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alphasense
Страна
Канада
Зарплата
143 000 CA$ – 197 000 CA$
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Staff Software Engineer

Оценка ИИ

Высокая оценка обусловлена престижем компании, сложностью задач на острие ИИ-технологий и прозрачным диапазоном компенсации для удаленной работы.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль уровня Staff требует не только 10+ лет опыта, но и способности проектировать сложные системы на стыке ИИ и распределенных вычислений, а также высокого уровня технического лидерства.

Анализ зарплаты

Медиана175 000 CA$
Рынок150 000 CA$ – 210 000 CA$
Оценка ИИ

Предложенный диапазон 143k–197k CAD полностью соответствует рыночным стандартам для позиций уровня Staff в Канаде, особенно для удаленного формата в крупных технологических компаниях. Верхняя граница выше медианы рынка, что делает предложение конкурентоспособным.

Сопроводительное письмо

I am writing to express my strong interest in the Staff Software Engineer position at AlphaSense. With over a decade of experience in building large-scale distributed systems and a deep focus on data processing pipelines, I am particularly drawn to your mission of reducing human dependency in financial data extraction through sophisticated AI and deterministic safeguards. My background in architecting high-throughput systems that handle heterogeneous data sources aligns perfectly with the technical challenges of your extraction and normalization platform.

In my previous roles, I have successfully led the transition from manual validation to automated, confidence-based scoring systems, ensuring both scalability and traceability. I am excited by the opportunity to act as a technical authority at AlphaSense, balancing probabilistic AI approaches with procedural reliability to deliver trusted insights to the world's most sophisticated companies. I look forward to the possibility of contributing to your engineering excellence and mentoring the next generation of senior talent within your team.

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Составьте идеальное письмо к вакансии с ИИ-агентом

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Откликнитесь в alphasense уже сейчас

Присоединяйтесь к AlphaSense, чтобы возглавить разработку ИИ-платформ нового поколения и определять будущее финансовой аналитики!

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 Team:

Our diverse Product & Engineering team values innovation, collaboration, and the continuous improvement of our platform. We offer a dynamic work environment where employees can grow and thrive while working on cutting-edge technology that shapes the future of AI and search.

At AlphaSense, we believe that diverse experiences and perspectives are critical to our success. We are committed to creating an inclusive workplace where all employees feel valued and empowered to be their authentic selves.

About the Role:

We are seeking a Staff Software Engineer to lead the design, evolution, and reliability of our global financial data extraction and normalization platform. This system ingests filings across formats (HTML, PDF, APIs, spreadsheets), extracts and standardizes financial data using a combination of AI and deterministic systems, and delivers trusted, traceable outputs at scale to downstream financial services teams and customers.

This role owns the self-sourcing and extraction foundation used by multiple teams across the organization. Today, human validation is used as a safety net. Your mission is to systematically reduce human dependency by increasing system correctness, confidence, and trust, without sacrificing speed or scalability.

You will operate at the intersection of distributed systems, AI-driven extraction, data quality, and platform architecture, raising the technical bar and reshaping how we build reliable systems.

Who You Are:

  • 10+ years of experience building and operating large-scale production systems
  • Proven experience designing data ingestion, extraction, or processing systems at scale
  • Deep expertise in one or more of:

+ Distributed systems

+ Data platforms and pipelines

+ AI/ML-powered extraction or classification systems

+ Platform or infrastructure engineering

  • Demonstrated ability to design trustworthy systems that combine probabilistic (AI) and deterministic approaches
  • Strong understanding of system reliability, observability, failure modes, and iterative hardening
  • Experience reducing operational or human overhead through better system design
  • Track record of technical leadership across teams without formal people management
  • Comfortable operating in ambiguity and driving clarity where none exists
  • Passion for using AI responsibly and effectively to deliver scalable, high-confidence systems

Nice to Have

  • Experience with document understanding, NLP, or financial data extraction
  • Experience building provenance, lineage, or confidence-scoring systems
  • Familiarity with cloud-native architectures and modern data stacks
  • Experience shaping or owning internal platforms used by multiple teams

What You’ll Do: 

  • Own the architecture and evolution of our large-scale data extraction and normalization platform
  • Design systems that process hundreds of thousands of records across heterogeneous sources (PDF, HTML, APIs, XLS) with high reliability
  • Define and implement strategies to minimize human-in-the-loop validation through AI-based validation, confidence scoring, provenance tracking, and deterministic safeguards
  • Establish clear system contracts for correctness, traceability, and confidence (e.g., “where did this number come from?”)
  • Balance AI-driven approaches with procedural and rules-based systems where they improve reliability and explainabilityIdentify and remediate architectural and operational bottlenecks impacting scale, accuracy, and developer velocity
  • Act as the technical authority to block poor designs, redesign critical systems, and introduce new platforms or tooling when necessary
  • Partner with product and downstream consumers to define quality bars, SLAs, and success metrics
  • Serve as a technical escalation point for production issues, reliability failures, and systemic risks
  • Mentor senior engineers, shape technical culture, and raise expectations for system design and execution
  • Contribute to long-term technical strategy while remaining hands-on with critical implementations

For base compensation, we set standard ranges for all roles based on function and level benchmarked against similar stage growth companies and internal comparables. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. Final offer amounts are determined by multiple factors including candidate experience/expertise and may vary from the amounts listed below.

You may also be offered equity, and a generous benefits program.

Compensation Range

$143,000—$197,000 CAD

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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Создайте идеальное резюме с помощью ИИ-агента

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Навыки

  • Distributed Systems
  • Data Pipelines
  • AI
  • Machine Learning
  • System Architecture
  • NLP
  • Cloud Native
  • Observability
  • Data Quality

Возможные вопросы на собеседовании

Проверка опыта проектирования систем, способных обрабатывать сотни тысяч записей из разных источников (PDF, HTML, API).

Расскажите о самой сложной архитектуре системы обработки данных, которую вы проектировали: с какими узкими местами вы столкнулись и как их устранили?

Вакансия делает упор на снижение участия человека в процессе валидации данных.

Как бы вы спроектировали систему оценки уверенности (confidence scoring) для ИИ-модели, чтобы минимизировать ручную проверку без потери качества данных?

Важно понимать, как кандидат обеспечивает прозрачность данных («откуда взялось это число»).

Какие стратегии вы используете для обеспечения прослеживаемости (lineage) и объяснимости данных в гибридных системах (ИИ + детерминированные правила)?

Роль Staff подразумевает влияние на другие команды и культуру разработки.

Опишите случай, когда вам пришлось заблокировать неудачное техническое решение или настоять на рефакторинге критической системы. Как вы аргументировали свою позицию?

Проверка навыков работы с облачными технологиями и современными стеками данных.

Каков ваш подход к обеспечению отказоустойчивости и наблюдаемости (observability) в глобально распределенных платформах данных?

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alphasense
Страна
Канада
Зарплата
143 000 CA$ – 197 000 CA$