical guidance</li><li>Collaborate with researchers and product engineers on the team's most demanding customer projects</li><li>Feed customer insights back into product and model development</li></ul>How the Team Works<ul><li>What matters is what you ship, not the time you spend on it</li><li>No bureaucracy - you talk to whoever you need to, the best idea wins</li><li>Low ego, high standards; direct, honest feedback</li><li>Unstructured environment where people find joy in ambiguity</li></ul>About You<ul><li>Fluent in English and German (required for this Munich-based role)</li><li>2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products</li><li>Proven experience implementing AI/ML products with APIs, back-end and front-end interfaces</li><li>Hands-on experience with fine-tuning LLMs, advanced RAG, or agentic use cases</li><li>Deep understanding of machine learning concepts and the algorithms underlying LLMs</li><li>Strong Python coding skills</li><li>Able to explain complex technical concepts clearly to technical audiences</li></ul>Nice to Have<ul><li>Open-source contributions, especially in the LLM space</li><li>Prior experience as a Customer Engineer, Forward Deployed Engineer, Solutions Architect, or Technical PM</li><li>Experience with PyTorch and deep learning</li></ul>Compensation<ul><li>Base salary + equity</li></ul>Job Type: Full-timePay: 61.504,42&euro; per yearApplication Question(s):<ul><li>Cover letter</li><li>Expected Salary</li></ul>Work Location: In person

Applied AI Engineer

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