Westbrook, CT · On-site +1
$55 - $60/hr
... Remote - EST Responsibilities: Your primary focus: Predicate & invariant framework for data ... labeling platforms into a shared representation. Add adapters for new platforms, evolve the schema ...
Westbrook, CT · On-site +1
$55 - $60/hr
... Remote - EST Responsibilities: Your primary focus: Predicate & invariant framework for data ... labeling platforms into a shared representation. Add adapters for new platforms, evolve the schema ...
Remote data labelers often face challenges such as maintaining focus during repetitive tasks, managing volume-based workloads, and interpreting ambiguous data with consistency. To manage these, it's important to set up a distraction-free workspace, take regular breaks to avoid fatigue, and seek clarification from supervisors or project guidelines when uncertainties arise. Most companies provide onboarding and ongoing support to help new labelers understand annotation standards and best practices. Collaborating with remote team members via chat or project management platforms also helps maintain quality and stay connected. By being proactive and utilizing available resources, remote data labelers can maintain high accuracy and productivity.
To thrive as a Remote Data Labeling specialist, you need strong attention to detail, basic data analysis skills, and the ability to accurately tag and categorize diverse data types, often with a high school diploma or equivalent. Familiarity with data labeling platforms, annotation tools (such as Labelbox or Amazon SageMaker Ground Truth), and, occasionally, basic knowledge of data privacy standards is helpful. Time management, self-discipline, and effective remote communication are valuable soft skills in this position. These skills ensure that labeled data is accurate and reliable, supporting the success of machine learning and AI projects.
A Remote Data Labeling job involves annotating or categorizing data, such as images, text, audio, or video, to train machine learning models. Workers review and tag content based on specific guidelines provided by companies. This job is typically done online from home and requires attention to detail, consistency, and sometimes specialized domain knowledge. It plays a crucial role in improving artificial intelligence systems by providing high-quality labeled data.
$55 - $60/hr
Other
Posted 15 days ago
Job Description Location: Hybrid in Westbrook, CT or Remote - EST Job Description: Responsibilities: Your primary focus: Predicate & invariant framework for data contracts - the core of the role. Design and implement declarative contract classes that attach to Python methods (design-by-contract decorators - no relation to the ML data annotations below) and trigger verification of the code inside, using AST-level analysis. Predicates enforce data contracts: they state what a method must guarantee about the data it produces or consumes, and the verifier checks the implementation against those statements.
Invariants constrain evolution: they state properties of the codebase that must survive change, so that modifications - human- or AI-authored - that would break them fail at verification time, not in production. You'll shape the vocabulary of predicates and invariants together with the architect, build the verifier and its diagnostics, and make violation messages clear enough that they teach the contract they enforce. Your secondary focus: Annotation data platform evolution.
Extend a shipped canonical schema (Avro) and adapter layer that normalize ML annotation data from multiple commercial labeling platforms into a shared representation. Add adapters for new platforms, evolve the schema under a versioned spec and ADR process, and keep validation utilities and Python typing overlays in sync with the schema. Design and implement the predicate/invariant framework: contract classes, the AST-based verifier, and CI integration.
Turn abstract contract concepts into APIs and diagnostics that working engineers adopt willingly - making the ideas graspable is part of the job, not an afterthought. Extend and evolve schemas, adapters, and validation layers for the annotation platform under its established change process. Investigate verification and validation failures and determine whether the fix belongs in the contract, the code, or the source system, documenting your reasoning.
Document the framework thoroughly and transfer knowledge continuously - by the end of the engagement, the team must be able to own and extend it without you. Work closely with a senior architect on initial designs, then independently own implementation in your areas. Qualifications: We're flexible on background, but you should be able to demonstrate: Comfort with formal and abstract structures - logic, type systems, program analysis, algebraic thinking - demonstrated by working software you built from them.
Vision and execution together; neither alone is enough. Deep production Python: decorators, descriptors, metaclasses, type hints, and the standard library. Strong analytical reasoning: comfort working from ambiguous or underspecified ideas and finding structure.
Ability to communicate technical ideas clearly in writing (design docs, code reviews, documentation, async messaging). Independence in scoping and delivering work, with the judgment to escalate complex design questions. Bonus Qualifications: A computer-science degree, or any particular number of years of experience.
Prior data engineering or ML experience (the role is adjacent to ML, not part of model training). Experience with our exact stack (Avro, Databricks, Spark, dbt, etc. can be learned on the job).
Experience in any of these areas is a genuine plus: Contracts and verification Design-by-contract tooling (icontract, deal, Eiffel, JML, Dafny) or other program-verification exposure. Property-based testing (Hypothesis or similar). Code-as-data work Parsing or analyzing source code (Python ast / libcst, tree-sitter, or equivalents); codemods; mypy plugins or typing internals.
Code generation, templating, or compiler back-ends - especially if you've maintained a code generator in production. Rule and constraint systems DSLs, OPA/Rego, rule engines, or knowledge-representation/constraint languages (OWL, RDF, SHACL, Datalog). Translating declarative business rules into executable validation logic.
Schema and validation tooling Avro, JSON Schema, OpenAPI/Swagger, LinkML, CUE, or similar; Pydantic, Marshmallow, or attrs with validators. What success looks like: In your first 30 days, you'll internalize the contract model and the platform's spec/ADR process, and ship a first working predicate end-to-end - decorator, verification, diagnostics. By 90 days, the framework core will be enforcing real data contracts in CI on at least one system, and teammates will be writing predicates without your help.
By end of term, the framework will be documented, adopted, and owned by the team; invariants will be guarding codebase evolution; and the extension conversation will be about what to build next, not whether it worked.
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It services
51 - 200 Employees
Irvine, CA, US
1998