Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
Agentic Systems @ 1
Experimentation
LLM
Machine Learning @ 6
Python @ 6
Sentry
TypeScript @ 6
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Sentry helps developers fix errors and performance issues before users notice. Trusted by more than 200,000 organizations, Sentry is building its AI-native future.
As a Senior Software Engineer on Sentry's AI/ML team, you will build evaluation infrastructure that measures the accuracy, reliability, and real-world performance of AI systems. You will design datasets, benchmarks, and test harnesses that turn ambiguous AI behavior into measurable signals, helping the team ship AI with confidence.
Responsibilities
- Design and build robust evaluation frameworks to measure accuracy, reliability, regressions, and edge cases in AI systems.
- Create and curate high-quality datasets, golden test cases, and benchmarks grounded in real production data.
- Build automated test harnesses and metrics pipelines to continuously evaluate models, prompts, and agentic workflows.
- Partner with applied AI engineers and product leaders to define what “good” looks like and translate it into measurable criteria.
- Own the evaluation lifecycle for major AI initiatives, from early experimentation through production monitoring.
- Help influence model design through improved evaluation and measurement.
Requirements
- At least 5 years of professional experience and a bachelor's degree in computer science, machine learning, or a related field.
- Experience building testing, evaluation, or data infrastructure for complex systems; AI/ML experience is strongly preferred.
- Ability to write production-quality code in Python and TypeScript.
- Experience working with structured and unstructured datasets, labeling workflows, or data quality pipelines.
- Familiarity with modern ML systems and evaluation techniques, including offline metrics, online evaluation, and regression testing for models or prompts.
- Experience evaluating LLMs, agentic systems, or AI-assisted developer tools is a bonus.
- Strong interest in correctness, rigor, and measurement in AI systems.
- Ability to turn ambiguous product goals and model behavior into concrete tests and metrics.
- Ability to work effectively in cross-functional environments.
Compensation and Benefits
- Base salary: $240,000–$280,000 USD per year.
- Eligible candidates may participate in employee benefit plans and programs, including incentive compensation, equity grants, paid time off, and group health insurance coverage.
Equal Opportunity
Sentry is committed to providing equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other legally protected characteristic. The company provides reasonable accommodations to employees and candidates with disabilities and strives to build a diverse, inclusive culture.
Sentry's applicant privacy policy provides additional information about how applicant data is handled.