Software Engineer, ML Infrastructure

About the Job

Innodata's Frontier AI teams ship at high speed, but the systems underneath have to be rock-solid. We're hiring software engineers to build and own the infrastructure layer that powers our training and evaluation work—the distributed data pipelines, orchestration, and production services that the rest of the team depends on.

You will own meaningful pieces of our core platform: the pipelines that process training and evaluation data at scale, the services that run evaluations reliably, the tooling that makes fast iteration safe. You'll work alongside senior engineers, researchers, and partners who care deeply about craft and quality. This role is well-suited to a mid-level engineer with a few years of production experience who is ready to take ownership of meaningful components and grow into a senior role on a high-output team.

What You'll Do

• Design and implement distributed data pipelines that process training and evaluation data at scale, with correctness and reliability guarantees.
• Build the orchestration and infrastructure layer that researchers and AI engineers build on top of.
• Make production systems fast, observable, and reliable—concurrency, throughput, failure modes, and performance are first-class concerns from day one.
• Write clean, well-tested code that holds up under real production load and real customer deliverables.
• Use coding agents and AI-assisted development daily as a force multiplier on your output.
• Pair with senior engineers on hard problems, take ownership of well-scoped components, and grow into bigger ones.
• Debug across unfamiliar codebases and isolate root causes quickly.
• Contribute to design discussions, code reviews, and the engineering culture of the team as you grow.

What You'll Bring

• BSc or MSc or PhD in Computer Science, Software Engineering, or a related technical field.
• 3+ years of professional software engineering experience shipping production code.
• Strong coding ability with concrete evidence: production systems you've built, open-source contributions, competitive programming results, or significant personal projects.
• Mastery of Python/JAX and least one of the following languages: C++, Rust, or Go is required.
• Familiarity with other languages is a plus
• Hands-on experience with distributed systems, data pipelines, or backend services at scale.
• Solid computer science fundamentals: algorithms, data structures, concurrency, distributed systems.
• Comfort with modern infrastructure tooling: Docker, cloud services, CI/CD, observability.
• Fluency with coding agents (Claude Code, Cursor, etc.)—you use them as a daily tool, not as a novelty.
• Strong debugging intuition and a high-quality bar—you ship code you're proud of.
• Excellent English communication.

Bonus Points

• 5+ years of production engineering experience at engineering-driven companies.
• Experience with ML infrastructure specifically (training pipelines, model serving, experiment tracking).
• GPU programming or performance optimization work.
• Cloud infrastructure expertise (AWS, GCP).
• Open-source contributions to infrastructure or data tooling projects.