Location: Ukraine
About the role
We're a global education company running live, expert-led business courses across multiple markets. As we grow, so does the volume of legal questions that come with operating in many jurisdictions at once — how we contract with instructors in different countries, how we structure entities, how we respond when a regulator asks a question.
We're hiring a dedicated legal role to take ownership of the research, analysis, and drafting that sits underneath those decisions.
This is a genuinely broad role for someone early in their legal career who wants exposure to real cross-border commercial and employment questions, rather than a narrow slice of one. You'll research law in jurisdictions you haven't worked in before — the US, the UK and EU, South Africa, Australia, and across APAC — and your job will be to bring back a clear answer, not a hedge.
What you'll do
Research legal questions across multiple jurisdictions as we expand — contractor and partner engagement models, entity structuring, employment and commercial law, etc.
Draft and maintain our contract templates: consulting agreements, teaching assistant agreements, statements of work, amendments, and termination agreements, with jurisdiction-specific variants
Redline counterparty contracts and flag the terms that actually matter
Build and own our legal register: active matters, deadlines, obligations, renewals, filings
Scope and brief external counsel — write the question, assemble the pack, track cost and turnaround, and pressure-test what comes back
Support entity formation, restructuring, and market-entry diligence
Maintain approved communication templates and FAQs for teams who work with our instructors
Build the systems that let a small legal function support a fast-growing company
Requirements
A law degree and 2–4 years of practical legal experience — private practice, in-house, or legal services
Excellent written English. The core output of this role is a one-page memo a non-lawyer can act on immediately. Clarity beats comprehensiveness.
Proven legal research skill, including in legal systems that aren't your own — and the judgment to know when a question needs local counsel and how to frame it for them
Contract drafting and redlining experience
Thoughtful, verified use of AI research tools. We use them; we also check every citation at source.
Comfort with ambiguity, and the initiative to turn a half-formed business question into a precise legal one
Interest in how the business actually works, not just the legal question in isolation
Nice to have
Experience with cross-border contractor or freelancer engagement models
Employment or labour law background
Admission to the bar in your home jurisdiction
Data protection experience (GDPR, POPIA)
Experience in a distributed, remote-first company
Benefits
Competitive Compensation:
Competitive salary,Opportunities for career advancement as ELVTR continues to grow globally
Learn and Grow:
Free access to ELVTR courses,Ongoing professional development opportunities,Career growth within one of the world's fastest-growing online education companies
Remote-First Culture
: Work from wherever you're most productive, Flexible and supportive work environment,Collaborative international team
Health & Wellbeing:
Gym membership reimbursement,Mental health support, Generous paid vacation allowance
Why Join ELVTR?
Make an Impact:
Every course you help create empowers professionals to build new skills, advance their careers, and unlock opportunities they never thought possible.
Grow Your Career:
We're committed to developing our people. As ELVTR continues to expand globally, you'll have opportunities to grow professionally, take on new challenges, and shape the future of online education.
Join a Winning Team
:Work alongside passionate educators, creators, marketers, and innovators who are committed to delivering world-class learning experiences and making education more accessible around the world.
Ready to Help Shape the Future of Online Learning?
Submit your CV and a short note on a legal question you researched where the answer wasn't obvious — what you found, and what you recommended.
Location: Remote - United States
Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.
Scope of the Role:
Where models actually differ now is robustness across accents, noise, and code-switching; speaker diarization; the naturalness of generated speech; and latency under streaming. Measuring those honestly, and building the data that trains for them, is gated as much by data and evaluation design as by architecture. Innodata builds that data and those evaluations for the customers and frontier labs advancing speech and audio models, and we are hiring a Research Scientist to own the science behind it.
You will partner directly with the customers and frontier labs building ASR, text-to-speech, speech-to-speech and conversational voice, diarization, and audio-language models, as interested in the data behind them as in the models themselves. Your work is judgment: which conditions and languages a benchmark must cover to be honest, what a transcription convention should be for a given objective, and when an automated metric can be trusted versus when a human ear is required. You will also partner closely with our transcription and linguistics lead, whose standards directly shape what the models learn.
What You’ll Own:
You will define how Innodata designs, structures, and evaluates audio data for speech and audio models, and you will validate those choices experimentally. Concretely, you will:
Translate the requirements of speech and audio models — ASR, text-to-speech and speech generation, speech-to-speech and conversational voice, speaker diarization and verification, audio-language models, and streaming systems — into concrete data specifications: modalities, transcription and annotation schemas, sampling, and evaluation criteria.
Build evaluation methodology that goes past word error rate — semantic accuracy, robustness to noise and accent, code-switching, diarization error rate (DER), naturalness and intelligibility of generated speech, and streaming latency — and know when automated metrics hold and when they don't.
Decide how existing and incoming audio should be structured, enriched, and sampled for coverage that fits the model objective — across languages, accents, and acoustic conditions (studio, real-world, telephonic), speaker demographics, emotional and paralinguistic range, scripted versus spontaneous speech, and single- versus multi-speaker settings, including low-resource and code-switched speech.
Partner with the transcription and linguistics lead to turn model objectives into transcription specifications, and to quantify how transcription conventions and quality move ASR and speech-model results.
Partner with the audio solutions and engineering team so the audio we collect is built for the model objective: you specify what good data and evaluation require, and they scope programs with customers and capture audio to spec.
Run experiments that prove data decisions matter: fine-tune and evaluate models on Innodata data, with ablations tying specific data choices to measurable improvement.
Design adversarial and stumping evaluations — noisy, accented, and adversarial audio — that surface where speech systems fail, and turn those failures into better data.
Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with.
Work with annotation teams, subject-matter experts, and the synthetic- and augmented-audio pipeline to turn specifications into operational plans.
You’ll Thrive in This Role If You Have:
Roughly 5+ years of hands-on industry experience in speech or audio ML. We weight practical experience over formal credentials; a PhD with a compelling, current research agenda can offset the lower end.
A Bachelor's degree in computer science, electrical engineering, or a related technical or quantitative field is required; an advanced degree (MS or PhD) in a relevant field is preferred.
Trained and evaluated speech or audio models yourself — ASR, TTS, speech-to-speech, speaker, or audio-language models — with strong PyTorch fundamentals.
Fluency in the toolchains and metrics speech work runs on: ESPnet, NeMo, SpeechBrain, or Kaldi, HuggingFace, forced alignment, and WER/CER and the metrics that go beyond them.
Hands-on experience with multilingual, accented, dialectal, low-resource, or code-switched speech, and with synthetic or augmented audio (TTS pipelines, noise and room-response simulation).
A way of thinking in datasets: you have built evaluation sets, reasoned about coverage across conditions, and argued about what makes speech data good for a given objective.
A track record the field recognizes: first-author publications or strong open-source contributions at venues such as Interspeech, ICASSP, ASRU, SLT, or NeurIPS.
The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation.
Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming — for example spoofing and voice-cloning robustness, or bias across accents and languages.
The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications.
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at
https://consumer.ftc.gov/articles/job-scams.
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at
verifyjoboffer@innodata.com
and consider reporting it to the FTC at
ReportFraud.ftc.gov
.
Location: United States
About Turing
Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at
www.turing.com
.
The Role
Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience.
The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here:
Environments for Software Engineering / coding agents
UI-Environments for Computer-Use/Browser-Use agents
MCP-based Environments for general function-calling agents across various enterprise and consumer applications
We are seeking
Research Engineers
to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training.
You will work at the intersection of research and engineering, investigating high-impact questions that improve real-world AI systems. You will design and run rigorous experiments, develop research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications.
This role offers the opportunity to contribute to both foundational research and applied AI development across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.
What You’ll Do
1.
Conduct Research on Frontier AI Systems
Investigate the capabilities, limitations, and training methods of frontier AI systems.
Formulate research questions that can inform Turing’s products, platforms, and technical strategy.
Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.
Stay current with advances in machine learning and identify opportunities for meaningful technical contribution.
2. Build and Evaluate Research Systems
Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks.
Train, test, and evaluate models using modern AI and machine learning tools.
Analyze results carefully and draw clear, evidence-based conclusions.
Establish sound practices for experimental rigor, data quality, reproducibility, and interpretation.
Iterate quickly from early hypothesis through validated technical insight.
3. Translate Research into Practical Impact
Collaborate closely with Research, Engineering, Product, and Operations teams.
Translate research findings into improvements for Turing’s products, platforms, and AI capabilities.
Help identify which ideas are ready to move from exploration into scalable, real-world applications.
Communicate technical findings clearly to both specialized and cross-functional audiences.
4. Contribute to the Research Community
Share findings through technical reports, publications, open-source work, workshops, or conference participation where appropriate.
Contribute to Turing’s research culture through technical discussions, peer review, mentorship, and collaboration.
Represent Turing thoughtfully within the broader AI research community.
What We’re Looking For
Research background:
PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field; exceptional equivalent research experience will also be considered.
Technical depth:
Strong foundation in machine learning and practical experience designing experiments, training or evaluating models, and working with modern AI tooling.
Relevant expertise:
Demonstrated research experience in synthetic or agentic data generation, reinforcement learning or post-training, AI understanding, evaluation, or benchmarks.
Research engineering ability:
Strong programming skills and the ability to implement, test, and iterate quickly in a research environment.
Scientific judgment:
Sound judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making.
Communication and collaboration:
Clear written and verbal communication skills, intellectual curiosity, and the ability to work effectively across research and engineering teams.
A note from our CTO,
Ece Kamar
Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters.
Why Turing
Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design.
Build datasets and environments that directly improve the capabilities of advanced AI systems.
Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks.
Apply frontier AI innovations to high-value enterprise workflows.
Operate with high autonomy, rapid iteration, and meaningful commercial impact.
Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies.
Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS.
Compensation:
$250,000 to $400,000 OTE + Equity
Values
We are client first:
We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
We work at Start-Up Speed:
We move fast, stay agile and favor action because momentum is the foundation of perfection
We are AI forward:
We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.
Advantages of joining Turing
Work at the frontier of AI
, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.
Contribute to leading-edge AI research
and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.
Bring frontier AI innovation to the enterprise
, applying lessons learned from leading AI labs to solve real-world business challenges.
Collaborate with and learn from exceptional colleagues
with deep AI experience from Google, Meta, Amazon, and other leading technology companies.
Move at the pace of AI innovation
, with the speed, ownership, and impact of a startup.
Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
For applicants from the European Union, please review
Turing's GDPR notice here
.
Location: United States
About Turing
Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at
www.turing.com
.
The Role
Turing is seeking exceptional
AI Research Scientists
to join our STEM research organization and develop new ways to evaluate, train, and improve frontier AI systems.
This is a research-first role focused on problems where the right benchmark, dataset, or methodology often does not yet exist. You will identify important gaps in the literature, propose ambitious new research directions, and take projects from initial hypothesis through experimentation, benchmark construction, and publication.
Our research is deliberately focused on
frontier STEM evaluation, synthetic data, hallucination and reliability, and agentic science
. We are looking for scientists who can recognize important problems early, formulate them precisely, and design rigorous research programs to answer them.
What You'll Do
Frontier benchmarks and evaluation
Identify high-impact gaps in existing benchmark and evaluation literature.
Design novel benchmarks in and across STEM fields and on general model functionality.
Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks.
Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies.
Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models.
Synthetic data and post-training
Develop methods for generating high-quality synthetic STEM training data.
Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance.
Explore methods for generating useful training signal in domains where expert human data is scarce or expensive.
Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume.
Hallucination, reliability, and verification
Study hallucination, uncertainty, calibration, and epistemic failure in technical domains.
Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention.
Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know.
Agentic science
Research AI systems capable of performing extended scientific and technical work.
Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning.
Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research.
Explore new approaches to human-AI and multi-agent scientific collaboration.
New research directions
The areas above are our core focus, not an exhaustive list. Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities.
What We’re Looking For
PhD or equivalent research experience in
machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field
.
Demonstrated ability to formulate and execute original research.
Strong understanding of modern LLMs and the frontier AI research landscape.
Excellent experimental design, quantitative reasoning, and scientific judgment.
Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas.
Strong Python skills and the ability to independently build research prototypes and evaluation pipelines.
Excellent technical writing and communication.
Comfort working in a fast-moving environment where the research agenda evolves with the frontier.
A strong publication record is valuable, but we care most about whether you can
identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter
.
A note from our CTO,
Ece Kamar
Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters.
Why Turing
Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design.
Build datasets and environments that directly improve the capabilities of advanced AI systems.
Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks.
Apply frontier AI innovations to high-value enterprise workflows.
Operate with high autonomy, rapid iteration, and meaningful commercial impact.
Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies.
Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS.
Compensation:
$150,000 to $300,000 OTE + Equity
Values
We are client first:
We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
We work at Start-Up Speed:
We move fast, stay agile and favor action because momentum is the foundation of perfection
We are AI forward:
We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.
Advantages of joining Turing
Work at the frontier of AI
, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.
Contribute to leading-edge AI research
and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.
Bring frontier AI innovation to the enterprise
, applying lessons learned from leading AI labs to solve real-world business challenges.
Collaborate with and learn from exceptional colleagues
with deep AI experience from Google, Meta, Amazon, and other leading technology companies.
Move at the pace of AI innovation
, with the speed, ownership, and impact of a startup.
Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
For applicants from the European Union, please review
Turing's GDPR notice here
.
Задачи:
· Ежедневно исследовать TikTok, Instagram Reels и YouTube Shorts по заданным категориям, регионам, хештегам и креаторам;
· Проводить первичный скрининг ориентировочно 80-120 роликов в день;
· Отбирать перспективные ролики для глубокого анализа;
· Анализировать просмотры, вовлеченность, динамику, показатели аккаунта и внутренний коэффициент виральности;
· Проверять ролики по внутреннему чек-листу на релевантность и воспроизводимость механики;
· Корректно заносить результаты в рабочую таблицу;
· Передавать лучшие находки менеджеру на подтверждение;
· Формировать базу успешных креаторов, хуков, форматов и повторяющихся механик;
· Собирать и актуализировать данные по результативности креаторов.
Требования:
· Опыт в сходных задачах в роли Marketing Analyst / Influence Marketing Specialist от 1-2 лет;
· Высокая внимательность к цифрам и деталям;
· Уверенная работа с Google Sheets / Excel: фильтры, сортировка, базовые формулы, большие таблицы;
· Хорошее понимание TikTok / Reels / Shorts;
· Способность аргументировать выбор данными и критериями, а не только субъективным мнением;
· Готовность работать с большим объемом однотипной информации;
· Аналитическое мышление, системность, дисциплина и самостоятельность;
· Готовность к ежедневной research-работе.
Будет плюсом:
· Опыт анализа short-form контента;
· Понимание performance-маркетинга и контент-метрик;
· Опыт работы с VidIQ, SocialBlade или аналогами;
· Английский язык от Intermediate;
· Понимание US/EU digital-контекста, трендов и мемов;
· Опыт research-задач или работы с большими массивами данных.
Условия:
· Официальное трудоустройство;
· Пятидневная рабочая неделя с гибридным форматом работы;
· Быстрый и понятный процесс найма — оперативно проводим собеседования и даем обратную связь;
· Работа с современным англоязычным контентом;
· Постоянный поток задач и возможность расти внутри команды;
· Открытый стиль управления без микроменеджмента — доверяем профессионализму и инициативе;
· Обсуждение идей на равных — не боимся экспериментировать и вместе радуемся результатам.
ГибридАстана; Сейчас находимся в поиске Контент-аналитика
В роли контент-аналитика предстоит заниматься исследованием и анализом контента, выявлением тенденций и подготовкой отчетов на основе собранных данных. Кандидат должен обладать навыками работы с аналитическими инструментами и уметь систематизировать информацию для принятия решений. Дополнительным преимуществом станет опыт в смежных областях и знание современных технологий анализа данных. В рамках работы ожидается взаимодействие с командой для улучшения качества контента и поддержки стратегических инициатив компании.
Location: Remote
Workplace: Remote
Our Team
Dandelion Health unlocks a layer of unexplored clinical depth that has been previously inaccessible at scale. By combining all the raw multimodal data from community health systems with validated AI models, we deliver regulatory-grade scientific insights and products that give teams the evidence to design better trials, enroll the right patients, and generate AI-powered insights that drive confident decisions across the development and commercial lifecycle.
Your Role
You are a healthcare data scientist who knows your way around clinical and electronic health record data. Your primary responsibility is to partner collaboratively with our clients to develop data science solutions and analyze AI-ready datasets to provide key insights using multimodal data. You will curate datasets by identifying patient subpopulations or disease cohorts, and pool multimodal data to drive rapid exploratory AI/ML and Real-World Evidence analyses, model experimentation, and/or model validation. You will lead analytics research from inception to closeout by having ownership over study design, data curation strategies, effort estimation, analytic design, and delivery of final results. You will use your data expertise, programming abilities, and critical thinking skills to support our clients by designing and delivering solutions to help them tackle a broad range of business challenges. Your team’s ultimate goal is to deliver the highest-quality data possible to our clients, who are building products that improve patient health, and demonstrate the value of these data. You will report to the Research Data Science Manager.
Responsibilities
Your day-to-day responsibilities will include the following:
- Collaborate with clients to develop creative analytic solutions that create value and address challenges across critical areas of their business.
- Design statistical analysis plans for evidence generation projects that include methodology, results structure, expected outputs and dataset requirements;
- Query complex source systems in a range of health data sources (e.g., EMRs, ECG data, DICOM data) to identify and map data elements in order to create high-quality datasets for analytic and AI use cases;
- Develop descriptive analyses and in-depth predictive and causal inference models to drive evidence generation on Dandelion data and enhance existing data products;
- Summarize the methods and results from projects into clear explanations and documentation for internal and external audiences including for submission to conferences and peer-reviewed publications;
- Support all phases of SQL/analytical programming, data management, quality control, and reporting for analytics projects;
- Develop code and documentation to deliver high-quality and HIPAA-compliant data products on time to internal and external customers;
- Create summarized findings and recommendations that are clearly presented and adapted for audiences that have a varying range of technical and clinical experience;
- Identify and resolve problems using your knowledge, background, and troubleshooting skills;
- Ensure accuracy, data integrity, and validity of data and analysis in all work;
- Present to senior leadership as well external audiences
You are not afraid to dig into massive, confusing, disorganized new datasets and get them under control. You are excited to learn new environments, languages, and skills. This is a small, early stage company with enormous ambitions and everyone pitches in across the team.
Qualifications
- M.S. or PhD degree in a relevant field, such as Biomedical Informatics, Data Science, Biostatistics, or B.S. with at least seven years of experience
- Experience in client interaction, crafting project proposals, and leading teams.
- Background in statistical modeling and real-world data analysis
- Fluency in Python and SQL
- 1+ years experience with extracting, curating, and analyzing data created within the HIT and healthcare delivery ecosystem (e.g., EMR, claims, registry); this may include knowledge of the roles of data exchange and content standards (e.g., FHIR, CDA, CQL) and clinical terminology standards (e.g., ICD, CPT, LOINC, SNOMED-CT, NDC, RxNorm)
- Strong technical writing, editing, and communication skills along with a collaborative, client-first mindset
- Excellent organizational skills with an ability to embrace change and effectively manage multiple projects and consistently plan work to meet deadlines
- Experience working in or with startups is a plus
Technology Experiences and Skills
We don’t expect anyone to have all of the following skills or experiences, but we do seek candidates who are interested in growing their skill sets and working with healthcare data in all its glorious complexity. The Data Team works closely with our Engineering Team to put our work into production and meet client needs.
- SQL
- Python and/or R
- Git and version control
- Familiarity with encryption methods and writing regular expressions
- Prior experience querying EDWs or databases and creating reports or analytics for healthcare data
- Familiarity with the data aspects of electronic medical records, ex. Epic, Cerner, Allscripts
- Prior experience working with insurance claims data
- Familiarity with medical terminologies or controlled vocabularies such as ICD-10, SNOMED-CT, LOINC, CPT/HCPCS, NDC, and RxNorm
- Any medical ontology experience
- Any NLP experience
- Any experience working with DICOM or other imaging modalities
- Experience with OMOP common data model
- Familiarity with Machine Learning concepts
- Experience with AWS
Note that familiarity with machine learning model development and deployment is not required for this position, but familiarity with high-level ML concepts is a plus.
Nature of our work
Our work is fast paced and iterative. We are growing, and we want to support our team members to grow in their skills as well. We are building a team that approaches problems with a diversity of perspectives, values experimentation, and refining our approach based on that experimentation. We work with the full spectrum of healthcare data from tabular data, videos, images, waveforms, etc. If a health system collects it, we might work with it!
If this looks like a partial fit, please reach out, we would love to share more about the work we do for you to understand if it would be a good fit for you.
There is occasional travel for in-person company working days on roughly a quarterly basis.
Team Benefits
- Remote work and flexible hours. Availability needed for meetings, which we try to keep to a healthy minimum
- Complete wellness benefits including healthcare, dental, vision, PTO, sick days and more. Ask for details
- Professional development days to build your skills
- Collegial work environment
- Academic bent towards inquiry and problem solving but start-up speed and flexibility
- Great balance of focus time to work on projects but easy to access team members to discuss issues and work collaboratively
- Dandelion is a mission-driven company that is focused on improving patient care
Location: United States (Remote)
Workplace: Remote
ABOUT ONEPAY
OnePay is the consumer fintech trusted by millions of Americans to make money better.
Our financial system is broken. High fees, low rates, and too few ways to actually grow your money. We’re fixing it. And we’re moving fast.
We’re an all-in-one financial services platform that brings together banking, high-yield savings, credit cards, point-of-sale lending, investing, and crypto in one place. We also partner with employers, HCM providers, gig platforms, and others to deliver embedded financial services to millions of employees and frontline workers.
We’re backed by Walmart, the world’s largest retailer, and Ribbit Capital, one of fintech’s most respected investors, giving us rare scale, distribution, and the opportunity to build something truly category-defining.
But what really sets OnePay apart is how we move. Our customers don’t have time to wait… and neither do we. This place moves fast, and we’re looking for people who are:
- Ready to run
- Hungry and driven by urgency
- Exceptional at what they do, with low ego
- Comfortable operating in motion
THE ROLE
As an AI Research Intern, your mandate is to research and prototype foundation models, both transformer-based and graph-based, applied to OnePay's customer, transaction, and merchant data. You'll work on real modeling problems and build a working prototype that demonstrates the approach. This role will impact OnePay's vision by providing a remarkable customer experience, driving positive business outcomes, and improving operational efficiency. You will work closely with the product, engineering and applied science team(s) working across all business units. This role reports to the Head of AI/ML.
This role is responsible for:
- Research, design, and train foundation models on OnePay's data, working with transformer and/or graph-based architectures.
- Run experiments, evaluate results rigorously, and turn promising findings into a working prototype.
- Opportunity to work in an extremely fast paced growing company
- Partner with Product, Engineering and Analytics teams to ensure we are providing a remarkable customer experience.
- Driving significant impact on the company’s bottom line.
YOU BRING
- Final-Year Graduate Student: You’re in the final year of a master’s or PhD program, expected to graduate in Fall 2027 or Spring 2028, majoring in Engineering or a related field.
- Deep Learning Fundamentals: You have a strong grasp of how neural networks work under the hood, including backpropagation, attention, embeddings, optimization, and training dynamics. You've implemented, trained, or fine-tuned models yourself through research, coursework, or personal projects, and you're comfortable working in PyTorch or an equivalent framework.
- Prior Experience: You've completed at least one previous internship in research, machine learning, or software engineering, or have built substantial personal or academic projects you're excited to talk through.
- Availability: This is a full-time, 8-week program running June 21 - August 13, 2027. You are available to work in our New York City office at least 3-4 days a week throughout.
- Proactive & Collaborative Attitude: You have an act-like-an-owner mentality, thrive in a fast-moving environment, and enjoy working with others to achieve collective goals.
- Critical Thinking & Problem-Solving Skills: You enjoy tackling complex challenges, analyzing problems, and developing solutions. Clear communication of ideas is one of your strengths.
- Organized & Detail-Oriented: You’re a keen listener with strong organizational skills, capable of managing multiple tasks efficiently.
- Excited to Learn & Grow: You are passionate about being part of a rapidly growing start-up and look forward to expanding your skills in a real-world setting.
NICE TO HAVE
- Familiarity with transformer-based foundation model architectures, including pre-training, fine-tuning, inference, and the tradeoffs between approaches.
- Exposure to graph-based learning or graph foundation models, such as GNNs, message passing, or representation learning on graph-structured data.
- Coursework or projects involving Large Language Models or Generative AI.
- Research output in any of the above: publications, workshop papers, or open-source contributions.
WHAT WE OFFER
- Early access to a high potential, high growth fintech
- Hands on experience building product ready to ship
- Beautiful NYC SoHo Office
- Professional development and team social outings
PROGRAM & APPLICATION TIMELINE
- Program runs June 21 - August 13, 2027 (8 weeks, full-time, in our NYC office)
- We review applications and conduct interviews on a rolling basis as they come in
- No offer decisions will be made until the application window closes – all candidates are evaluated together before final decisions go out
- We are targeting a finalized cohort by December 1, 2026
- If there are intern spots still available, we may reopen applications in Spring 2027
Work Authorization: OnePay will not sponsor applicants who need employment sponsored visas for this position. Future sponsorship will not be considered.
STANDARD INTERVIEW PROCESS
Our process varies by role. Most candidates go through:
- AI-assisted initial screen
- Interview with Talent Partner
- Technical or Hiring Manager Interview
- Team Interview
- Executive Interview
- Offer!
EQUAL EMPLOYMENT OPPORTUNITY
To build technology and products that are used and loved by people and solve real-world problems, we need to build a team with many different perspectives and experiences. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We encourage candidates from all backgrounds to apply. Applicants in need of special assistance or accommodation during the interview process or in accessing our website may contact us at talent@onepay.com.
42 — независимая AI research-лаборатория, создающая универсальные фундаментальные модели для прогнозирования и принятия решений в сложных, быстро меняющихся системах.
Стек: Go, Rust, Kafka, Postgres, ClickHouse.
Амстердам, Нидерланды. Помощь с переездом.
Location: Palo Alto; New York; San Francisco
Workplace: Remote
ABOUT MISTRAL
Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.
We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.
The Role
As a member of the US Talent Acquisition team focused on Research and technical hiring, you will be instrumental in building and scaling Mistral's US-based research and engineering teams from the ground up. Joining early means you'll help shape both our technical recruitment strategy and operations, partnering closely with Research leadership across Research Science, Research Engineering, and adjacent technical functions. Your work will directly influence Mistral's frontier-model ambitions by ensuring we find and engage exceptional researchers and engineers who will drive Mistral's growth in the US. In this role, you'll also foster a strong candidate experience and help establish technical recruiting best practices as we expand across the US.
What You Will Do
Technical Stakeholder Partnership
- Partner deeply with technical leaders — from Research Scientists and Research Engineers to team leads and hiring managers — to understand hiring needs, define role specs, and identify the right technical talent
- Own full-cycle recruitment across Research Science and Research Engineering, with flexibility to support other technical roles as they come along
- Advise hiring managers on market mapping, leveling, comp positioning, and the competitive landscape for US research and engineering talent
- Translate the nuances of each technical role into compelling, accurate candidate outreach
End-to-End Technical Recruitment
- Own the full recruiting lifecycle from sourcing to close, operating with autonomy on complex, competitive technical roles
- Source passive and active candidates across US research and engineering talent pools — from PhD students and postdocs to experienced researchers and engineers at leading AI labs, universities, and technology companies
- Conduct intake and calibration sessions with hiring managers to align on ideal candidate profiles and stage expectations
- Drive efficient, high-conversion pipelines and close candidates in a fiercely competitive market for top research talent
Candidate Experience & Selling Mistral
- Guide candidates through the hiring process with clear communication and a compelling narrative about Mistral's mission, culture, and trajectory
- Articulate Mistral's positioning in the AI landscape — frontier models, open-weight philosophy, and research credibility — to differentiate us against better-resourced labs
- Provide candidates with real insight into our research agenda, compute access, publication culture, and the specific impact of their role
- Deliver a high-quality, respectful candidate experience at every stage, regardless of outcome
Recruiting Operations & Data-Driven Improvement
- Develop and refine technical recruiting processes, tools, and documentation to improve outcomes and efficiency
- Use data to diagnose pipeline issues (e.g., drop-off at specific stages, time-to-fill by role) and propose targeted solutions
- Build and maintain market maps, competitive intel, and talent pools for key US research and engineering segments
- Standardize technical interview rubrics, scorecards, and debrief practices across US technical hiring teams
Raising the Bar
- Collaborate with Research leadership and the broader Talent team to continuously elevate our hiring standards
- Contribute to defining and evolving our technical talent acquisition strategy in the US as we scale our research presence
- Partner on employer-brand and outbound sourcing initiatives — including academic pipelines, conference presence, and open-source community engagement — that position Mistral as a destination for top research and engineering talent
What We're Looking For
- 6+ years of recruiting experience, including 2+ years in a startup or high-growth environment, with a focus on Research Science and Research Engineering roles
- Demonstrated ability to independently hire across the technical stack — from Research Engineers to Research Scientists, and senior research talent
- Deep network and sourcing expertise in US research and engineering talent markets, with credibility in AI/ML and ideally connections across top labs and universities
- Track record of hiring in competitive, candidate-scarce markets where top researchers have multiple offers
- Experience working across multiple US markets, including relocation and visa-driven hiring
- Willingness to work on other roles as needs come up
- Comfort navigating ambiguity and switching between strategic planning and hands-on execution
- Strong written and verbal communication skills, with the ability to influence peers and senior technical stakeholders without direct authority
- Organized, detail-oriented, and able to juggle shifting priorities across a high-volume hiring slate
- Professional experience with modern recruiting tools such as Ashby
- Familiarity with the AI/ML research landscape and the ability to credibly discuss frontier model capabilities, research directions, and publications with technical candidates
WHAT WE OFFER
We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.
For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link.
PRIVACY POLICY
Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.
Лаборатория «Космос» — российский стартап, создающий собственные программно-аппаратные продукты: от первых прототипов до готовых устройств. В портфеле два продукта: «Каспер» — робо-компьютер для обучения детей; «Света» — интерактивная светозвуковая панель для дома и различных пространств.
Задачи:
— исследовать новые AI-модели, сервисы и способы их применения;
— решать отдельные продуктовые задачи с помощью существующих AI-технологий: исследовать рынок, выбирать решение, тестировать и собирать прототип, например, найти и интегрировать технологию для работы Каспера с произношением ребёнка;
— собирать внутренние AI-инструменты для команды из готовых моделей, API и автоматизаций;
— автоматизировать работу с документами, голосовыми сообщениями, ссылками, PDF и базой знаний;
— создавать инструменты для работы команды с видео, изображением, музыкой, звуком и другим контентом;
— проводить исследования по продуктам, технологиям, конкурентам и патентам.
Требования:
— очень хорошее знание современных нейросетей, основных моделей и сервисов;
— постоянное отслеживание новинок и тестирование;
— умение применять AI-технологии для решения задач;
— API, автоматизации и базовый код важны;
— опыт классической разработки будет плюсом, но не обязателен;
— самостоятельность, любопытство, способность быстро проходить путь от задачи и исследования до работающего решения.
Условия:
— работа в офисе в Санкт-Петербурге, центр города, м. Горьковская;
— оплачиваемая работа;
— занятость и график обсуждаются индивидуально;
— уровень вознаграждения зависит от навыков, опыта и объёма занятости.
ОфисСанкт-Петербург; Мы находимся в центре города, м
Задачи:
— исследовать и разрабатывать торговые стратегии для DeFi и крипторынков: от гипотезы и анализа данных до бэктестинга, валидации и оценки результата стратегии
— Quant/ML research и разработка торговых стратегий для DeFi и крипторынков
— применять ML/DL-подходы для маркет-мейкинга, forecasting, управления позициями и смежных задач
— разрабатывать стратегии дельта-нейтрального хеджирования и управления рисками
— проводить бэктестинг и использовать симуляционные среды на крипто-данных: order book, trades
— анализировать рыночные данные и улучшать алгоритмы по результатам реального исполнения
Требования:
— senior-уровень в Quant/ML research, systematic trading, HFT/prop trading, market microstructure или смежных задачах
— участие в полном цикле ресерча: гипотеза > данные > модель/логика > бэктестинг > валидация и анализ результата
— понимание временных рядов, OOS-проверки, future leakage и торговой экономики
— знание Supervised Learning, статистического анализа, оптимизации моделей
— уверенный Python; PyTorch, Scikit-Learn, CatBoost, XGBoost, Optuna
— опыт доведения стратегий до реального исполнения или PnL-оценки
— опыт работы с order book и trades
— автономность в постановке гипотез и доведении их до результата
— владение русским языком для общения в команде
Будет плюсом:
— Reinforcement Learning
— опыт на Rust или low-latency engineering
— опыт в трейдинге, маркет-мейкинге, low-latency крипте
— опыт в квант-фондах, HFT, prop trading, банковских квант-командах
— опыт с DeFi, DEX/CEX, AMM/LP, perpetuals, funding, cross-venue arbitrage
— активное использование AI-инструментов
Условия:
— фиксированная зарплата в стейблкоинах (USDT/USDC, 1:1 к доллару)
— квартальный profit sharing от результатов стратегий
— оплачиваемый отпуск
— компания оплачивает AI-подписки
Location: London
DRW
is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.
Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.
We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it.
DRW
is a place of high expectations, integrity, innovation and a willingness to challenge consensus.
Quantitative Researcher — Compute Markets
We are building the pricing infrastructure for a market that does not yet have any. Compute capacity is becoming a tradeable asset: spot indices exist, listed futures are in development, and bilateral forward and structured transactions are already being negotiated. Almost nobody transacting in this market can price the underlying risk. We intend to.
What you would do
Build and own the pricing framework for forward, structured and credit-linked transactions on compute capacity, including forward curve construction on an asset with no observable forward market.
Produce reservation bids and offers on bilateral requests, and defend them internally.
Own the valuation model for the book, to a standard that survives institutional operational due diligence.
Work directly with trading, risk and the firm's leadership on transaction structure, not only on the numbers.
What we are looking for
PhD or MSc in a quantitative discipline: financial mathematics, operations research, statistics, physics, economics.
Demonstrated ability to build a price from structural first principles where market data is sparse or absent. Prior work on commodities, energy, freight, weather, insurance-linked or other physically-grounded markets is directly relevant, though not required.
Fluency in Python, and comfort with Monte Carlo methods and uncertainty quantification.
The temperament to state an assumption, own it, and change it when the evidence moves.
Prior exposure to compute or semiconductor markets is not expected and will not be weighted.
For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at
https://drw.com/privacy-notice
.
California residents, please review the California Privacy Notice for information about certain legal rights at
https://drw.com/california-privacy-notice
.
#LI-JW1
О проекте и команде:
Технологическая компания, развивающая quantitative-инфраструктуру и ML-решения для рынков цифровых активов. Проект находится на pre-launch стадии, команда специализируется на quant research, machine learning и blockchain-разработке.
Задачи на этой роли:
— Полный цикл исследований по краткосрочной микроструктуре, LP/хеджированию, исполнению, межплощадочным сигналам, распределению риска и смежным задачам crypto/DeFi;
— Дизайн исследования: гипотеза, target/разметка, baseline, метрики, причинно корректная OOS-проверка, анализ режимов и явные критерии GO/NO-GO;
— Работа с данными: поиск источников, выравнивание времени, контроль качества, построение признаков и воспроизводимые контракты данных;
— ML/DL по структуре задачи; RL или другие методы последовательного управления - только когда они действительно оправданы;
— Реалистичная симуляция и торговая экономика: комиссии, проскальзывание, ликвидность, задержки, adverse selection, impact, fill assumptions, хеджирование и синхронизация DEX/CEX;
— Paper/shadow-стратегии, анализ sim-to-real и совместная работа с quant developer при переносе доказанных решений в production;
— Инженерный стандарт исследования: воспроизводимые эксперименты, чистый Python, критичные проверки, мониторинг и дисциплинированное использование LLM/агентов.
Ожидания от кандидата:
— Проверяемый опыт 2-3 Quant/ML проектов, где вы лично отвечали за существенную часть цепочки от постановки задачи и данных до проверки, кода и решения о внедрении либо GO/NO-GO;
— Отличный Python и сильная инженерная база: вы сами пишете, отлаживаете и ревьюите исследовательский код;
— Сильную статистику, вероятность, оптимизацию и работу с временными рядами: временную зависимость, leakage, selection bias, uncertainty и честную OOS-проверку;
— Глубокое практическое понимание микроструктуры и исполнения: order book/flow, liquidity, latency, fees, slippage, adverse selection, impact, sizing и hedging.
— Сильное практическое владение ML/DL для последовательных финансовых данных и способность отличать реальный сигнал от оптимизации метрики;
— Высокую самостоятельность: структурировать неясную проблему, выбирать наиболее информативный эксперимент, быстро останавливать слабые гипотезы и принимать решения на основании доказательств;
— Рабочее понимание структуры crypto-рынка. Прямой опыт DeFi/DEX будет плюсом, но сильный CEX/microstructure опыт может его компенсировать при быстрой адаптации.
Условия и возможности:
— Полная занятость, 5/2, гибридный/удаленный формат, офис в центре Москвы;
— Работа с реальным капиталом, где качество исследования, реалистичность исполнения и риск-контроль имеют прямое значение;
— Прямая работа с Quant/ML, разработкой и руководством компании, высокая самостоятельность и короткий цикл обратной связи;
— Небольшая команда, где сильное исследование может стать production-системой, а не закончиться презентацией;
— Компенсация всех необходимых инструменты и ПО для работы.
О проекте: Международный проект в области AI Security, связанный с разработкой сред, которые помогают LLM-моделям проходить обучение с подкреплением для задач, связанных с кибербезопасностью. Эти среды содержат задания в различных областях кибербезопасности (пентестинг, реагирование на инциденты, эксплуатация, вредоносное ПО и т.д.), цель — создавать достаточно сложные задачи, чтобы модель допускала ошибки, но которые остаются решаемыми для профессиональных специалистов. Это не классическая позиция Pentester или Red Team Engineer. Основная задача — создавать сложные, но реалистичные и проверяемые security-сценарии, в которых LLM-модели совершают ошибки из-за недостатков в рассуждении (reasoning), а не из-за неоднозначных инструкций или ограничений среды.
Задачи:
— Проектировать security-сценарии для оценки и обучения LLM-моделей
— Создавать environments с использованием GitHub, Slack и других инструментов, встречающихся в реальной разработке
— Разрабатывать задачи, требующие сложного многошагового reasoning
— Моделировать реальные offensive security-сценарии (supply chain attacks, malicious pull requests, exploitation через GitHub Actions и другие техники)
— Проверять сценарии на нескольких frontier-моделях и анализировать причины ошибок
— Формулировать ожидаемое поведение модели и описывать, почему ошибка считается reasoning failure
— Автоматизировать создание и проверку задач с использованием Python и других инструментов
Требования:
— 5+ лет практического опыта в Offensive Security, Application Security или Web Penetration Testing
— Глубокое понимание современных техник атак и поверхности атаки веб-приложений, API, GitHub, CI/CD и связанных экосистем
— Практический опыт penetration testing и исследования безопасности приложений
— Опыт разработки на Python и/или JavaScript
— Навыки статического и динамического анализа кода (Python, JavaScript, Java)
— Умение самостоятельно исследовать новые направления, проектировать сложные сценарии и объяснять результаты
— Свободный английский язык (Upper-Intermediate / B2+)
— Практический опыт работы с современными LLM-моделями
— Понимание особенностей работы LLM, ограничений и типичных failure modes
— Интерес к AI Security и исследованию поведения LLM
Будет плюсом:
— Практический опыт AI Red Teaming или LLM Security
— Опыт работы с Agentic AI, AI Agents или Model Evaluation
— Опыт исследований безопасности GitHub Actions, CI/CD, Supply Chain Security
— Сертификаты OSWE, eWPTXv2, OWEE, OSWA или аналогичные
— Публикации, write-ups, участие в Bug Bounty, HackTheBox, TryHackMe, Pwn2Own или других проектах
Условия:
— B2B-контракт (с ИП)
— Оплата в долларах США
— Part-time: ориентировочно 5–20 часов в неделю
— Минимальный объем — 1–2 задачи в неделю
— На выполнение одной задачи обычно требуется 5–10 часов
— Локация — строго вне РФ (либо уже за пределами РФ, либо готовность к релокации)
— Старт — ASAP
О проекте: Международный проект в области AI Security, связанный с разработкой сред, которые помогают LLM-моделям проходить обучение с подкреплением для задач, связанных с кибербезопасностью. Эти среды содержат задания в различных областях кибербезопасности (пентестинг, реагирование на инциденты, эксплуатация, вредоносное ПО и т.д.), цель — создавать достаточно сложные задачи, чтобы модель допускала ошибки, но которые остаются решаемыми для профессиональных специалистов. Это не классическая позиция Pentester или Red Team Engineer. Основная задача — создавать сложные, но реалистичные и проверяемые security-сценарии, в которых LLM-модели совершают ошибки из-за недостатков в рассуждении (reasoning), а не из-за неоднозначных инструкций или ограничений среды.
Задачи:
— Проектировать security-сценарии для оценки и обучения LLM-моделей
— Создавать environments с использованием GitHub, Slack и других инструментов, встречающихся в реальной разработке
— Разрабатывать задачи, требующие сложного многошагового reasoning
— Моделировать реальные offensive security-сценарии (supply chain attacks, malicious pull requests, exploitation через GitHub Actions и другие техники)
— Проверять сценарии на нескольких frontier-моделях и анализировать причины ошибок
— Формулировать ожидаемое поведение модели и описывать, почему ошибка считается reasoning failure
— Автоматизировать создание и проверку задач с использованием Python и других инструментов
Требования:
— 5+ лет практического опыта в Offensive Security, Application Security или Web Penetration Testing
— Глубокое понимание современных техник атак и поверхности атаки веб-приложений, API, GitHub, CI/CD и связанных экосистем
— Практический опыт penetration testing и исследования безопасности приложений
— Опыт разработки на Python и/или JavaScript
— Навыки статического и динамического анализа кода (Python, JavaScript, Java)
— Умение самостоятельно исследовать новые направления, проектировать сложные сценарии и объяснять результаты
— Свободный английский язык (Upper-Intermediate / B2+)
— Практический опыт работы с современными LLM-моделями
— Понимание особенностей работы LLM, ограничений и типичных failure modes
— Интерес к AI Security и исследованию поведения LLM
Будет плюсом:
— Практический опыт AI Red Teaming или LLM Security
— Опыт работы с Agentic AI, AI Agents или Model Evaluation
— Опыт исследований безопасности GitHub Actions, CI/CD, Supply Chain Security
— Сертификаты OSWE, eWPTXv2, OWEE, OSWA или аналогичные
— Публикации, write-ups, участие в Bug Bounty, HackTheBox, TryHackMe, Pwn2Own или других проектах
Условия:
— B2B-контракт (с ИП)
— Оплата в долларах США
— Part-time: ориентировочно 5–20 часов в неделю
— Минимальный объем — 1–2 задачи в неделю
— На выполнение одной задачи обычно требуется 5–10 часов
— Локация — строго вне РФ (либо уже за пределами РФ, либо готовность к релокации)
— Старт — ASAP
Location: London, England; Remote, Europe; Remote, Asia; Remote, US
Workplace: remote
About us
Aave Labs began with a simple question: how do we make financial services like borrowing, saving, and earning more accessible globally?
We built the Aave Protocol, now the world’s largest and most trusted onchain lending market, connecting decentralized finance with individuals, traditional finance, and fintech.
Today, we build trusted, high-quality products that expand access to lending, credit, and savings globally
Our culture
We build trusted products that help people access and discover economic opportunities.
We hold a high bar for quality and craftsmanship. No detail is too small. Trust depends on it.
We’re a global team building products people can actually use, turning complex technology into something simple and reliable.
We stay aligned, move quickly, and think long term, while keeping teams lean and focused on impact.
About the Role
Aave is looking for a Protocol Security Researcher to help build its internal protocol security function.
The role sits at the intersection of smart contract security, protocol design, adversarial research, and AI-assisted review. You will help assess major protocol changes before deployment, maintain security context across Aave’s deployed systems, and reason about the risks introduced by critical dependencies, integrations, and Aave-adjacent infrastructure.
This is not a narrow audit role. The goal is to improve Aave’s ability to understand and reduce protocol risk continuously.
How you can make an impact:
Review major Aave protocol changes before external audit or deployment
Perform attacker-driven analysis of smart contracts, protocol mechanisms, and integrations
Participate in design and architecture discussions early enough to influence security-relevant decisions
Identify risks in Aave-adjacent systems, including assets, bridges, oracles, adapters, and critical dependencies
Contribute to AI-assisted security tooling and evaluate its effectiveness in real review workflows
Turn review findings into reusable knowledge, invariants, assumptions, and testing or monitoring ideas
Work with monitoring and incident response teams when review findings imply operational detection or response needs
Collaborate with external auditors by helping define scope, known risks, and areas of concern
Let's connect if you have:
5+ years of relevant security experience
Strong smart contract security background
Ability to independently review complex codebases and reason about protocol-level failure modes
Attacker mindset: you can move from “this looks wrong” to “this is how it could be exploited”
Strong understanding of DeFi mechanisms, including lending, liquidations, accounting, oracles, collateral, governance, and integrations
Evidence that you are actively using AI to improve security research, review quality, or review throughput
Clear written communication: you can explain risk, impact, uncertainty, and trade-offs to engineers and non-security stakeholders
Good judgment about severity, exploitability, and when a finding matters
Nice to haves:
Experience with formal methods, fuzzing, invariant testing, or custom security tooling
Experience building internal tools for security review, code understanding, or knowledge management
Experience working with external audit firms or leading audit engagements
Familiarity with governance payloads, upgrade systems, permissioning, and incident response
Open-source security research, published findings, CTFs, bug bounties, or prior public vulnerability research
Haven’t quite met all the criteria? Let’s not miss out on the chance to speak. Whilst you might not meet every single requirement, you might bring other, more exciting skills to the companies!
Equal opportunities statement:
Aave Labs celebrates diversity and views each and every team member as a separate individual with their own unique identity. No matter your race, religion, gender, ethnicity, age, disability, sexual orientation or even the wallet you use, we welcome you at Aave Labs. As an equal opportunities employer, we take accountability and believe in everyone's potential to build, create and inspire changes. With a mission to build a diverse workforce, we are proud to foster a working environment in which everyone can feel safe and valued for who they are.
УдалённоRemote - United States
United States; For applicants from the European Union, please review
Не хотите искать каждый день?
Настройте подбор один раз, и подходящие вакансии будут сами приходить в Telegram.
Настройте подбор, и подходящие вакансии будут приходить в Telegram.
United States; For applicants from the European Union, please review
УдалённоГеография найма не уточнена
УдалённоСтажёрUnited States (Remote); We encourage candidates from all backgrounds to apply; Applicants in need of special assistance or accommodation during the interview process or in accessing our website may contact us at talent@onepay.com
Mumbai
УдалённоPalo Alto; New York; San Francisco; Drive efficient, high-conversion pipelines and close candidates in a fiercely competitive market for top research talent; Experience working across multiple US markets, including relocation and visa-driven hiring; Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks
London
ГибридseniorМосква
УдалённоseniorРоссия, США; Условия: B2B-контракт (с ИП) Оплата в долларах США Part-time: ориентировочно 5–20 часов в неделю Минимальный ожидаемый объем — 1–2 задачи в неделю На выполнение одной задачи обычно требуется от 5 до 10 часов, в зависимости от сложности Локация — строго вне РФ (либо уже находиться за пределами РФ, либо быть готовым к самостоятельной релокации) Старт — ASAP
УдалённоseniorРоссия, США; Условия: B2B-контракт (с ИП) Оплата в долларах США Part-time: ориентировочно 5–20 часов в неделю Минимальный ожидаемый объем — 1–2 задачи в неделю На выполнение одной задачи обычно требуется от 5 до 10 часов, в зависимости от сложности Локация — строго вне РФ (либо уже находиться за пределами РФ, либо быть готовым к самостоятельной релокации) Старт — ASAP
УдалённоLondon, England; Remote, Europe; Remote, Asia; Remote, US
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