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About This Role
Location
United States; Canada; Germany; The Netherlands; United Kingdom
Employment Type
Full time
Location Type
Remote
Department
Engineering
Compensation
- US Premium Locations (SF, NYC, Seattle)$190K – $235K • Offers Equity
- All Other US Locations$175K – $220K • Offers Equity
For roles based in the United States, the typical starting salary range for this position is listed on the job posting. In certain locations, such as the San Francisco Bay Area and the New York City Metro Area, a premium market range may apply, as listed.
These salary ranges reflect what we reasonably and in good faith believe to be the minimum and maximum pay for this role at the time of posting. The actual compensation may be higher or lower than the amounts listed, and the ranges may be subject to future adjustments.
An individual’s placement within the range will depend on various factors, including (but not limited to) education, qualifications, certifications, experience, skills, location, performance, and the needs of the business or organization.
If you have any questions or comments about compensation as a candidate, please get in touch with us at [email protected].
ClickHouse Labs is an applied research group that works closely with engineering teams to identify challenging problems in database systems and develop novel solutions. Our goal is to advance the state of the art and translate research ideas into real-world system improvements.
About the Role
As a Research Scientist at ClickHouse Labs, you will work on high-impact problems at the intersection of database systems, distributed systems, AI, and systems software engineering while collaborating with experienced researchers and engineers. The role covers a broad range of problems across database systems research and engineering, including but not limited to query optimization, execution engines, distributed systems, consensus protocols, materialized views, indexing and data structures, benchmarking, workload analysis, and emerging agentic workloads.
In this role, you will work on leading open-source database systems (e.g., ClickHouse, PostgreSQL). You will investigate performance and scalability bottlenecks in production-quality systems, develop new algorithms and system designs, and evaluate them under realistic workloads and hardware configurations. Projects may involve improving query planning and execution, reducing CPU and memory overhead, increasing throughput and concurrency, scaling across distributed deployments, or redesigning system components to leverage modern hardware better. You will work closely with engineers to turn successful ideas into robust implementations that can be deployed and maintained in widely used open-source systems.
Researchers are encouraged to publish their work, contribute to open-source projects, participate in leading research conferences, and pursue ambitious projects with strong engineering and research support.
Responsibilities
- Conduct applied research aimed at advancing the performance, scalability, reliability, and functionality of open-source database management systems, including ClickHouse and PostgreSQL.
- Design and evaluate new algorithms, data structures, and system architectures for areas such as query optimization, indexing, storage management, query execution, and distributed data management.
- Develop high-quality research prototypes and production-grade implementations, and work with engineering teams to integrate successful techniques into open-source and production systems.
- Collaborate closely with database engine, infrastructure, and data-plane engineers to identify high-impact research problems and translate research results into practical system improvements.
- Analyze large-scale database workloads, performance traces, benchmarks, and production telemetry to identify bottlenecks and optimization opportunities.
- Design rigorous experiments and benchmarks to evaluate new techniques across realistic workloads, hardware configurations, and deployment environments.
- Communicate research results through technical reports, open-source contributions, internal presentations, and publications at leading database and systems venues (e.g., SIGMOD, VLDB, CIDR).
- Contribute to the broader technical direction of ClickHouse Labs by identifying emerging research opportunities and helping shape longer-term research projects.
Qualifications
Minimum Qualifications
- Masters/Ph.D. in Computer Science, Data Science, or a related field, with research experience in databases, distributed systems, systems software, or machine learning systems.
- Strong publication record in leading peer-reviewed research venues such as SIGMOD, VLDB, SOSP, OSDI, NSDI, or NeurIPS, with work relevant to databases or systems.
- Strong programming and systems-development background, including experience working with database internals or other large systems codebases such as PostgreSQL, MySQL, ClickHouse, or comparable modern DBMSs.
- Proficiency in systems programming using C, C++, Rust, Zig, or a comparable low-level programming language.
- Strong analytical, experimental, problem-solving, and technical communication skills.
Preferred Qualifications
- Experience developing or operating database systems in cloud environments such as AWS, Google Cloud Platform, or Microsoft Azure.
- Familiarity with large language models, reinforcement learning, learned optimization, or other techniques at the intersection of machine learning and systems.
- Experience contributing to large open-source systems projects, particularly database engines, distributed systems, compilers, or storage systems.
Bonus Qualifications
- Familiarity with modern systems-development and verification tooling, including formal specification or model-checking frameworks such as TLA+/PlusCal, Alloy, or Lean, as well as relevant development tools and languages such as LLVM, eBPF, Python, Go, or advanced profiling and debugging tools (e.g., perf, sanitizers, or tracing frameworks).
- Proven track record in optimizing database systems via hardware acceleration (e.g., GPU, FPGA, CXL), with a focus on designing methods for offloading for query execution, storage layers, and data-intensive primitives using frameworks like CUDA, SYCL, ROCm, or specialized FPGA development flows.
Perks
- Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries.
- Healthcare - Employer contributions towards your healthcare.
- Equity in the company - Every new team member who joins our company receives stock options.
- Time off - Flexible time off in the US, generous entitlement in other countries.
- A USD$500 Home office setup if you’re a remote employee.
- Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites.
Culture - We All Shape It
As part of a rapidly scaling start-up, you will be instrumental in shaping our culture.
Are you interested in finding out more about our culture? Learn more about our values here. Check out our blog posts or follow us on LinkedIn to find out more about what’s happening at ClickHouse.
Equal Opportunity & Privacy
ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Compensation Range: $175K - $235K
Salary Context
This $175K-$235K range is above the median for Research Scientist roles in our dataset (median: $197K across 73 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 8,943 AI roles we're tracking, Research Scientist positions make up 2% of the market. At clickhouse, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $222,200 based on 708 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($205K) sits 8% below the category median. Disclosed range: $175K to $235K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
clickhouse AI Hiring
clickhouse has 1 open AI role right now. They're hiring across Research Scientist. Based in US. Compensation range: $235K - $235K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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