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Staff Software Engineer

Оценка ИИ

Отличная позиция для опытного инженера: работа в крупной международной AI-компании, высокий уровень автономии и возможность влиять на продукт, используемый S&P 500. Использование современных инструментов (AI-assisted dev) и работа с интересными технологическими вызовами делают вакансию крайне привлекательной.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль Staff-уровня требует не только глубоких знаний Python и системного дизайна, но и способности влиять на архитектуру всей организации без формальной власти. Высокая сложность обусловлена необходимостью работы с высоконагруженными пайплайнами данных и интеграцией AI-инструментов в процесс разработки.

Анализ зарплаты

Медиана85 000 $
Рынок70 000 $ – 110 000 $
Оценка ИИ

Зарплата для позиции Staff Engineer в Дели обычно значительно выше среднего по рынку. Учитывая масштаб AlphaSense и требования к роли, можно ожидать компенсацию в верхнем дециле для данного региона, дополненную бонусами и опционами.

Сопроводительное письмо

I am writing to express my strong interest in the Staff Software Engineer position within the Unstructured Content Portfolio at AlphaSense. With extensive experience in designing and scaling production-grade systems in Python and Java, I have a proven track record of leading cross-team initiatives and making critical architectural decisions that balance innovation with operational reliability. My background in building real-time ingestion pipelines and managing complex entitlement systems aligns perfectly with the challenges of the Broker Research domain.

I am particularly drawn to AlphaSense’s commitment to integrating AI-assisted development into the engineering workflow. As an active practitioner of AIDLC, I leverage tools like Cursor and Copilot to accelerate delivery while maintaining high standards for system design and observability. I am eager to bring my expertise in Kubernetes, cloud infrastructure, and distributed systems to help AlphaSense continue to deliver low-latency, high-impact insights to its global client base.

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Откликнитесь в alphasense уже сейчас

Присоединяйтесь к AlphaSense, чтобы определять будущее AI-платформ для анализа рыночных данных и влиять на решения крупнейших компаний мира!

Описание вакансии

About AlphaSense:

The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content.

The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S&P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us!

About the Role

We're looking for a Staff Software Engineer who owns technical direction, thrives in ambiguity, and uses AI as a natural part of how they build software. You won't just write code - you'll make decisions that shape systems for years, mentor engineers across teams, and drive the engineering bar higher across the organization.

This role sits within the Unstructured Content Portfolio, Broker Research domain - the systems that acquire, process, and deliver equity research from major brokers, and the authorization layer that controls who can access what. You'll work on real-time ingestion pipelines, multi-vendor API integrations, entitlement enforcement across distributed systems, and content delivery at scale - where compliance, reliability, and low-latency processing matter deeply.

As a Staff Engineer, you'll operate at the intersection of technical depth and organizational influence - turning ambiguous business problems into executable technical strategies, and shipping them end-to-end.

What You'll Do

  • Set technical direction for your area - make build-vs-buy decisions, define architecture, and own the technical roadmap alongside product leadership.
  • Take ambiguous problems and make them concrete - scope work, identify risks, break down large initiatives into deliverable increments, and drive alignment across teams.
  • Design and deliver production-grade systems - scalable pipelines, robust services, and high-performance solutions that serve real users at scale.
  • Leverage AI tools as part of your workflow - you use AI-assisted development (Claude Code, Cursor, Copilot) to accelerate your work and have formed opinions on when it helps and when it gets in the way.
  • Evaluate and integrate AI/ML capabilities into production systems when the problem calls for it - you don't need to be an ML researcher, but you're sharp enough to pick up LLMs, embeddings, or classification models and put them to work.
  • Drive cross-team technical initiatives - influence engineers and teams you don't manage. Lead RFCs, drive architectural reviews, and build consensus on hard technical decisions.
  • Own what you build - from requirements to release to production. You build it, you run it. You monitor SLOs/SLIs, troubleshoot production issues, and continuously improve reliability.
  • Raise the engineering bar - through code reviews, mentorship, technical documentation, and by modeling the standards you expect from others.

Must Have

  • Strong in Python (our primary backend language). Comfortable working across languages - you've shipped production code in at least two.
  • Designed and owned production systems serving real users at scale - not just contributed to them, but made consequential architectural decisions and lived with the outcomes.
  • Led cross-team technical initiatives without formal authority - driven migrations, platform changes, or architectural shifts that required aligning multiple teams.
  • Strong system design instincts - you think in terms of failure modes, data flow, scalability, and operational cost. You design for the system you'll maintain, not just the one you'll ship.
  • Deep DevOps and operational experience - Kubernetes, cloud infrastructure (AWS/Azure/GCP), CI/CD, observability. You don't throw code over the wall.
  • Track record of mentoring engineers and raising team standards - through pairing, reviews, RFCs, and leading by example.

Good to Have

  • Experience leading large-scale migrations or platform rewrites
  • Hands-on experience with AI/ML in production - LLMs, BERT, NLP pipelines, or document understanding systems
  • Contributed to or driven engineering-wide standards, practices, or tooling
  • Experience with content processing, enrichment, or search systems at scale
  • Familiarity with Java (parts of our stack)
  • Experience with GitOps, ArgoCD, or Infrastructure as Code
  • Active practitioner of AI-assisted development (AIDLC) - uses AI tools daily in their engineering workflow

Why This Role

  • Real impact at scale - our entitlements engine manages complex workflows for an exponentially growing number of entitlement data for users and documents. Your architectural decisions will directly affect Top Global Companies.
  • Autonomy with accountability - Staff Engineers at AlphaSense own their technical area. You'll have the freedom to make big decisions and the responsibility to make them stick.
  • Hard problems, not busywork - Complex rule engine, Multi-format document processing, real-time entitlements with low latency. The problems are genuinely interesting.

AlphaSense is an equal-opportunity employer. We are committed to a work environment that supports, inspires, and respects all individuals. All employees share in the responsibility for fulfilling AlphaSense’s commitment to equal employment opportunity. AlphaSense does not discriminate against any employee or applicant on the basis of race, color, sex (including pregnancy), national origin, age, religion, marital status, sexual orientation, gender identity, gender expression, military or veteran status, disability, or any other non-merit factor. This policy applies to every aspect of employment at AlphaSense, including recruitment, hiring, training, advancement, and termination.

In addition, it is the policy of AlphaSense to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations, and ordinances where a particular employee works.

Recruiting Scams and Fraud

We at AlphaSense have been made aware of fraudulent job postings and individuals impersonating AlphaSense recruiters. These scams may involve fake job offers, requests for sensitive personal information, or demands for payment. Please note:

  • AlphaSense never asks candidates to pay for job applications, equipment, or training.
  • All official communications will come from an @alpha-sense.com email address.
  • If you’re unsure about a job posting or recruiter, verify it on our Careers page.

If you believe you’ve been targeted by a scam or have any doubts regarding the authenticity of any job listing purportedly from or on behalf of AlphaSense please contact us. Your security and trust matter to us.

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Навыки

  • Python
  • Java
  • Kubernetes
  • AWS
  • Azure
  • GCP
  • CI/CD
  • System Design
  • Microservices
  • LLM
  • NLP
  • ArgoCD
  • Infrastructure as Code
  • GitOps

Возможные вопросы на собеседовании

Проверка способности проектировать отказоустойчивые системы с низкой задержкой.

Как бы вы спроектировали систему обработки прав доступа (entitlements) в реальном времени для миллионов документов, обеспечив при этом минимальную задержку и высокую согласованность данных?

Оценка опыта лидерства и влияния на другие команды.

Опишите случай, когда вам нужно было внедрить архитектурное изменение, затрагивающее несколько команд. Как вы добивались консенсуса и справлялись с сопротивлением?

Проверка практического опыта работы с AI в разработке.

Как вы используете AI-инструменты (например, Cursor или Copilot) в своей ежедневной работе? В каких случаях, по вашему мнению, они могут навредить качеству кода или архитектуре?

Оценка ответственности за эксплуатацию систем.

Расскажите о самом сложном инциденте в продакшене, с которым вы столкнулись. Как вы его диагностировали и какие системные изменения внедрили, чтобы это не повторилось?

Проверка навыков принятия решений.

Какими критериями вы руководствуетесь при принятии решения 'build vs buy' для критически важного компонента инфраструктуры?

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