Staff ML Engineer, Agent Training & Environments

EngineersOfAI; San Francisco (CA); On-site; USD 170,000 - 260,000; Full time

Job summary

Labelbox is shaping the data infrastructure for frontier AI models, building environments, evaluators, and pipelines that enable scalable experimentation and rapid iteration. This role blends platform engineering with agent-focused post-training expertise, requiring engineers who can ship reliable systems at scale and drive architectural direction.

You will work on RL environments, verifiers, and fine-tuning pipelines, contributing to the throughput and quality of AI training workflows in a

Qualifications

• 3+ years shipping reliable systems used by customers.
• Strong system and API design judgment; able to make hard architecture calls.
• Experience shipping production code with coding agents daily.

Responsibilities

• Develop RL environments and tool surfaces for agentic tasks.
• Build verifiers, graders, and evaluation pipelines to measure progress.
• Create scalable training and serving infrastructures for research workloads.
• Enable large-scale experimentation with reliable fault tolerance and cost accounting.
• Collaborate with teams to drive architecture decisions and roadmap.

Skills

System design

API design

High throughput

Production code

Platform engineering

Job description

Shape the Future of AI

At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.

About Labelbox

We're the only company offering three integrated solutions for frontier AI development:

1. Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale
2. Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models
3. Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling

Why Join Us

• High-Impact Environment: We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions.
• Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.
• Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.
• Continuous Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.
• Clear Ownership: You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.

Role Overview

Labelbox is the RL data factory for advancing frontier agent capabilities. We build the data, environments, and evaluations that frontier labs use to train and judge.

This role sits where training meets infrastructure. You will run the experiments and build the systems that run them: environments agents act in, verifiers that decide whether they succeeded, and the fine-tuning pipelines that turn that signal into a better model. We're looking for someone who does both halves — the engineering throughput of a strong platform engineer, and real depth in post-training agents.

The bar is high: engineers with strong judgment who set technical direction, turn prototypes into reliable systems fast, and are at the frontier of agent-first engineering practice.

What you'll work on

• RL environments for agentic tasks: task definitions, tool surfaces, state and reset semantics, reward design — and the harness that runs thousands of them in parallel.
• Verifiers and graders: programmatic checks, LLM judges, rubric pipelines, pass@k scoring. Deciding what "the agent succeeded" means, and making that judgment trustworthy at scale.
• Fine-tuning pipelines that turn evaluation signals into measurable agent improvements — SFT and RL, from data collection through training to checkpoint evaluation.
• Eval systems that run millions of agent trajectories to measure model and product quality.
• Training and serving infrastructure that scales to the throughput frontier labs need: multi-launcher orchestration, long-running job fault tolerance, cost accounting.

What we're looking for

As an engineer

• A 3+ year track record of shipping systems that customers and other engineers still rely on.
• Exceptional throughput, without the quality tax. You ship a lot, you review a lot, and the v1 you ship becomes the foundation the rest of the team builds on.
• Strong system and API design judgment. Hard architecture calls land with you: you make them, defend them under pressure, and update fast when someone else is right.
• You ship production code with coding agents daily. You know where they break and wh