AI Work Central

Scala Engineer

SME CareersSTEMFirst seen 9 Oct 2026

up to$70/hr as listed by SME Careers, USDEstimate a week at this rate
Apply on SME Careers

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Where
17 countries

United States, Germany, Brazil, India, Bangladesh, Bhutan, Indonesia, Cambodia, Sri Lanka, Malaysia, Nepal, Philippines, Pakistan, Singapore, Thailand, Timor Leste, Vietnam

Language
English
Type
Contract, remote
Field
STEM

Posted on SME Careers First seen 9 Oct 2026Checked 12 Oct 2026

From the SME Careers listing

As a Scala Engineer, you will work remotely on an hourly paid basis to review AI-generated Scala code, system designs, and technical explanations, as well as produce high-quality reference implementations and step-by-step reasoning for complex engineering problems. You will assess solutions for correctness, clarity, safety, performance, and adherence to the prompt; identify errors in concurrency, distributed systems behavior, JVM performance, functional correctness, and resource handling; fact-check technical information; write high-quality explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on correctness and reasoning quality.

This fully remote, hourly paid contractor role is with SME Careers, a fast-growing AI data services company and subsidiary of SuperAnnotate that provides AI training data to many of the world’s largest AI companies and foundation model labs, where your Scala and systems expertise will directly help improve the world’s premier AI models used by millions of developers worldwide.

Important:

There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Responsibilities

  • Develop AI Training Content: Create detailed prompts across Scala/JVM topics and write high-quality reference answers to guide AI learning.
  • Optimize AI Performance: Evaluate and rank AI responses to improve correctness, safety, performance, and contextual relevance.
  • Ensure Model Integrity: Test AI outputs for inaccuracies, unsafe patterns, and bias; validate reliability across real-world use cases.

Requirements

  • Bachelor’s degree or higher in Computer Science, Software Engineering, or a closely related technical field (or equivalent practical experience).
  • 4+ years of professional experience building production systems using Scala (backend services, data platforms, or distributed systems).
  • Expert-level proficiency in Scala 2.13+ and/or Scala 3, including strong understanding of the type system, implicits/givens, collections, and functional patterns.
  • Strong JVM fundamentals: memory model, garbage collection, profiling, performance tuning, and troubleshooting production issues.
  • Deep experience with concurrency and effect systems (e.g., cats-effect, ZIO) and/or actor-based systems (Akka/Pekko).
  • Experience designing and operating services in distributed environments: microservices, messaging/streaming, retries, idempotency, backpressure, consistency tradeoffs.
  • Strong testing practices (ScalaTest, MUnit, property-based testing like ScalaCheck) and comfort with CI/CD workflows.
  • Comfortable with build tooling and dependency management (sbt, Maven/Gradle interop), and version control best practices.
  • Minimum C1 English proficiency (written and spoken), with the ability to write clear technical explanations and follow detailed English-language guidelines.
  • Prior experience with AI data training, annotation, or evaluating AI-generated technical content is a strong plus.
  • Highly detail-oriented and systematic, with a methodical approach to evaluating reasoning quality and identifying subtle issues in Scala/JVM systems.

Text above is the platform's own listing, shown as published. Check the details on SME Careers before applying.

Prepare for the assessment

Practice questions and what each platform says about its screening for this kind of role:

Terms in this listing

  • Prompt: The input given to a model: a question, an instruction or a conversation so far.
  • Relevance: How well a result or answer matches what the user was looking for.
  • Guidelines: The project's written instructions that define how to do a task and how to judge answers. On most projects they take precedence over personal preference.
  • Annotation: Adding labels or notes to data such as text, images, audio or video so a model can learn from it.

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