Machine Learning Engineer (AI/ML)

Location

New York; Austin; Canada; Dallas; Detroit; Los Angeles; Phoenix; San Francisco

Employment Type

Full time

Location Type

Remote

Department

Engineering

Compensation

• Premium Locations (New York, Seattle, LA)$170K – $238K • Offers Equity
• US General$150K – $215K • Offers Equity

For roles based in the United States, the typical starting salary range for this position is listed on the job posting. In certain locations, such as the San Francisco Bay Area, Seattle, and the New York City Metro Area, a premium market range may apply, as listed.

These salary ranges reflect what we reasonably and in good faith believe to be the minimum and maximum pay for this role at the time of posting. The actual compensation may be higher or lower than the amounts listed, and the ranges may be subject to future adjustments.

An individual’s placement within the range will depend on various factors, including (but not limited to) education, qualifications, certifications, experience, skills, location, performance, and the needs of the business or organization.

If you have any questions or comments about compensation as a candidate, please get in touch with us at paytransparency [at] clickhouse [dot] com.

Recognized on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads.

The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla.

We’re on a mission to transform how companies use data. Come be a part of our journey!

About the team

The AI/ML Engineering team builds and operates ClickHouse’s AI and machine learning products end-to-end. This includes the Agentic Data Stack, AI Functions, chDB, the in-Console copilot, and the AI/ML partnerships that distribute them — together with the shared components and expertise that let every other ClickHouse team ship AI in the surfaces they own. Our team is looking for highly skilled and experienced software engineers to join us, who will be responsible for designing, building, and operating the products that make ClickHouse the platform of choice for agents and data scientists.

What you will do

• Feature Development: Design and implement AI-powered features across the full stack, from backend inference services to intuitive frontend interfaces within the ClickHouse Cloud platform.
• API Architecture: Create robust, scalable APIs that connect ClickHouse’s database capabilities with modern AI/ML inference systems and external/internal AI services.
• Ecosystem Integrations: Implement and maintain integrations with the broader AI/ML ecosystem and standards, ensuring that ClickHouse as a technology works seamlessly with popular frameworks and tools.
• Technical Integration: Integrate models into production systems with proper monitoring, versioning, observability, and evaluation.
• Oncall: Participate in the daytime oncall rotation along with the rest of the team.
• ML Engineering: Own the ML pipeline end to end (data, training, evaluation), and fine-tune or train custom models when off-the-shelf ones fall short. Track the state of the art closely enough to apply what genuinely improves our products.

What you will bring along

• 5+ years of software engineering experience in production environments
• Exposure to working directly with AI/ML technologies
• Experience integrating and deploying AI/ML models in production systems, including working with inference APIs and vector databases
• A high level of ownership and can drive features from concept to production with minimal supervision
• Ability to thrive in collaborative environments and can effectively communicate technical concepts to diverse stakeholders
• Applied ML experience in Python (Jupyter, PyTorch, common ML libraries), hands-on with compute clusters (Ray, Slurm), and designs trustworthy experiments to drive architectural decisions

Nice to have

• Fami…