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

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