The opportunity
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be…
What you'll do
+ years of software engineering experience with strong fundamentals in: distributed systems, system design, data structures, and algorithms.
Strong Python skills and a track record of shipping production software;: comfort in at least one other part of the stack (TypeScript/React, Go, Rust, or similar).
Deep experience with containerization and sandboxed execution, including: Docker, VMs, gVisor/Firecracker, Kubernetes, or equivalent.
Experience building or operating high-throughput backend systems: orchestration, job scheduling, queuing, and large-scale data pipelines.
Hands-on experience building with LLMs including agent loops, tool calling,: MCP, or eval harnesses, and enough intuition about model behavior to reason about what a training signal actually teaches.
Demonstrated ability to own ambiguous, undefined problems end to end and drive them to a shipped system.
What they're looking for
- Excellent written and verbal communication; ability to align engineers,: researchers, and non-engineering partners on a technical direction.
- Direct experience building RL environments, agentic benchmarks, or eval: harnesses (SWE-bench-style task suites, terminal or browser environments, tool-use benchmarks, or in-house equivalents).
- Familiarity with post-training methods: RLHF, RLAIF, RLVR, GRPO/PPO-family algorithms, rejection sampling, reward modeling, and the practical failure modes of each.
- Experience designing verifiable reward signals, and firsthand experience with: reward hacking and how to defend against it.
