Data Annotation Engineer Jobs in Maine (NOW HIRING)

• AI assistants have made writing code dramatically faster.


• They have not made writing correct code dramatically faster that gap is widening, and the tooling that closes it matters more than it used to.


• That's the work this role exists to do.


• A common thread runs through everything we build: a formal specification a schema, a contract, a grammar is the source of truth, and the tooling we write makes other code conform to that specification automatically.


• When the specification is the source of truth, code that doesn't match it fails loudly rather than silently whether that code was written by a human, generated by an AI assistant, or somewhere in between.

• 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 Machine Learning 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.

• Annotation data platform evolution.


• Extend a shipped canonical schema (Avro) and adapter layer that normalize Machine Learning 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.

• 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.

• A computer-science degree, or any particular number of years of experience.


• Prior data engineering or Machine Learning 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).

• 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.

The Company offers the following benefits for this position, subject to applicable eligibility requirements: medical insurance, dental insurance, vision insurance, 401(k) retirement plan, life insurance, long-term disability insurance, short-term disability insurance, paid parking/public transportation, paid time off, paid sick and safe time, hours of paid vacation time, weeks of paid parental leave, and paid holidays annually as applicable.