Senior Python Engineer - ML and Data Science

This position is posted by Jobgether on behalf of ClickHouse. We are currently looking for a Senior Python Engineer – ML and Data Science in the United States.

This role sits at the crossroads of data science and backend engineering, offering a unique opportunity to shape the experience of thousands of machine learning engineers and data scientists. You will be instrumental in expanding the capabilities of the Python ecosystem, optimizing performance for real-time analytics, and contributing to widely used open-source projects. Collaborating with product, engineering, and external communities, you’ll work to bridge the gap between analytical databases and modern data science workflows—all in a highly distributed and collaborative environment.

Accountabilities:

• Design and implement Python features that streamline data ingestion, transformation, and exploration for analytics and ML teams.
• Enhance existing integrations with popular data science libraries, improving the interoperability between Python and modern analytical databases.
• Contribute actively to open-source repositories, ensuring compatibility with leading data science and machine learning frameworks.
• Optimize performance for querying and data handling in Python-driven environments.
• Work closely with cross-functional teams (engineering, product, community) to gather feedback and improve developer and data science user experience.
• Develop reference architectures and examples to guide best practices in data workflows and ML integration.

• Expert-level proficiency in Python and familiarity with key libraries such as Pandas, Polars, Scikit-learn, or PyTorch.
• Hands-on experience integrating databases or analytical engines (e.g., Snowflake, BigQuery, DuckDB) into ML or analytics pipelines.
• Solid understanding of distributed systems and OLAP architecture.
• Proven ability to work in open-source environments and contribute to public codebases.
• Strong collaboration skills with experience in cross-functional teams and user feedback loops.
• Excellent written and verbal communication skills, with the ability to translate complex technical ideas to diverse audiences.
  Nice to Have:
• Prior exposure to ClickHouse or similar high-performance analytical databases.
• Knowledge of ML workloads and how they intersect with large-scale data processing.
• Experience working in developer-first or database infrastructure companies.

• Competitive compensation based on location:
• New York / San Francisco: $139,000—$209,500 USD
• Los Angeles / Washington, DC: $125,000—$188,550 USD
• US Remote: $118,000—$178,000 USD
• Stock options as part of every offer.
• Flexible remote work environment; globally distributed team across 20+ countries.
• Employer contributions toward healthcare.
• Generous time off policies (flexible in the US, generous elsewhere).
• $500 home office stipend for remote team members.
• Global in-person gatherings and offsites.
• Be part of a growing core team that’s shaping the company’s early culture and engineering standards.

Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.

When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.
🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
📊 It compares your profile to the job’s core requirements and past success factors to determine your match score.
🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.
🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.

The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role.
Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.

Thank you for your interest!

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