Westbrook, CT · On-site +1
$55 - $60/hr
Hybrid in Westbrook, CT or Remote - EST Responsibilities: Your primary focus: Predicate & invariant ... Annotation data platform evolution. Extend a shipped canonical schema (Avro) and adapter layer that ...
Westbrook, CT · On-site +1
$55 - $60/hr
Hybrid in Westbrook, CT or Remote - EST Responsibilities: Your primary focus: Predicate & invariant ... Annotation data platform evolution. Extend a shipped canonical schema (Avro) and adapter layer that ...
$15.47 - $17.52
5% of jobs
$17.52 - $19.56
10% of jobs
$21.50 is the 25th percentile. Wages below this are outliers.
$19.56 - $21.60
11% of jobs
$21.60 - $23.65
2% of jobs
$23.65 - $25.69
17% of jobs
The median wage is $27.73 / hr.
$25.69 - $27.73
5% of jobs
$27.73 - $29.78
10% of jobs
$29.78 - $31.82
11% of jobs
$32.66 is the 75th percentile. Wages above this are outliers.
$31.82 - $33.87
12% of jobs
$33.87 - $35.91
11% of jobs
$35.91 - $37.95
7% of jobs
$15
$27
$37
A Remote Annotation job involves labeling or tagging data, such as images, text, audio, or video, to help train machine learning models. Annotators follow specific guidelines to ensure accuracy and consistency in the data. This work is typically done from home using specialized annotation tools provided by companies or platforms. It is commonly used in AI development, including natural language processing, computer vision, and autonomous systems.
To thrive in a Remote Annotation role, you need meticulous attention to detail, strong analytical skills, and the ability to quickly learn and apply specific data-labeling guidelines. Familiarity with annotation tools such as Labelbox, Supervisely, or CVAT and, in some cases, basic knowledge of machine learning concepts or relevant certifications are valuable. Excellent written communication, time management, and the capacity to work independently make a candidate stand out. These abilities ensure high-quality, consistent data labeling crucial for the success of AI and machine learning projects.
Remote annotation professionals often encounter challenges such as interpreting ambiguous data, maintaining consistency with guidelines, and managing repetitive tasks without direct supervision. Working remotely also means you need to stay self-motivated and disciplined while communicating clearly with project managers and team members through digital platforms. Adapting to updates in annotation protocols or tool changes can require flexibility and ongoing learning. However, overcoming these challenges can help you develop a highly sought-after skill set and pave the way for advancement into roles such as quality assurance or data analyst positions within the machine learning field.
For Remote Annotation jobs in New Haven, CT, the most frequently searched job titles are:
The top searched job categories for Remote Annotation jobs in New Haven, CT are:
Cities near New Haven, CT with the most Remote Annotation job openings:
Westbrook, CT • On-site, Remote
$55 - $60/hr
Full-time
Re-posted 5 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.
Sourced by ZipRecruiter
It services
51 - 200 Employees
Irvine, CA, US
1998