- Страна
- США
- Зарплата
- 246 000 $ – 339 000 $
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на вакансии с ИИ

Principal Software Engineer
Высокая заработная плата, работа в ведущей ИИ-компании и возможность влиять на архитектуру глобального продукта. Отличные перспективы для инженеров топ-уровня.
Сложность вакансии
Роль требует исключительного опыта (15+ лет) и глубоких знаний в распределенных системах и ИИ. Высокая ответственность за архитектуру платформы, используемой всей компанией, и необходимость работы в условиях неопределенности.
Анализ зарплаты
Предложенный диапазон ($246k - $339k) соответствует верхнему сегменту рынка для позиций уровня Principal в США, особенно в секторе FinTech и AI. Это конкурентоспособное предложение, превышающее средние показатели по стране.
Сопроводительное письмо
I am writing to express my strong interest in the Principal Software Engineer position at AlphaSense. With over 15 years of experience in building large-scale distributed systems and data platforms, I have a proven track record of designing robust architectures that bridge the gap between probabilistic AI outputs and deterministic reliability. My background in developing high-throughput data ingestion pipelines and my passion for reducing operational overhead through intelligent system design align perfectly with your mission to evolve the global financial data extraction platform.
In my previous roles, I have successfully led the transition from human-dependent validation to automated, confidence-scored systems, ensuring data provenance and traceability at every step. I am particularly drawn to AlphaSense’s challenge of normalizing heterogeneous financial sources like filings and transcripts. I am confident that my technical leadership and hands-on expertise in system hardening will contribute significantly to raising the technical bar and driving the long-term strategy of your Product & Engineering team.
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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 Principal 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:
- 15+ 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
$246,000—$339,000 USD
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
- Distributed Systems
- Data Pipelines
- AI/ML
- System Architecture
- Observability
- Cloud Native
- NLP
- Data Quality
- Infrastructure Engineering
Возможные вопросы на собеседовании
Проверка опыта проектирования сложных систем обработки данных.
Опишите архитектуру системы обработки данных, которую вы проектировали: как вы обеспечивали масштабируемость и отказоустойчивость при работе с разнородными источниками?
Ключевая задача роли — уменьшение зависимости от ручной проверки.
Как бы вы подошли к созданию системы оценки уверенности (confidence scoring) для ИИ-экстракции, чтобы минимизировать участие человека без потери качества?
Важно для обеспечения прозрачности финансовых данных.
Какие стратегии вы используете для обеспечения прослеживаемости данных (lineage) и объяснимости результатов в гибридных системах (AI + правила)?
Оценка лидерских качеств и умения влиять на техническую культуру.
Расскажите о случае, когда вам пришлось заблокировать неудачное архитектурное решение или настоять на редизайне критической системы. Как вы аргументировали свою позицию?
Проверка навыков устранения узких мест.
Как вы выявляете и устраняете архитектурные узкие места в системах, которые уже работают под высокой нагрузкой?
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- Страна
- США
- Зарплата
- 246 000 $ – 339 000 $