Location: Remote - Singapore
Workplace: Remote
ABOUT US
At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.
With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.
Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.
ABOUT THE ROLE:
We're looking for a Deployed Engineer to join our Professional Services team, working directly with enterprise customers to build reliable, production agents. You'll translate vague enterprise workflows into concrete software specs and guide engineering teams through the resulting solution, or build it for them. You might spend a week designing a customer's agent architecture, a few weeks co-building their evaluation pipeline, or a quarter embedded inside their team shipping alongside their engineers. You are someone who's built real AI systems for production and can defend the technical tradeoffs within them.
KEY RESPONSIBILITIES:
- Advising: Agent architecture design, evaluation strategy review, and best-practice production guidance.
- Building: Co-build with the customer's engineering team across the full Agent Development Lifecycle (ADLC) in outcome-scoped engagements.
- Embedding: Serve as a deployed engineer inside the customer's team for extended engagements, operating as a de facto member of their org to ship agent systems directly.
- Agent Engineering: ADLC end-to-end, architecture design, orchestration patterns, evals, custom conversational UIs, and production deployment.
- Applied AI: Post-training, supervised fine-tuning, harness engineering, trace mining, model selection and evaluation methodology.
REQUIREMENTS:
- 4+ years of software engineering experience with deep expertise in Python. TypeScript/JavaScript a plus.
- 2+ years of hands-on experience building and shipping production agent systems.
- Strong client-facing communication skills, with the ability to confidently articulate architectural decisions to technical stakeholders (engineers, architects, CTOs).
- Strong experience with LangChain/LangGraph/Deep Agents or comparable frameworks, including multi-agent patterns and state management (short and long-term memory).
- Deep familiarity designing and implementing evaluation methodologies for non-deterministic AI systems.
- Comfortable operating across the full spectrum from advisory to embedded delivery.
- Willing to travel up to 20% of the time.
NICE TO HAVE:
- Exposure to dataset curation and post-training techniques (SFT, DPO, RLHF) on open-weight models using tools like Axolotl, Unsloth, Hugging Face transformers, or TRL.
- Experience with trace mining to drive continuous improvement loops
COMPENSATION:
$185,000-$260,000 base salary + equity
Location:
- Singapore
- Remote with up to 25% travel
Looking to lead technical strategy across the customer journey from Proofs of Value (PoVs) to driving overall adoption? Please check out the Deployed Engineer listing focused on Account Strategy & Solutions.
Compensation Philosophy:
We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks.
We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations.
BENEFITS
Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.
• Создавать игровые локации — от блок-схем и планировки до полноценного блокинга в UE5; • Прорабатывать структуру и сюжетное наполнение локаций совместно с нарративными дизайнерами;
-Развивать DWH HR-отчетности: участвовать в проектировании и масштабировании корпоративного хранилища данных. -Интегрировать источники: собирать, очищать и консолидировать данные из учетных систем (в том числе SAP HCM/SAP BW).
-Анализ и верификация контрактов (API) между клиентом и сервером. -Функциональное тестирование эндпоинтов: REST API и gRPC (изолированное и интеграционное тестирование) в соответствии с DoD.
As a Cyber Defense Engineer on our Cyber Defense Operations team, you will serve as a front-line defender responsible for protecting both BeyondTrust’s enterprise infrastructure and the integrity of the products our customers depend on. 4+ years of experience in a SOC, security operations, or incident response role.
As a Senior Customer Engineer, you own strategic and complex Enterprise accounts across acquisition, implementation, adoption, expansion, and escalation. You lead technical strategy for complex deployments, resolve ambiguous customer challenges, and partner closely with account teams to drive retention, expansion, and customer value.
Develop, maintain, and own key fraudulent CV models of Jumio, which shapes the whole fraud product offering of Jumio. Design and implement machine learning, deep learning, classical CV focused on fraud detection.
Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition). Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions.
Задачи:
— Строить надежные backend-системы и проектировать современные API для новых функций продукта.
— Работать в кросс-функциональной fullstack-команде в тесном сотрудничестве с продуктом, дизайном, customer success, поддержкой и продажами.
— Работать напрямую с сооснователями компании.
— Иметь большую автономию и влияние.
— Выпускать новые функции каждую неделю.
— Владеть центральными, сложными компонентами приложения.
— Вести и реализовывать дорожную карту продукта, превращая черновые спецификации в функции.
— Находить элегантные решения проблем удобства использования.
— Обеспечивать лидерство и наставничество для других инженеров команды.
— Реализовывать масштабируемый, стабильный и удобный продукт, способный справляться с текущим ростом.
— Сотрудничать с опытными инженерами на этапах проектирования, разработки и поддержки продукта.
Требования:
— Опыт проектирования и поддержки сложных, больших и растущих систем.
— При рассмотрении новых системных дизайнов или кода естественно учитывать возможные ошибки: крайние случаи, режимы отказа, узкие места, миграции, релизы, интересные метрики и т.д.
— Любовь к отладке и поиску корневых причин проблем в продакшене.
— Проактивность: при обнаружении проблем быстро исправлять или предлагать улучшения.
— Стремление использовать лучшие практики и всегда тестировать поставляемое ПО.
Условия:
— Работа в стартапе после раунда Series A.
— Поддержка Y Combinator.
— Сильная техническая команда основателей из MIT и IST.
— Быстрорастущий стартап с продуктом, имеющим рыночное соответствие.
— Конкурентная зарплата.
— Медицинская страховка.
— Опционы на акции.
— Ежегодная корпоративная поездка в секретное место и другие бонусы.
Position - Digital IC Design Engineer. Requirements - Bachelors degree in Electrical Engineering, Electronics Technology, Electrical Engineering Technology, Electrical and Computer Engineering or related field.
• Own AI features from concept to production in a high-load environment (50k RPS, 450M users). • Work with LLMs and train purpose-built models for real-time applications.
Location: Remote, Canada; Remote, United States
GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.
The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems.
Co-create the future with us
as we build technology that transforms how the world develops software.
*
Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.
An overview of this role
As a Senior Backend Engineer on GitLab's Security Factory: Code Security team, you help developers find and fix security issues. Those issues live in the code they write and in the open source components they depend on.
Your work spans complementary parts of a complete security analysis. On the dependency analysis side, you teach the analysis engine what a project depends on. You parse dependency manifests, lockfiles, and SBOMs so the engine knows which libraries and frameworks are in play and how the code uses them. You also extend the service that turns security advisories into automated merge requests that update vulnerable dependencies. On the vulnerability detection side, you grow GitLab's security analysis engine with hybrid analyzers that blend rule-based detection and AI reasoning. You build and apply the tooling that develops, evaluates, and ships those analyzers, and you measure how well they find real problems. That work also expands coverage of the libraries, frameworks, and AI agent tooling that real-world applications are built on.
These threads reinforce each other. What a project depends on, and how its code uses those dependencies, are parts of one picture, and this team works across all of it. We make thoughtful use of AI-assisted development tools in our daily work. We expect you to use them effectively and with good judgment.
What you do
Act as the directly responsible individual (DRI) for team initiatives from design through delivery, shipping with minimal guidance in partnership with the technical lead
Bring systems built by one engineer to team ownership through documentation, tests, and shared review
Design and ship analyzers that pair deterministic analysis capabilities with AI-driven analysis, and build the evaluation harnesses that measure their false positives and missed findings against benchmark applications with known vulnerabilities, raising the bar for what counts as a trustworthy result. That includes detection rules mapped to CWE and the test fixtures that prove they work.
Package those analyzers for every place GitLab runs them, including CI jobs, AI agent workflows, and command-line tools, with findings reported in GitLab's standard security report formats
Design and ship the manifest, lockfile, and SBOM parsing that tells the static analysis engine what a project depends on, and extend it to new ecosystems and formats
Ship features across the automated remediation service that turns security advisories into dependency update merge requests, from sandboxed updates to merge request creation
Solve technical problems of high scope and complexity, actively seek out difficult impediments affecting the whole team, and advocate for improvements to quality, security, and performance with Product Management, Engineering stakeholders such as Frontend and UX, and partner teams such as Code Scanning and Composition Analysis
Mentor Intermediate engineers through code review and pairing, and maintain our internal standards for style, maintainability, and best practices through review
Participate in on-call rotations to assist troubleshooting product operations, security operations, and urgent engineering issues
What you bring
Experience building your own LLM tooling, such as a harness, an agent pipeline, or evaluations, with the judgment to know when output is trustworthy and when it isn't
Substantial professional experience writing and testing production code in a systems language, particularly Go and/or Rust, with depth in at least one. We use both, plus Ruby on the remediation side and Python for some analyzers, and value willingness to work across them.
Familiarity with package managers and dependency management in one or more language ecosystems, such as npm, Maven, pip, Bundler, or Cargo. Experience building tooling around them is better still.
Demonstrated application security experience, such as vulnerability research, secure code review, or writing detection rules, and fluency with vulnerability classes (OWASP Top 10, CWE) and the software supply chain
A track record of taking ownership of ambiguous problems and shipping with minimal guidance, with the self-motivation and organizational skills suited to a remote, largely asynchronous environment
Demonstrated capacity to communicate clearly and concisely about technical problems, and to write design proposals that bring a team to a decision
Helpful experience
Experience evaluating AI-driven detection against labeled data, including measuring false positives and missed findings
Experience with performance optimization at scale
Hands-on program analysis experience, such as parsing, ASTs, or data-flow analysis
Familiarity with popular web or mobile application frameworks and how they handle input, data, and configuration
Experience with containerized workflows and CI/CD (we use Docker heavily)
About the team
The Security Factory: Code Security team builds GitLab's code-level security scanning capabilities, spanning static application security testing and dependency scanning. We help developers find and fix security issues in both the code they write and the open source components they depend on, as early as possible.
We work closely with the Static Analysis team on GitLab's SAST engines, with Composition Analysis on the broader software supply chain, and with other groups across the Sec Section. We rely heavily on asynchronous work across time zones.
Remote-Global
The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our
benefits
and
equity
. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary.
United States Salary Range
$139,200
—
$235,200 USD
How GitLab Supports Full-Time Employees
Benefits to support your health, finances, and well-being
Flexible Paid Time Off
Team Member Resource Groups
Equity Compensation & Employee Stock Purchase Plan
Growth and Development Fund
Parental Leave
Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from
underrepresented groups
are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.
Country Hiring Guidelines:
GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.
Privacy Policy:
Please review our
Recruitment Privacy Policy.
Your privacy is important to us.
GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also
GitLab’s EEO Policy
and
EEO is the Law
. If you have a disability or special need that requires
accommodation
, please let us know during the
recruiting process
.
Location: Remote, Canada; Remote, Israel; Remote, United Kingdom; Remote, United States
GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.
The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems.
Co-create the future with us
as we build technology that transforms how the world develops software.
*
Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.
An overview of this role
As a Staff Backend Engineer on GitLab's Security Factory: Code Scanning team, you help developers find and fix security issues. Those issues live in the code they write, and you set the technical direction for the static analysis engine that finds them.
Your work spans two complementary parts of a complete security analysis. On the engine side, you shape how the static analysis toolkit models a program: parsing source into intermediate representations, resolving symbols, building call graphs, and tracking tainted data across files and languages. You define the architecture and specifications the team builds against, and you delegate component specifications within them to engineers and to the AI agents that implement them. On the evaluation side, you build and apply tooling that tests, measures, and validates what the engine finds against benchmark applications with known vulnerabilities.
What the engine can model and how we measure what it finds are parts of one picture. As a Staff engineer you hold that picture, shape the long-range technical goals of the team, and raise the bar for every engineer on it. We build the engine with AI agents that write and review code from written specifications, under human direction. We expect you to direct that work effectively and with good judgment, and to help the team do the same.
What you do
Act as the directly responsible individual (DRI) for the team's highest-scope initiatives from design through delivery, shipping large features with minimal guidance and shaping the long-range goals of the team
Bring systems built by one engineer to team ownership through documentation, tests, and shared review
Set the technical direction for the AI-assisted tooling that implements, reviews, and validates engine changes and findings, design the checks that decide whether an agent-written change or generated result is fit to merge, and measure detection quality against benchmark applications with known vulnerabilities, raising the bar for what counts as a trustworthy result. That includes static application security testing (SAST) rules mapped to CWE and OWASP, and the test fixtures that prove they work.
Own the architecture of the program model (parsing, symbol resolution, intermediate representations, call graphs, and taint and data-flow analysis) and of the pipeline that turns source code into findings, and define how both extend to new languages and frameworks
Solve technical problems of the highest scope and complexity, actively seek out difficult impediments affecting the whole team, and advocate for improvements to quality, security, and performance with Product Management, UX, and partner teams such as Code Security and Composition Analysis
Mentor all engineers on the team through code review and pairing, remove blockers to their autonomy, and shepherd the definition and improvement of our internal standards through review
Participate in on-call rotations to assist troubleshooting product operations, security operations, and urgent engineering issues
Define the overarching architecture and specification documents for the engine, delegate component specifications within them, and drive their implementation by AI agents and engineers working independently through phased plans, small reviewable units of change, and specifications kept in lockstep with the code
Build and maintain the harness, agent instructions, and agentic skills that keep agent-written code trustworthy, including separate coding and reviewing agents, rules and tests the coding agents cannot change, independent review panels of agents from different model providers, and performance, API, and dependency gates, with risky changes escalated to human review
Stay current with research in program analysis and security, run AI-assisted research and spikes, and turn the findings into prototypes, proposals, and upstream contributions, including responsible disclosure of findings in open source projects
What you bring
Experience building your own LLM tooling, such as a harness, an agent pipeline, or evaluations, with the judgment to know when output is trustworthy and when it isn't
Extensive professional experience writing, testing, and reviewing production code in Rust, Go, or a comparable systems language, with depth in at least one. The engine is Rust, the analyzer wrapper is Go, and the monolith integration is Ruby, and we value willingness to work across them.
Experience with performance optimization and with containerized workflows and CI/CD (we use Docker heavily)
A deep program analysis and static analysis background, such as parsing and ASTs, intermediate representations (SSA, control-flow and call graphs), taint and data-flow analysis, type inference, incremental and fixpoint computation, or writing detection rules, with the ability to read, evaluate, and apply the research literature
Deep application security experience, such as vulnerability research, secure code review, or writing detection rules, and fluency with vulnerability classes (OWASP Top 10, CWE)
Demonstrated capacity to communicate clearly and concisely about complex technical, architectural, and organizational problems, and to write architecture and design specifications that bring a team to a decision
A track record of taking ownership of ambiguous, team-wide problems and shipping them from concept to production with minimal guidance, with specific, regular async updates on progress, blockers, risks, and next steps
Experience defining a system's overarching architecture, delegating component specifications to engineers and AI agents, and getting them implemented reliably
A track record of mentoring engineers, raising a team's technical bar, and influencing technical direction by achieving consensus with peers and leadership
Helpful experience
Familiarity with popular web or mobile application frameworks and how they handle input, data, and configuration
Experience designing guardrails for agent-written code, such as mutation testing, fuzzing, and CI gates the coding agent cannot modify
A record of engaging with the research community, such as publications, tool papers, or upstream open source contributions in program analysis or security
About the team
The Security Factory: Code Scanning team develops GitLab's SAST capabilities for customer software repositories.
We work closely with the Code Security team on scanning rules & flows, with Composition Analysis on the broader software supply chain, and with other groups across the Sec Section. We rely heavily on asynchronous work across time zones.
Remote-Global
The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our
benefits
and
equity
. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary.
United States Salary Range
$152,800
—
$259,200 USD
How GitLab Supports Full-Time Employees
Benefits to support your health, finances, and well-being
Flexible Paid Time Off
Team Member Resource Groups
Equity Compensation & Employee Stock Purchase Plan
Growth and Development Fund
Parental Leave
Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from
underrepresented groups
are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.
Country Hiring Guidelines:
GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.
Privacy Policy:
Please review our
Recruitment Privacy Policy.
Your privacy is important to us.
GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also
GitLab’s EEO Policy
and
EEO is the Law
. If you have a disability or special need that requires
accommodation
, please let us know during the
recruiting process
.
About the job:
QV.AC is a general purpose AI assistant that runs locally on your devices, giving people real control over their data and AI usage without cloud dependency or data harvesting. The focus is on local, on-device, privacy-preserving AI connected across Tether's peer-to-peer ecosystem.
Responsibilities:
Engineering:
— Run the app and feature teams day to day: unblocking, reviewing, and keeping them shipping.
— Set and hold a high engineering bar for performance, reliability, and clean APIs.
— Own the technical design of features and the API surface between the AI core and the app.
— Ensure close collaboration with AI research and deep tech teams on code reviews, design, and anything involving models or inference.
Product Partnership:
— Work alongside product managers to turn vision and roadmap into scoped, buildable work.
— Push back when scope and reality don't match and propose better paths.
— Bring an engineering perspective to product decisions, especially regarding local AI model capabilities and limitations.
— Help the team move fast using AI tools and quick prototypes to validate ideas before commitment.
Leadership:
— Create clarity around team goals and define success.
— Mentor team leads to build strong operating systems, improve collaboration, and grow as leaders.
— Lead by example: actively contribute when needed and step back when teams are ready to own their work.
Mandatory Requirements:
— Experience managing engineers or engineering leads who ship consumer products, ideally AI-powered apps.
— Strong product sense to distinguish good features from busywork.
— Intuition for applied AI and on-device constraints such as model size, latency, memory, battery, and the gap between demos and reliable features.
— Heavy use of AI tools in own work and enthusiasm for experimenting with new models and capabilities.
— Deep knowledge of JavaScript and React.
— Initiative, curiosity, resilience, and ability to give and receive direct critique.
Preferred:
— Experience with local or on-device inference, LLM evaluations, cross-platform mobile and desktop apps, or open source.
Work format:
— Remote work from anywhere in the world.
Additional information:
— Recruitment scams warning and official application channels provided.
— Official careers page: https://tether.recruitee.com/
— Communication only via official company emails ending with @tether.to or @tether.io.
— No requests for payment or financial details during hiring.
— Candidates encouraged to mention the word EXTOLL when applying to confirm they read the job post fully.
Ищу Senior Data Engineer, который сможет взять на себя развитие нового DWH / Lakehouse-контура и стать владельцем хранилища данных end-to-end. — перенести текущую аналитику на новую Lakehouse-архитектуру;
УдалённоseniorРоссия
УдалённоseniorRemote, Canada; Remote, United States; We rely heavily on asynchronous work across time zones; The base salary range for this role’s listed level is currently for residents of the United States only
УдалённоRemote, Canada; Remote, Israel; Remote, United Kingdom; Remote, United States; We rely heavily on asynchronous work across time zones; The base salary range for this role’s listed level is currently for residents of the United States only
