AI/ML Engineer

Astera Labs•San Jose, CA

8d•$140,000 - $165,000•Onsite

About The Position

Astera Labs (NASDAQ: ALAB) provides rack-scale AI infrastructure through purpose-built connectivity solutions. By collaborating with hyperscalers and ecosystem partners, Astera Labs enables organizations to unlock the full potential of modern AI. Astera Labs’ Intelligent Connectivity Platform integrates CXL®, Ethernet, NVLink, PCIe®, and UALink™ semiconductor-based technologies with the company’s COSMOS software suite to unify diverse components into cohesive, flexible systems that deliver end-to-end scale-up, and scale-out connectivity. The company’s custom connectivity solutions business complements its standards-based portfolio, enabling customers to deploy tailored architectures to meet their unique infrastructure requirements. Discover more at www.asteralabs.com. AI/ML Engineer Location: San Jose, CA Experience: 1–5 years Team: Applied AI The role We’re hiring an AI/ML Engineer to build production AI systems for technical users. This is an applied engineering role for someone who can take modern model capabilities and turn them into reliable systems that people actually use. The core problems in this role are the same ones that matter in modern applied AI: getting the right context into the system, making tool use reliable, designing useful abstractions around skills and workflows, building evals that reflect real tasks, and iterating until the system is good enough to become part of a team’s daily workflow. In practice, you might work on coding agents in terminal and IDE environments, verification and debug assistants, log-analysis systems tied to real product diagnostics, documentation and spec-comparison agents, or internal assistants that operate over company knowledge and engineering data. You will be expected to think end-to-end: prompt and context design, retrieval quality, tool interfaces, evals, failure modes, deployment, and ongoing improvement. Why this role is interesting The team’s direction is very concrete: enterprise search, coding agents, workspace automation, customized skills, and agentic applications for specific engineering problems, all measured against real usage and outcomes. This role sits directly in that path. If you want to build applied AI systems that are ambitious but grounded in real workflows, technical users, and fast feedback loops, this is that job.

Requirements

1–5 years of experience in software engineering, applied AI, ML engineering, or related backend/platform roles.
Strong Python skills and strong production engineering fundamentals.
Hands-on experience building AI/LLM applications, agents, retrieval-backed systems, or workflow automation.
Comfort working with tool-using systems where correctness depends on context quality, tool integration, and careful failure handling.
Experience with AWS or GCP and the realities of deploying and debugging production AI services.
Good judgment around evals, failure modes, latency/cost tradeoffs, and safe rollout of non-deterministic systems.
Clear communication and the ability to turn ambiguous technical workflows into robust product behavior.

Nice To Haves

They have built more than demos. They have worked on systems where retrieval quality matters, where tool use can fail in subtle ways, where evaluation changes engineering decisions, and where product usefulness depends as much on system design as on model choice. They usually care about the details that separate a clever prototype from a dependable system.

Responsibilities

Build AI applications and agentic workflows for engineering productivity, diagnostics, search, documentation, and workflow automation.
Design systems that combine LLMs with retrieval, tool use, structured outputs, and evaluation loops.
Integrate models with internal tools, APIs, CLIs, MCP interfaces, and operational workflows so they can do useful work in real environments.
Improve system quality through eval design, prompt and context iteration, model selection, failure analysis, and human feedback.
Build reusable skills, workflows, and abstractions so useful capabilities can be shared across agents and teams instead of rebuilt from scratch.
Work closely with infrastructure and domain teams to deploy, monitor, and continuously improve AI systems in production.