Who we are

The role

At Helsing, we are redefining perception by building foundational intelligence for the physical world. You will research, design, and train large-scale Foundational Models that transform complex multimodal sensor data into cutting-edge autonomous capabilities.

We are seeking an individual at the intersection of AI Research and Machine Learning Engineering, with a proven track record in LLM/VLM/(multimodal) backbones. Your primary focus will be training and fine-tuning Vision-Language Models on our custom datasets to power our different products. You'll be responsible for the entire model lifecycle, from data curation and training to evaluation.

You should apply if you

• Hold an MSc or PhD in Machine Learning, Computer Science, or Mathematics with a focus on Deep Learning.

• Possess theoretical understanding and practical experience in training Foundational Models (LLMs, VLMs, or other large-scale multimodal models) from scratch or through fine-tuning.

• Have strong software engineering skills in Python and fluency with modern DL frameworks (PyTorch/JAX). You don’t just import libraries; you are comfortable writing custom layers, loss functions, and distributed training loops.

• Are a clear communicator who can build from complex theoretical concepts and contribute to the company's internal research culture.

• Have a "first-principles" mindset: you enjoy reading the latest ArXiv papers and implementing them into the codebase rapidly.

Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points.

Nice to have

• Top-Tier Research Track Record: You have authored publications in top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ACL) specifically regarding attention mechanisms, efficient transformers, or multimodal learning.

• Experience training models on large-scale GPU clusters.

• Proven experience in data curation, data cleaning, data pruning, and building robust data pipelines for large-scale datasets.

Join Helsing and work with world-leading experts in their fields

• Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns. 

• The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world. 

• Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward. 

• In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts.  

• We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about. 

What we offer

• Competitive salary and VSOP options

• Relocation support: up to €2,500 and 4 weeks temporary accommodation

• Learning: €500/£450 yearly allowance

• Health & wellness: gym membership and mental health support (Nilo.health)

• Social: regular company events and monthly social allowances

• Enhanced parental leave: 22 weeks fully paid for primary caregivers & 6 weeks for secondary caregivers

• Family support: 5 days of paid family emergency leave, 100% remote work option during pregnancy and phased return to work

These are the core benefits across all locations, there may be additional benefits in certain locations.

Helsing is an equal opportunities employer. We are committed to equal employment opportunity regardless of race, religion, sexual orientation, age, marital status, disability or gender identity. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation. 

About The Role

The Autonomy Team is seeking a Senior Perception Engineer to help bring ARX Robotics robot autonomous capabilities the next level. This role involves developing and productizing innovative solutions that will transform the field manned and unmanned ground vehicles.

You will collaborate with talented and dedicated cross-functional engineering teams across various robotic sub-systems to integrate their work into a cohesive platform for operating autonomous and teleoperated systems in the field, ensuring compliance with all relevant standards and guidelines.

The work is exciting, impactful, and enjoyable, with deadlines, customers, and competition to navigate.

What You’ll Build

• Design and implement perception algorithms for object detection, terrain classification, and obstacle segmentation in off-road environments

• Fuse sensor data from LiDAR, cameras, radar, IMU, and GNSS for robust environmental understanding elevation mapping and free-space detection

• Apply machine learning and deep learning techniques for improved scene understanding in dynamic outdoor environments

• Optimize real-time perception pipelines for embedded hardware under size, weight, and power (SWaP) constraints

• Conduct field testing in challenging conditions (mud, dust, rain) to validate and improve algorithm robustness

• Integrate perception outputs with navigation and path planning systems for safe autonomous traversal of unstructured terrain

• Utilize simulation tools for development and validation of perception systems before field deployment

• Stay current with advancements in sensor technologies, AI, robotics, and industry standards to drive innovation and maintain technical leadership.

What You Bring

• MS or higher in Robotics, Computer Vision, or related engineering fields

• Strong proficiency in C++ and Python software development

• Solid understanding of perception algorithms terrain classification, obstacle detection, segmentation and traversability estimation in dynamic outdoor environments

• Strong experience in developing and deploying machine learning–based perception systems for robotics in real-world, unstructured environments

• Proficiency with ML frameworks (e.g., PyTorch and TensorRT) and perception data processing libraries (e.g., OpenCV, PCL and Open3D)

• Proficiency with ROS2 and simulation tools for algorithm validation

• Proficiency with Linux systems, version control, CI/CD, and software engineering best practices in a robotics context

• Self-motivated, organized, and proactive work style

Please note: You do not need to meet every single requirement to apply. We welcome motivated candidates who are eager to grow into the role and develop their expertise further.

What We Offer

• Competitive salary and benefits package 

• A shaping role in an emerging dual use company 

• Opportunity to work with a highly innovative, international, and dynamic team 

• Room for professional growth and development 

• Access to regular events, knowledge exchange and meetups 

Who we are

Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards.

As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously.

We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications.

The role

At Helsing, we are pioneering the future of autonomous decision-making for defence. Our work spans the full AI landscape, including high-volume data processing, RL agent training, and large-scale foundation models. As a member of a cross-functional team, you will architect and implement the tools and platforms that enable these breakthroughs. Your focus will be on abstracting complex distributed systems to maximise training throughput and developer velocity. We are looking for engineers who can navigate the convergence of machine learning and systems engineering to build robust, scalable platforms.

What you will do:

• Extend our highly integrated deep learning frameworks (built on top of PyTorch), making them efficient and easy to use for a wide range of use cases.

• Scale our current infrastructure and tooling stack to support faster and larger distributed training.

• Design data strategy to support large scale datasets and efficient storage, ensuring GPUs stay warm.

You should apply if you:

• Hold an MSc or PhD in Computer Science or STEM field, with a focus on Machine Learning and Deep Learning.

• Have strong software engineering skills in Python and fluency with modern DL frameworks (PyTorch/JAX/TensorFlow). You don’t just import libraries; you are comfortable writing custom layers, loss functions, and distributed training loops.

• Are a clear communicator who can build from complex theoretical concepts and contribute to the company's internal engineering culture.

• Have a "first-principles" mindset: you enjoy reading the latest AI optimisation blog posts and integrating them into codebases rapidly.

• You have debugged production ML pipelines and can tell a good war story about finding a subtle numerical or performance issue.

Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points.

Nice to have

• You have hands-on experience training models on large-scale GPU clusters, implementing advanced parallelism strategies, and understanding the underlying cross-node communication patterns (NCCL, MPI).

• You have worked with large-scale datasets made of different modalities. Understanding the tradeoffs in locality, encoding, formats, and streaming strategies.

• You are proficient with workload orchestrators like Slurm, Kubernetes, or Ray, having used them to schedule and manage complex, concurrent training jobs at scale.

• You understand GPU architecture at a low level: memory hierarchies, warp execution, and what makes a GPU suited for training versus inference workloads.

Join Helsing and work with world-leading experts in their fields

• Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns. 

• The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world. 

• Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward. 

• In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts.  

• We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about. 

What we offer

• Competitive salary and VSOP options
  
  
• Relocation support: up to €2,500 and 4 weeks temporary accommodation
  
  
• Learning: €500/£450 yearly allowance
  
  
• Health & wellness: gym membership and mental health support (Nilo.health)
  
  
• Social: regular company events and monthly social allowances
  
  
• Enhanced parental leave: 22 weeks fully paid for primary caregivers & 6 weeks for secondary caregivers
  
  
• Family support: 5 days of paid family emergency leave, 100% remote work option during pregnancy and phased return to work

• A hands-on onboarding program (affectionately labelled “AI-duction”), in which you will be familiarising yourself with our tools and ML pipelines used across the company. This is your opportunity to learn our tech stack, explore the company, and learn how we get things done - all whilst working with other AI and engineering teams from day one

These are the core benefits across all locations, there may be additional benefits in certain locations.

Helsing is an equal opportunities employer. We are committed to equal employment opportunity regardless of race, religion, sexual orientation, age, marital status, disability or gender identity. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation. 

Software engineers and AI agents alike suffer from the same problem: finding that one person or place that will answer their tough, specific question. Many solutions promise to solve this with similarity search in vector databases. Unfortunately, finding the answer is often a puzzle with pieces to be collected across a myriad of contradictory sources and cannot be solved without surgical search and careful reasoning.

Spectrum collects data from an organization's code, docs, and issues, and organizes knowledge in a unified ontology that AI agents can efficiently search through and reason over. We aim to revolutionize the semantic layer space for software-building organizations and move beyond specs that fall out of sync with code, introducing a living spec – one that's extracted from the whole system and used to keep it aligned. Spectrum is meant to be the single source of truth for all product and architectural knowledge.

A resident of JetBrains' startup incubator, Spectrum enjoys startup speed and autonomy, and is backed by 25 years of developer tooling expertise. We are looking for a Senior ML Researcher to develop the core methods that make Spectrum possible – novel approaches to temporal ontology extraction, contradiction detection, and semantic alignment across heterogeneous software artifacts. You will help define and execute the research agenda, while also collaborating with JetBrains Research and external academic advisors.

Your responsibilities will include:

• Developing methods for LLM-based ontology construction from code, documentation, and issue trackers, including contradiction detection and reasoning over the resulting knowledge graph.
• Creating datasets, metrics, and benchmarks that drive measurable improvements across the system.
• Prototyping and validating research ideas, then collaborating with ML and software engineers to bring them to production.
• Collaborating with JetBrains Research and external academic advisors to shape our research agenda.
• Publishing research findings at top venues and representing Spectrum in the research community.

We expect you to have:

• A PhD (or equivalent research experience) in NLP, knowledge graphs, ontology learning, information extraction, or a closely related field.
• A strong publication record in at least one of the following areas: knowledge graph construction, ontology learning, information extraction, or NLP.
• Experience applying LLMs to structured knowledge extraction tasks.
• Experience designing and running rigorous experiments, including hypothesis formulation, ablation studies, and statistical evaluation.
• Strong Python and PyTorch skills, with the ability to implement research ideas from scratch.
• Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
• Proficiency in English, both written and verbal.

Our ideal candidate would also have:

• Experience with ontology engineering, semantic web technologies (OWL, RDF, SPARQL), or ontology alignment.
• A background in code analysis, developer tools, or software engineering research.
• Experience with knowledge graph embedding methods or graph neural networks.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• Actively contributed to relevant open-source projects.

What we offer:

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.
• Full support for publishing ML research and attending industry conferences.
• Collaboration with JetBrains Research and external academic advisors in the knowledge graph and ontology learning community.
• A motivated team that moves fast, with short feedback loops, rapid prototyping, and lots of ownership.
• A rare applied research role where your methods ship in a real product used by software teams.

Why join JetBrains? 

• Strong base salary. We offer competitive pay that reflects your skills and experience.
• Flexible work location. Enjoy the freedom to work from home or from the office.
• Remote work. Spend up to 30 days per year working remotely from abroad.
• Extra time off. More days to relax, recharge, and do the things you love.
• Medical insurance allowance. Enjoy peace of mind for you and your family
• Learning and development opportunities. Access to conferences, courses, and language classes.
• Language classes. Pick up the local language or sharpen your English skills.
• Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays.
• Mental health support. To help you feel your best, we provide easy access to professional mental health services.
• Sports benefit. Enjoy an on-site gym or sports club stipend.
• Internal events. Join company-wide celebrations and team gatherings.

*Some benefits may vary depending on location.

#LI-DNI

Role Description

• As a Senior Applied Value Engineering Consultant - Public Sector you are pushing the envelope in solving business-critical problems. You will be working with our most strategic customers understanding their objectives and key challenges, and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks. With Celonis’ Process Intelligence (PI) platform we feed operational context to AI so it understands our customers’ business and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers. This position will focus on our customers across the Public Sector, you’ll spearhead our mission to make processes work for citizens, government agencies and the planet, creating efficient, resilient processes which will shape a modern, citizen-oriented state. 

Key Responsibilities:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Value Engineers stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Value Engineers specialize in domains (e.g. supply chain) and industries

Requirements:

• 5+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.
• Excellent communication and presentation skills in English and German. The role requires a high level of fluency in German.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)
• Security Certification (SIcherheitsüberprüfung) up to SÜ2

The Role

The Lead Researcher in Quantum Machine Learning will be a member of Terra Quantum's AI Applied Research team. This team is at the forefront of conducting both fundamental and applied research in the field of quantum machine learning. Although the Lead Researcher will focus on innovating in quantum machine learning, the role involves translating research findings into practical algorithmic software solutions. The Lead Researcher is expected to closely monitor and analyse scientific and industrial trends and needs across short-term, mid-term, and long-term horizons, specifically within the quantum technology and machine learning sectors, to guide the development and implementation of hybrid quantum-classical algorithms.

Reporting directly to the Director of AI, the Lead Researcher in Quantum Machine Learning plays a pivotal role in driving excellence within their team. They are not only detail-oriented but also possess a remarkable capacity for enthusiasm. By demonstrating commitment and passion for the mission, they inspire their team members to contribute to making quantum technologies widely accessible and to effect positive change globally.

The Responsibilities

The Lead Applied Researcher should expect to work in the following Quantum Machine Learning Team activities.

• Fundamental research in quantum machine learning
  • Developing quantum machine learning algorithms for different data processing tasks, e.g. time series, routing and planning, image, graph data, or natural language processing
  • Keeping up with state-of-the-art approaches in quantum machine learning
  • Analysing parametrised quantum circuits in their capacity to learn via a hybrid quantum-classical optimization loop
  • Utilizing metrics of, e.g., the Fisher information matrix, the effective dimension, and Fourier terms accessibility for parametrized quantum circuit ansatz analysis
  • Improving the trainability of parametrised quantum circuits through layerwise batch-entropy regularization and similar techniques
  • Interpreting and explaining quantum machine learning models. Analysing the flow of information through different architectures of quantum neural networks
  • Researching and understanding where the quantum brings the benefit to machine learning
  • Developing data encoding and data processing techniques for different types of quantum computers
  • Assisting in writing patent draft applications
  • Writing research papers for scientific journals

• Efficient implementation of quantum machine learning algorithms
  • Executing quantum machine learning algorithms on QPUs, e.g. of QuEra, IonQ, Rigetti, and IBM Q
  • Adjusting and improving implementation of hybrid quantum neural networks for different QPUs
  • Exploring and testing the best ways to hybridize classical machine learning solutions with quantum machine learning
  • Understanding the way to implement an efficient interaction of hardware (CPU, GPU) and software (PyTorch, Pennylane)
  • Assisting in the development of our quantum machine learning SDK
  • Optimising, at least theoretically, the code of hybrid quantum-classical machine learning solutions for faster execution

• Supporting industry projects implementation
  • Bringing in novel ideas based on industry needs in time-series, routing and planning, GenAI, and natural language processing tasks
  • Brainstorming on possibilities of quantum machine learning algorithms applications to our client’s industry problems
  • Working on finding a theoretical or empirical advantage of using hybrid quantum-classical machine learning in industrial problems
  • Assisting in writing applied industry research papers for scientific journals

The Requirements

The Lead Applied Researcher is expected to have several qualifications depending on the area of activity in quantum machine learning.

• PhD in computer science, physics, mathematics, electrical engineering, or equivalent subject is required
• Publication record in quantum machine learning, or variational quantum algorithms is required
• Advanced expertise in one or more quantum programming languages (Qiskit, Q#, Pennylane, Cirq, Quipper, Scaffold, tket) is required
• Advanced experience working with variational quantum algorithms for machine learning is required
• Experience working with the classical machine learning algorithms is required
• Computational learning theory expertise is optional
• Expert knowledge in Python is optional
• Expert knowledge in Pytorch and TensorFlow is optional
• Experience in programming for GPUs is optional
• Experience in working with QPUs is optional
• Goal-oriented, analytical and the ability to work independently
• Flexible, proactive, and creative with the ability to work in the team
• Highly motivated and resilient with the ability to work interdisciplinary
• Proficiency in written and spoken English
• Applicants must have the legal right to live and work in the European Union or Switzerland. Unfortunately, we are unable to offer visa sponsorship for this role.

The Role

The Researcher in Quantum Machine Learning or Quantum Information will be a member of Terra Quantum's AI Applied Research team. This team is at the forefront of conducting both fundamental and applied research in the field of quantum machine learning. Although the Researcher will focus on innovating in quantum machine learning, the role involves translating research findings into practical algorithmic software solutions. The Researcher is expected to closely monitor and analyse scientific and industrial trends and needs across short-term, mid-term, and long-term horizons, specifically within the quantum technology and machine learning sectors, to guide the development and implementation of hybrid quantum-classical algorithms.

The Researcher in Quantum Machine Learning plays a role in driving excellence within their team. They are not only detail-oriented but also possess a remarkable capacity for enthusiasm. By demonstrating commitment and passion for the mission, they inspire their team members to contribute to making quantum technologies widely accessible and to effect positive change globally.

The Responsibilities

The Applied Researcher should expect to work in one and supporting in the other areas of the following Quantum Machine Learning Team activities.

• Fundamental research in quantum machine learning
  • Developing quantum machine learning algorithms for different data processing tasks, e.g. time series, routing and planning, image, graph data, or natural language processing
  • Exploring state-of-the-art approaches in quantum machine learning
  • Analysing parametrised quantum circuits in their capacity to learn via a hybrid quantum-classical optimization loop
  • Utilizing metrics of, e.g., the Fisher information matrix, the effective dimension, and Fourier terms accessibility for parametrized quantum circuit ansatz analysis
  • Improving the trainability of parametrised quantum circuits through layerwise batch-entropy regularization and similar techniques
  • Interpreting and explaining quantum machine learning models. Analysing the flow of information through different architectures of quantum neural networks
  • Researching and understanding where the quantum brings the benefit to machine learning
  • Developing data encoding and data processing techniques for different types of quantum computers
  • Writing research papers for scientific journals

• Efficient implementation of quantum machine learning algorithms
  • Executing quantum machine learning algorithms on QPUs, e.g. of QuEra, IonQ, Rigetti, and IBM Q
  • Adjusting and improving implementation of hybrid quantum neural networks for different QPUs
  • Exploring and testing the best ways to hybridize classical machine learning solutions with quantum machine learning
  • Understanding the way to implement an efficient interaction of hardware (CPU, GPU) and software (PyTorch, Pennylane)
  • Assisting in the development of our quantum machine learning SDK
  • Optimising, at least theoretically, the code of hybrid quantum-classical machine learning solutions for faster execution

• Supporting industry projects implementation
  • Bringing in novel ideas based on industry needs in time-series, routing and planning, GenAI, and natural language processing tasks
  • Brainstorming on possibilities of quantum machine learning algorithms applications to our client’s industry problems
  • Working on finding a theoretical or empirical advantage of using hybrid quantum-classical machine learning in industrial problems
  • Assisting in writing applied industry research papers for scientific journals

The Requirements

The Applied Researcher is expected to have several qualifications depending on the area of activity in quantum machine learning or quantum information.

• Currently pursuing or recently completed a degree in computer science, physics, mathematics,
  or equivalent subject
• Experience in one or more quantum programming languages (Qiskit, Pennylane, Cirq, etc.)
• Experience working with variational quantum algorithms for machine learning
• Experience working with classical machine learning algorithms
• Basic knowledge in Python, Pytorch and TensorFlow
• Experience in working with QPUs is optional
• Familiarity with quantum information theory is optional
• Familiarity with Tensor-Network techniques is optional
• Goal-oriented, analytical and the ability to work independently
• Flexible, proactive, and creative with the ability to work in the team
• Highly motivated and resilient with the ability to work interdisciplinary
• Proficiency in written and spoken English
• Applicants must have the legal right to live and work in the European Union or Switzerland. Unfortunately, we are unable to offer visa sponsorship for this role.

At JetBrains, code is our passion. Ever since we started back in 2000, we have been striving to make the world’s most robust and effective developer tools. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

We are working on an ambitious new platform that provides AI capabilities to all JetBrains products. Our platform is based on models developed in-house for writing and coding assistance, as well as integration with our strategic partners.

We are looking for a Research Engineer who can contribute to training foundation models for coding tasks. You’ll be working on developing Large Language Models from scratch and deploying them into production environments where they will be accessible by end users across the globe.

We value engineers who: 

• Can plan projects and make decisions independently, consulting with others if needed.
• Identify customer needs and prioritize their tasks accordingly.
• Start with the simplest solutions and gradually add complexity as needed.
• Take sole responsibility for an entire subsystem.
• Have a passion for learning and a desire to stay up to date with the latest developments in the LLM field.

In this role, you will: 

• Work with stakeholders to convert business requirements into technical specifications.
• Train LLMs from scratch on a large GPU cluster.
• Collect and process pre-training and fine-tuning datasets.
• Support and improve existing subsystems.

We’ll be happy to have you on our team if you have: 

• Experience in design, deployment, and support of production ML systems.
• A strong theoretical background in NLP and transformer-based approaches.
• Proficiency with modern deep learning frameworks such as PyTorch and common libraries for NLP.
• Experience in distributed training of multi-billion parameter models.
• Attention to detail in everything you do and great communication skills.

We’d be especially thrilled if you have experience with: 

• LLM inference frameworks such as vLLM, DeepSpeed, TensorRT.
• LLM alignment techniques such as RLHF/RLAIF.
• MLOps tools and practices, including CI/CD for ML.
• K8s and Kubeflow.
• Scientific publications in the NLP field.

How we develop JetBrains AI: 

• A cluster of hundreds of NVIDIA GPUs as training infrastructure.
• Git for source control management.
• Python, PyTorch, and HuggingFace as an ML stack.
• Kubeflow and Weights & Biases for experiment tracking.
• TeamCity as a CI Automation system.

#LI-KP1

At JetBrains, code is our passion. Ever since we started, back in 2000, we’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’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’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.

You’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:

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
• Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
• Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
• 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.
• Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.
• Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.
• Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features. 

We’ll be happy to bring you on board if you have:

• Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
• Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
• Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.
• 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.
• A product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.
• At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.
• 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.
• Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.
• AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.
• Inference optimization and serving optimized models in production.

#LI-KP1

At JetBrains, code is our passion. Ever since we started, back in 2000, we've been striving to make the strongest, most effective developer tools on earth. Today, AI-powered coding agents are becoming a core part of how developers write Kotlin – and we want to make sure they write it well.

The Kotlin AI Value Stream team is responsible for how AI agents understand, generate, and improve Kotlin code across all platforms: Android, Kotlin Multiplatform, server-side, web, desktop, and others. We build the evaluation infrastructure, error analysis tools, and post-training pipelines that measure and improve agent behavior on real Kotlin developer tasks.

As a Research Engineer on this team, you'll own the end-to-end loop: Analyze how agents fail on Kotlin → build evals that capture those failures → research and implement methods to fix them → measure the improvement. Your work will directly shape how millions of developers experience Kotlin through AI coding agents.

As part of our team, you will:

Build tools for agentic error analysis

• Design and implement tooling to systematically capture, classify, and analyse errors that AI coding agents make when generating Kotlin code.
• Build observability pipelines over agentic traces – mining patterns from agent sessions in JetBrains IDEs, Junie, Claude Code, Cursor, and other coding agents.

Build evaluation pipelines

• Design, implement, and maintain evaluation pipelines that measure Kotlin code generation quality across dimensions, including correctness, idiomaticity, build success, framework usage, and test coverage.
• Build simulation environments where coding agents can be measured on realistic Kotlin developer tasks – from greenfield KMP projects and Gradle dependency management to migrating Spring applications from Java to Kotlin.
• Own evaluation infrastructure: metrics, experiment tracking, automated regression checks, and reproducible benchmarking.

Research methods for improving agent and model behavior on Kotlin

• Experiment with post-training techniques (SFT, DPO, GRPO) to improve how models handle Kotlin-specific patterns, idioms, and frameworks.
• Investigate context engineering approaches: CLAUDE.md/AGENTS.md files, compiler-as-verifier feedback loops, Kotlin LSP integration, and MCP-based tooling.
• Run experiments to measure impact: A/B comparisons, benchmark suites, and before/after analyses on real codebases.
• Collaborate with model providers (Anthropic, OpenAI, and Google) to translate Kotlin-specific findings into model improvements.

Build public Kotlin benchmarks

• Design and build open-source benchmarks that measure AI coding agent performance on Kotlin tasks and eventually become the standard reference for the ecosystem.
• Create task datasets covering the breadth of Kotlin usage: the server side (Spring, Ktor), multiplatform projects (KMP), build systems (Gradle), Android, library development, and others.
• Include both mined real-world tasks and carefully designed synthetic tasks that test specific Kotlin capabilities.
• Maintain and evolve benchmarks as models improve, ensuring they remain challenging, relevant, and contamination-resistant.

We'll be happy to have you on board if you have:

• Hands-on experience building evaluation or analysis pipelines for LLMs or AI coding agents in a research or production setting.
• Strong Python engineering skills (at least three years), with the ability to write clean, maintainable code in data-heavy and ML-adjacent codebases.
• Experience with data analysis at scale: querying large datasets (SQL/Athena), building data pipelines, and performing statistical analysis of experimental results.
• The ability to own projects end to end – from identifying a problem in agent traces to designing an eval, running experiments, and shipping a fix.
• A product-aware mindset: You care about how agents are actually used by developers and can translate real failure modes into evaluation and training work.
• Familiarity with Kotlin or a strong willingness to develop deep Kotlin expertise (you'll be living in Kotlin codebases daily).

Our ideal candidate would also have experience with:

• Post-training LLMs: SFT, RLHF, DPO, GRPO – either hands-on training or designing the data and reward pipelines that feed into training.
• Modern deep learning frameworks (PyTorch) and LLM training stacks (TRL, verl, Megatron, or similar).
• AI agent development: tool-using agents, multi-step coding workflows, agentic frameworks.
• Evaluation frameworks and tools: Inspect AI, Promptfoo, LM-evaluation-harness, or custom eval pipelines.
• Experiment tracking and observability: Weights & Biases, MLflow, Langfuse, or similar.
• The Kotlin ecosystem: Android, Gradle, KMP, Spring, Ktor – with an understanding of the developer workflows that agents need to support.
• Contributing to or maintaining open-source projects, especially benchmarks or evaluation tools.

Don't check every box? That's okay – if you're excited about this work and bring strong fundamentals, we'd love to hear from you. We're happy to talk and provide the training you need to grow into the role.

Why join JetBrains? 

• Strong base salary. We offer competitive pay that reflects your skills and experience.
• Flexible work location. Enjoy the freedom to work from home or from the office.
• Remote work. Spend up to 30 days per year working remotely from abroad.
• Extra time off. More days to relax, recharge, and do the things you love.
• Medical insurance allowance. Enjoy peace of mind for you and your family
• Learning and development opportunities. Access to conferences, courses, and language classes.
• Relocation support. We help make your move as smooth and stress-free as possible. 
• Language classes. Pick up the local language or sharpen your English skills.
• Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays.
• Mental health support. To help you feel your best, we provide easy access to professional mental health services.
• Sports benefit. Enjoy an on-site gym or sports club stipend.
• Internal events. Join company-wide celebrations and team gatherings.

*Some benefits may vary depending on location.

#LI-DNI