The Role

We are opening a 3–6 month Early Career program within our AI R&D team. You will work alongside experienced researchers to solve challenges in LLMs, reinforcement learning, and agentic systems.

We are looking for Early talents to support research in:

• Reinforcement learning for agents and reasoning models
• Building reinforcement learning environments
• Efficient model distillation and evaluation

Your responsibilities will include:

• Assist in designing and running experiments to train LLMs and speculators for them
• Help analyze experimental results and document findings
• Write and maintain code for data mining, processing, and model evaluation
• Collaborate with the team to explore new methods in the selected research direction
• Participate in technical discussions and discuss future steps of research
• Advanced methods for efficient speculative decoding
• Inference optimization techniques

We expect you to have:

• Strong knowledge of computer science and machine learning fundamentals
• Experience with Python and familiarity with deep learning frameworks
• Ability to write clean, efficient code
• Strong problem-solving skills and a willingness to learn quickly
• Good communication skills in English
• Permitted to work in the job’s location

Nice to have:

• Previous experience with NLP/reinforcement learning/AI agents projects
• Familiarity with current LLM concepts
• Enrollment in or completion of a degree in Computer Science, AI, or a related field
• Contributions to open-source projects

What we offer

• Competitive salary and comprehensive benefits package
• Mentorship from experienced AI, ML, and cloud infrastructure professionals
• Hands-on experience with real customer workloads and production systems
• Remote work flexibility and a collaborative, supportive team environment
• Opportunity to be considered for a full-time role after the Early Talent Program

We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us!

An overview of this role

GitLab is looking for a Product Design Manager to join the Plan & Analytics stage. As a design leader, you own the delivery of UX commitments, are always looking to improve productivity, and are responsible for the user experience in the product. While you are credible as a designer and know the details of what product designers work on, your time is spent hiring a world-class team and putting them in the best position to succeed.

This is an incredibly exciting time to join the Plan & Analytics stages - Working in GitLab's Plan space sits at the most human and strategic layer of the software development lifecycle — where ideas, priorities, and intentions are formed — making it the perfect frontier to explore how AI can augment not just execution, but thinking itself. As AI accelerates the speed at which code is written and shipped, the Plan space becomes even more critical, because the bottleneck is rapidly shifting from "how fast can we build?" to "how clearly can we define what to build and why?" This creates a rare opportunity to reimagine how teams collaborate, how requirements evolve, and how AI can transform a traditionally manual, document-heavy process into a dynamic, intelligent system that connects strategy directly to delivery.

You'll be working closely with the Product Management and Engineering teams to execute on the product roadmap and priorities.

Some examples of our projects: 

• Work Items
• Saved Views
• Software Factory

What you’ll do  

• Proactively identify large, strategic UX opportunities that span both inside your areas and across the product. Work with other product design managers, staff, and principal product designers to drive cross-product initiatives through awareness and prioritization.
• Identify strategic user research initiatives that span multiple stage groups (and possibly the entire product), and work with other product design/research managers to organize research efforts.
• Be a frequent and active participant in milestones by reviewing UX deliverables (such as research and designs) that your team creates, and provide feedback to ensure high-quality output.
• Communicate the value of UX to cross-functional GitLab team members and work with them to leverage the product development flow. 
• Help the product teams of areas you support to prioritize UX initiatives including Deferred UX, triage and resolution of UX bugs, and overall usability improvements to the product. Lead UX Scorecard initiatives in order to consistently measure the experience of your product areas and show improvements over time.
• Contribute to product direction conversations, with emphasis on leading the experience vision for your product areas.
• Hire, grow and retain a world-class team of Designers.

What you’ll bring 

• Experience managing a group of designers remotely, who work on complex products.
• Deep experience building AI experience for the software development lifecycle.
• Deep experience designing for large-scale enterprise platforms.
• Experience defining/executing on the high-level strategy (the why) and creating design deliverables (the how) based on research.
• Experience driving organizational change with cross-functional stakeholders, including Product Management and Engineering.
• Solid visual awareness with understanding of basic design principles and accessibility.
• You share our values, and work in accordance with those values.
• You possess leadership qualities needed to thrive at Gitlab.
• Ability to use GitLab.

About the team

You will work closely with three designers. You will collaborate with the Group Product Manager, Engineering Director and UX Researcher to improve team communication and maintain excitement about the opportunities ahead. You can learn more about what our teams are working on by visiting our direction page:

• Plan

About the role

We're looking for a Senior UX Researcher who brings rigour and curiosity to both product and marketing research. Someone who's as comfortable running foundational product studies as they are digging into brand perception, buyer mindsets, or how a marketing site actually works for the people using it.

This is strategic, high-visibility work — you'll partner with Product Design, Product Management, Brand, and Marketing to shape what we build and how we tell the world about it.

What makes this role unique:

• Work across both brand and product research, with real influence on each
• Research enterprise developer tools and DevOps workflows — including AI-powered features built for developers
• Fast-paced environment where research directly influences design and business decisions
• Be part of a team of talented, experienced researchers who genuinely invest in each other's growth

What you'll do

• Lead foundational, problem validation, and strategic research projects across your assigned product areas and beyond
• Drive both brand and product research — including buyer persona development, brand perception studies, and digital experience research — that directly informs positioning and messaging
• Be the expert on the users within your area of coverage: their behaviours, pain points, workflows, and goals
• Make connections across research initiatives, surfacing macro insights that no single study could reveal on its own
• Build trusted relationships with Product Design, Product Management, Brand, and Marketing counterparts, guiding them to the right research approach
• Advocate for and socialise actionable insights in a clear and compelling way that increases empathy across the organisation
• Independently drive research prioritisation across stage groups, balancing impact, product direction, and company objectives
• Mentor other researchers and actively shape how the Experience Research team works, through process iteration, documentation, and peer reviews

What you'll bring

• Empathetic, curious and open-minded
• Deep mixed-methods research experience across qualitative and quantitative methods
• A track record in both product research and marketing or brand research
• The ability to guide stakeholders — including senior ones — toward the right approach, even when they arrive with a firm opinion
• Strong instincts for prioritisation across multiple stakeholder groups in a fast-moving environment
• Clear, compelling communication that makes findings land with product, brand, and marketing audiences
• Experience working in a highly iterative, fast-paced environment, with the ability to pivot with sudden changes in direction
• Demonstrated ability to work effectively in a fully remote, globally distributed team — with strong async communication habits
• Self-motivated and self-managing, with strong organisational skills
• Ability to ramp up to a highly technical domain quickly
• Share our values, and work in accordance with those values
• Able to conduct research in a fully remote setting and use GitLab as part of your daily workflow
• Experience working on AI products, particularly for enterprise dev tooling, is a plus
• Experience working on enterprise and/or developer tooling products is a plus

About the position

You are expected to conduct and lead internationally competitive research in AI-driven semantic analysis and automated reasoning-flow modeling, with applications to educational materials, scientific documents, and research authoring. Your work will focus on developing advanced methods for semantic structure extraction, conceptual and argumentative flow reconstruction, rationale-aware content generation, metacognitive prompting, and adaptive personalization. Core research contributions will include designing algorithms for concept and structure extraction, building neural/graph hybrid models for pedagogical reasoning, implementing ontology-alignment methods for cross-domain transfer, and creating human-in-the-loop optimization pipelines that incorporate expert feedback—aligning with the multi-year research roadmap (WP1–WP3) .

As a postdoctoral researcher, you will take a leading role in designing experiments, shaping research directions, supervising junior researchers (including PhD students), and contributing to the development of end-to-end demonstrator systems capable of generating structured, rationale-rich, and learner-adaptive content. You will be responsible for coordinating data annotation efforts, overseeing the construction of evaluation frameworks, and driving publication-quality research outputs, including survey papers, methodology papers, and cross-domain transfer studies. Interdisciplinary collaboration is strongly encouraged, especially at the intersection of computer science, computational linguistics, cognitive science, and learning technologies, and you will have opportunities to initiate or co-lead joint projects with internal and external partners.

The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course development and supervising Bachelor’s, Master’s, and PhD theses. The university provides a supportive academic environment—including mentoring, administrative support, computational resources, and opportunities for technology transfer and industry engagement—enabling the postdoctoral researcher to establish or strengthen an internationally visible research profile.

Mandatory requirements

• PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a closely related field.
• Strong publication record in relevant top-tier venues (e.g., NeurIPS, ACL, ICML, ICLR, AIED, LAK, AAAI).
• Demonstrated expertise in at least two of the following areas:
  • semantic parsing or structured NLP
  • knowledge representation, reasoning graphs, or GNNs
  • natural language generation, explainability, or rationale modeling
  • ontology alignment, cross-domain transfer, or representation learning
  • personalization algorithms, metacognition, or educational AI
  • human-in-the-loop systems or RLHF
• Experience in leading research tasks, mentoring junior researchers, and coordinating multi-stage projects.
• Fluency in Python and modern deep learning frameworks (PyTorch/TensorFlow).
• Strong analytical, communication, and academic writing skills.
• Demonstrable ability to design rigorous experimental pipelines and validation methodologies.
• Responsible, independent, and proactive research personality with strong teamwork skills.
• Intercultural experience and readiness to work in an international research environment.
• Fluency with AI co-pilot assistants for coding and writing.
• Fluency in English, the primary language of research and instruction on campus.

Preferred qualifications:

• Experience in developing research prototypes, evaluation benchmarks, or large-scale datasets.
• Prior involvement in interdisciplinary or applied research projects.
• Potential to contribute to competitive funding applications.
• Experience with open-source releases or reproducible research pipelines.

Application Details

• Starting date: March, 2026
• Contract: Full-time (100%), TV-L E13
• Work mode: Hybrid

Application package must include: 

• Curriculum Vitae (CV);
• A detailed letter of motivation outlining research interests and career goals;
• 2 recommendation letters;

Applications to be reviewed on a rolling basis. Shortlisted candidates will be invited to interviews.

Trexquant develops and operates systematic trading strategies across a range of global liquid markets. Behind that is an enormous, deeply technical system and an exceptional team of researchers and engineers. We are collaborative, fast-moving, early adopters of new technology, and genuinely proud of what we build. Our headquarters is in Stamford, CT, and we plan to expand into Europe.

About the Position

Germany has world-class quantitative and engineering talent, and we want to be where that talent is. We are planning on opening an office in the Munich area and looking for someone to lead the buildout on the ground — someone who has done enough in their career to know how organizations work, how to get things moving in Germany specifically, and how to represent a high-caliber firm to skeptical, in-demand candidates. At the initial stage, this role will be engaged through our local Employer of Record partner. 

What You’ll Do

• Own hiring strategy and implementation for quant developers and researchers in the Munich area, working alongside the recruiting team in Stamford

• Coordinate the physical and operational setup of the office with US-based leadership and functional teams

• Represent Trexquant at hiring events, in meetings, and in direct conversations with candidates and partners in the German talent market

• Keep global teams informed with clear reporting on progress, hiring pipeline, and milestones

• Coordinate with company legal and compliance staff to follow local and global requirements for positive development and growth

Qualifications

• 5+ years of professional experience in operations, strategy, consulting, recruiting, business development, or a comparable role where you had real responsibility and delivered results

• Understanding of the German business environment — hiring practices, employment law basics, cultural expectations — either from working in Germany or operating across European markets

• Comfortable being the most senior person in the room locally while taking strategic direction from headquarters

• Presence and credibility to attract strong technical candidates to a firm they may not have heard of yet

• Genuine interest in quantitative finance and technical organizations, even if your background is not in trading

• Fluent in German and English

Experience

• Experience in financial services

• Prior exposure to office launches or market entry

• An existing network in Munich's tech or quant community

Compensation and Perks (Value Adds)

• The opportunity to build something from scratch — you will shape Trexquant's European presence from day one

• Direct collaboration with senior leadership at a successful, established quantitative fund

• Competitive compensation package including base salary, performance-based bonus, and relocation support where applicable

• Budget and autonomy to set up the Munich office the right way

• A team in the US that genuinely backs this initiative and will resource it properly

Location

Greater Munich Area. This is an on-the-ground role.

#li-jm1 #li-onsite

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

Constructor University in collaboration with Constructor Knowledge Labs and Constructor Technology

About the Position

The research group led by Prof. Dr. Andrey Ustyuzhanin at Constructor University, in collaboration with Constructor Knowledge Labs (CKL) and Constructor Technology (industry partner), invites applications for Ph.D. student positions in the field of Computer Science, with a focus on Artificial Intelligence (AI) and Machine Learning (ML).

This PhD position is part of an initiative to advance knowledge representation and adaptive reasoning systems. The research will focus on developing flexible frameworks for actionable knowledge representation that support storage, retrieval, and dynamic adaptation of information across diverse tasks.

Key objectives include:

• Transforming unstructured data into interactive knowledge graphs and personalized cognitive maps.
• Designing models that provide interpretable, persistent, and navigable structures of knowledge.
• Addressing challenges such as hierarchy, composability, and coarse-graining for robust, task-specific reasoning.
• Exploring individual and community-level knowledge modeling, including personalized domain maps, profile extraction from artifacts (e.g., papers, courses), and cross-domain abstraction.

The overarching goal is to create systems that enable transparent, adaptive, and spatially intuitive representations of knowledge, supporting both individual users and collaborative communities.

Applicant Profile

Mandatory requirements:

• Holding recognized MSc degree (or equivalent) in Computer Science, AI, ML, or a related discipline.
• Students holding BSc degree and exhibiting outstanding performance and extraordinary potential can apply for fast-track PhD.
• Strong mathematical background supported with experience in defining and developing knowledge-graph or information retrieval systems.
• Hands-on experience with large language models (LLMs) and their applications.
• A track record of publications in AI/ML or related areas.
• Documented experience in practical research work.
• Strong skills in academic English writing (peer-reviewed papers, reports, or equivalent).

Funding & Appointment Terms

The appointment provides full financial coverage through a dedicated fellowship, comprising:

• Monthly stipend of €1,650
• Monthly research-cost allowance of €100 (Forschungskostenpauschale)
• Health-insurance subsidy of €100 per month
• Supplementary €550 mini-job allowance to support parallel part-time employment (optional)

Application Details

• Expected start date: March, 2026

Application package must include: 

• Curriculum Vitae (CV);
• Academic transcripts ;
• A detailed letter of motivation outlining research interests and career goals;
• 2 recommendation letters;

Applications to be reviewed on a rolling basis. Shortlisted candidates will be invited to interviews.

About the position

You are expected to contribute to the development of internationally visible, foundational research in AI-driven semantic structure extraction, automated reasoning-flow modeling, and adaptive content generation. The research focuses on methods for analyzing and representing deep semantic and pedagogical structures in scientific and educational materials; high-fidelity extraction of conceptual and reasoning blocks; inference-time rationale generation; and adaptive, learner-aware sequencing of content. This includes work on semantic parsing, structured NLP, graph-based neural models, metacognitive prompting, ontology alignment across disciplines, and human-in-the-loop optimization.

In this context, interdisciplinary research is strongly encouraged—particularly collaborations spanning computer science, computational linguistics, cognitive science, and the learning sciences. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve system interpretability and instructional quality. The successful candidate will also contribute to high-quality publications, release research prototypes, and support demonstrator systems that deliver structured semantic extraction, rationale-aware content generation, and cross-domain transfer of reasoning structures.

Additionally, the successful candidate is expected to support teaching activities in areas such as Machine Learning, Natural Language Processing, AI in Education, Knowledge Representation, and Python-based analytical seminars at the BSc, MSc, and PhD levels. Responsibilities include assisting in course delivery, advising students, supervising Bachelor/Master theses, and engaging in methodological innovation for online, hybrid, and in-person learning environments. The university provides strong support for early-career researchers, including mentorship, administrative assistance, access to computational resources, conference funding, and opportunities to collaborate with other research groups and industrial partners working at the intersection of AI and digital education.

Mandatory requirements

• Master’s or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a related field.
• Strong research interest and practical experience in one or more of the following areas:
  • semantic parsing and structured representation learning
  • knowledge graphs or graph-based reasoning
  • transformer models, sequence modeling, or GNNs
  • natural language generation and explainability
  • educational AI, personalization algorithms, or cognitive modeling
• Evidence of research potential through publications, a strong thesis, or significant projects.
• Experience or interest in innovative teaching and learning approaches.
• Ability to translate theoretical insights into engineered prototypes and systems supporting scientific or educational use cases.
• Responsible, self-motivated, and capable of working independently as well as collaboratively.
• Excellent verbal and written communication skills.
• Intercultural competence and experience in international environments.
• Fluency with co-pilot tools for coding and writing.
• Fluency in English, the language of instruction and communication on campus.

Funding & Appointment Terms

The appointment provides full financial coverage through a dedicated fellowship, comprising:

• Monthly stipend of €1,650
• Monthly research-cost allowance of €100 (Forschungskostenpauschale)
• Health-insurance subsidy of €100 per month
• Supplementary €550 mini-job allowance to support parallel part-time employment (optional)

Application Details

• Expected start date: March, 2026

Application package must include: 

• Curriculum Vitae (CV);
• Academic transcripts ;
• A detailed letter of motivation outlining research interests and career goals;
• 2 recommendation letters;

Applications to be reviewed on a rolling basis. Shortlisted candidates will be invited to interviews.

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.

We're building an AI copilot for web testing automation. It enables manual and automation QA engineers to turn their manual testing flows into production-ready automated E2E testing scripts, saving hours of manual work and coding. We are a small team with zero bureaucracy, focusing on real users. We optimize for shipping value - not hype.

About the role:

You'll own the top of the funnel: outbound, qualification, and getting leads to sit down for a conversation. Demos, onboarding, and retention are staying with the founder for now.

Phase one: Get individual Automation QAs to try the product, aiming for fast adoption and no procurement. Once we hit 20+ weekly users, we’ll move on to phase two, where we’ll shift the focus to B2B – qualified meetings with QA Leads and Engineering Managers.

Your focus in the first three months:

• Build targeted lists: Automation QAs at companies with web products and Playwright, Cypress, or Selenium in their stack.
• Run outbound sequences (LinkedIn is the baseline, but you decide the channels). A/B test weekly.
• Design qualification gates (forms and scoring) to filter leads before the founder calls.
• Reach 20+ weekly active users after month one – ICP-qualified and giving direct feedback to engineering.
• Document what works into a repeatable playbook.

Growth path:

This role expands as your strategies deliver results. Once the user pipeline works, you can take over demos and run B2B sales end to end. From there, the natural next step is Growth Lead (funnel optimization and scaling the playbook) or Account Executive (owning deal execution).

Why to join:

• You'll be building a strategy that scales the product, not filling a quota. What you create from scratch becomes the foundation.
• Your outreach turns into users who talk directly to engineering, and their feedback ships as features within days.
• AI for developer tools is a market with real pull, and QA automation is one of its sharpest pain points.
• We’re backed by JetBrains – financially secure with no reliance on external VC funding.

Who we're looking for:

You should have a high level of agency and be quant-minded – you live in funnel metrics, run experiments, and are comfortable saying "no" to leads that are a bad fit. 

The ideal candidate will:

• Have done 0-to-1 outbound for an early-stage devtool, QA tool, infrastructure product, or similar.
• Have proven cold outbound results, instead of being dependent on warm inbound.
• Be able to talk credibly about flaky tests, CI pain, and Playwright vs. Cypress. You don't need to be an engineer, but you do need domain empathy.
• Possess strong written communication skills, with the ability to craft short, sharp messages that get replies.

Nice to have:

• A QA or SDET background – you've felt the pain yourself before moving to sales or BD.
• Exposure to security/compliance objections – enough to pre-empt and route.
• Content skills – your LinkedIn posts get engagement, not just your DMs.
• Experience as one of the first 10 employees at a startup.

#LI-REMOTE 
#LI-KT1