Product Engineer

Posted 7 Days Ago
Be an Early Applicant
28 Locations
Remote
63-63
Mid level
Machine Learning
The Role
Join Comet as a Product Engineer, building LLM observability tools by developing features, APIs, and collaborating with AI researchers.
Summary Generated by Built In
About Comet

Comet is building the development platform for teams who want to ship robust, reliable, and responsible AI applications. Opik, our open source LLM evaluation framework, has quickly become one of the most popular tools in the space. Our experiment management platform is used by data scientists at companies like Uber, Netflix, and Etsy. Tens of thousands of researchers, engineers, academics, and hobbyists use Comet every day to build the future of AI. 

Working at Comet will give you access to the most exciting work being done in all areas of machine learning. Some of the top researchers and companies working on self-driving cars, drug discovery, particle research, diffusion models, and LLMs use Comet every day. Your work has the potential to accelerate the development of some of the most impactful technology in the world, and you will be doing it alongside a team of passionate, caring individuals. If that sounds exciting, Comet is the right place for you.

Comet is backed by more than $63 million in venture capital funding and powers some of the best machine-learning teams in the world, including Netflix, Uber, Etsy, and Mobileye. We are a remote-first company with offices in New York City (USA) and Tel Aviv (Israel).

You are:

We are seeking a talented Product Engineer to join our team and help build the Opik product! Opik is an open-source platform designed to streamline the entire lifecycle of LLM applications, helping developers evaluate, test, monitor, and optimize their models and agentic systems.

Ready to shape the future of LLM development? Join us in building the next generation of AI observability tools!

Responsibilities:
  • Build & ship features at high pace in a fast-moving AI development environment
  • Develop and maintain comprehensive observability tools for LLM applications and RAG systems
  • Create intuitive user interfaces for LLM tracing, evaluation dashboards, and production monitoring
  • Build robust APIs and backend services to support real-time LLM application monitoring
  • Collaborate with AI researchers and ML engineers to implement cutting-edge evaluation methodologies
  • Contribute to both frontend (React/TypeScript) and backend (Python/Java) components
  • Design and implement automated evaluation systems and "LLM as a Judge" capabilities
  • Work on integrations with popular LLM frameworks and libraries
  • Participate in open-source community engagement and technical documentation
Requirements:
  • Experience: 4+ years of full-stack development experience, preferably in AI/ML domains
  • AI Development Tools: Hands-on experience with AI-assisted coding environments, IDEs, and agent-based workflows (e.g., Cursor, Windsurf, GitHub Copilot, Codex, Claude Code, and similar platforms)
  • Model Knowledge: Deep understanding of different AI model capabilities, limitations, and appropriate use cases (GPT-5, Claude, Gemini, etc.)
  • Frontend Development: Expertise in TypeScript, React, and modern web development practices
  • Backend Development: Strong proficiency in Python and/or Java with frameworks like Dropwizard
  • Database & Infrastructure: Knowledge of scalable database design and containerization
  • Observability Tools: Experience with tracing, monitoring, and evaluation systems
  • Collaboration: Strong communication skills for working in a distributed, global team environment
  • Problem Solving: Ability to work in ambiguous environments and solve complex technical challenges

Nice to have:

  • Open Source: Experience contributing to or maintaining open-source projects 
  • AI/ML Experience: Understanding of Large Language Models, RAG systems, and AI application architectures
Preferred Qualifications:
  • Experience with model evaluation, prompt engineering, and LLM optimization
  • Knowledge of distributed systems and high-throughput data processing
  • Familiarity with ML experiment tracking and model monitoring platforms
  • Experience with DevOps practices and CI/CD pipelines
  • Understanding of AI safety, model security, and responsible AI practices
What We Offer:
  • Competitive salary, based on proven experience, skills and location.
  • Competitive benefits package.
  • Flexible working hours and remote/Hybrid work options.
  • Opportunities for professional growth and development.
  • A collaborative and innovative work environment.
  • The chance to work with cutting-edge technologies and projects.
  • This role will be located in Tel Aviv, Israel (with hybrid flexibility) or in Europe (fully remote), working with a global team (large presence in the US, Tel Aviv and Europe), some flexibility with work hours is required to collaborate across time zones.
 

Comet is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees without regard to race, religion, color, sex, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship status, uniform service member status, marital status, pregnancy, age, medical condition, physical or mental disability, genetic information/characteristics, and any other characteristic protected by State or Federal law.

Top Skills

Containerization
Dropwizard
Github Copilot
Java
Python
React
Typescript
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The Company
HQ: New York, NY
87 Employees
Year Founded: 2017

What We Do

Comet is a meta machine learning platform designed to help AI practitioners and teams build reliable machine learning models for real-world applications by streamlining and connecting the machine learning model lifecycle. By leveraging Comet, users can employ machine learning experiment tracking to track, compare, explain and reproduce their models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.

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