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NeptuneNE

Staff Python Software Engineer

Neptune.ai is an experiment tracking platform designed for teams training foundation models in artificial intelligence and machine learning, providing scalable tools to monitor, visualize, and manage complex model training processes.

Neptune

Employee count: 51-200

We are seeking an experienced Staff Python Software Engineer to join our fully remote team. In this role, you will be part of the squad responsible for developing and maintaining our Python client libraries-APIs for logging and querying model-building metadata. As a key contributor, you will shape their architecture, optimize performance, and ensure seamless integration with machine learning workflows. This role requires a deep understanding of Python, API design, and performance optimization to deliver a robust and scalable experience for our users.

Our tech stack (the bigger the overlap, the better):

  • Languages: Python.
  • Cloud platforms: Microsoft Azure, Google Cloud Platform (GCP).
  • Others: Protobufs, gRPC, Swagger.

Responsibilities:

  • Develop and Maintain the Python Client Library: Contribute to the design, development, and maintenance of our Python API for logging and querying model-building metadata.
  • Code Quality Reviews: Ensure high-quality, maintainable, and well-documented code through active development and rigorous code reviews.
  • Technical Leadership: Drive 1-2 key initiatives, working closely with 3-5 engineers to ensure smooth execution, clear task breakdowns, and effective communication of progress and challenges.
  • Architecture Performance Optimization: Design scalable and efficient solutions for API interactions, ensuring high performance and reliability in large-scale ML workflows.
  • Innovation Best Practices: Identify and propose enhancements to the Python client library and its surrounding ecosystem to improve user experience and performance.

You might be a fit if you have:

  • Current residence in Europe.
  • Minimum of 6 years of professional software development experience.
  • Strong proficiency in algorithmic thinking and problem-solving.
  • Passion for performance optimization, both on architecture component level.
  • Concise and logical communication, both written and verbal.
  • Ability to balance business goals with technical purity, especially when adapting to high-paced, shifting priorities.
  • Experience in user-friendly API design.
  • Experience in leading 3-5 people engineering teams.

We offer:

  • Flexibility: 100% remote work with offices (co-works) in Warsaw/Wrocław/Poznań/Kraków available and flexible working hours;
  • Share in our success: Participate in the Employee Stock Option Plan and be part of our growth journey;
  • Contract: B2B;
  • Time off: 20 paid service-free days per year;
  • Ownership and impact: Space to take action, bring your ideas to life, and make a real impact.

Any questions?

Check our ultimate guide for candidates to the neptune.ai Engineering team.

Don’t hesitate to contact our Talent Acquisition team, and check out our About us page to get to know the story and faces behind Neptune.

By applying, you consent for neptune.ai to process your personal data to assess your suitability for the role you have applied for in accordance with the General Data Protection Regulation (GDPR). Your personal data will remain confidential and shared only with authorized personnel involved in the recruitment process. You have the right to access, rectify, or delete your personal data at anytime.
With your optional consent, we can retain your data for up to 12 months after the application to consider you for future suitable roles if you’re not a match for the current position.

About the job

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Job type

Full Time

Experience level

Senior

Location requirements

Open to candidates from all countries.

Hiring timezones

Worldwide

About Neptune

Learn more about Neptune and their company culture.

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Neptune.ai is an experiment tracking platform specifically designed for teams engaged in training foundation models within the fields of artificial intelligence and machine learning. The company provides highly scalable tools that empower users to effectively monitor, visualize, and manage intricate and long-duration model training processes. Neptune.ai's platform is engineered to adeptly handle experiments that may span several months and involve multiple steps and branches, facilitating the efficient tracking of substantial data volumes and thousands of metrics.

Key functionalities of Neptune.ai include rapid filtering and searching capabilities for experiment data, swift visualization and comparison of diverse metrics, and the option for clients to deploy the platform on their proprietary infrastructure from the outset. This self-hosting capability is particularly beneficial for organizations undertaking advanced AI projects where large-scale experiment management is a critical requirement. Neptune.ai distinguishes itself through its dedicated focus on delivering scalability and speed tailored to high-volume, enterprise-level AI teams. The overarching goal of the platform is to assist organizations in achieving significant AI breakthroughs more rapidly, while concurrently optimizing resource utilization throughout the model development lifecycle. The platform supports a wide array of use cases, encompassing computer vision, Natural Language Processing (NLP), reinforcement learning, and time series analysis. Furthermore, Neptune.ai offers robust features such as various model deployment options, comprehensive security solutions, and seamless integrations with other widely-used ML tools. It serves as a centralized hub for all machine learning metadata, simplifying the process for engineers to locate and resolve issues.

Neptune.ai is utilized by a diverse range of AI-centric organizations, particularly those involved in the development and training of large-scale machine learning models. The company's mission is to enhance the efficiency of ML engineers by providing an intuitive means to track their work and foster collaboration, with the ultimate aim of establishing itself as the MLOps standard for ML teams globally. The platform is designed to be lightweight and technology-agnostic, enabling straightforward integration with any existing MLOps stacks. It allows for the logging of a wide variety of experiment metadata, including source code, Jupyter notebook snapshots, and Git information, offering users the flexibility to adapt the metadata structure to their specific needs.

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