AI TEVV Engineer

Ursa Space Systems•Ithaca, NY

7d•$180,000 - $220,000•Remote

About The Position

Ursa Space is looking for an AI TEVV Engineer to define how we prove our AI-native geospatial platform works and to whom. This is an evaluation-science role at its core, not a test-automation role. The central skill is measurement under uncertainty: where classical QA asks "does this function return the correct output" (a deterministic pass/fail question), AI evaluation asks "what is the error rate, on what distribution of inputs, under what operating conditions, and is that rate acceptable for this mission" (a measurement question with confidence intervals). That work is closer to experimental design and psychometrics than to writing test suites. You will own the strategy, methodology, and evidence that let us and our customers' risk officers trust the platform's outputs and demonstrate where, and how well, they hold. This position reports to the Director of System Requirements, with a functional reporting line and charter that preserve evaluation independence from the teams whose outputs are under test. This position is fully remote and exempt.

Requirements

B.S. in Computer Science, Statistics, or Systems Engineering, or a related quantitative discipline (M.S./Ph.D. a plus)
10+ years of relevant experience, centered on evaluation, measurement, or test-and-evaluation of AI/ML or data-driven systems — not solely software QA or test automation
Demonstrated ability to design statistically defensible evaluations: input-distribution design, error-rate estimation, confidence intervals, and context-tied acceptance criteria
Hands-on experience building ground-truth/golden datasets — adjudication protocols, inter-rater reliability, and reference-data uncertainty
Experience supporting U.S. government contracts (aerospace, defense, or intelligence preferred), including requirements traceability and compliance documentation
Working knowledge of the NIST AI RMF and how TEVV evidence maps to customer compliance regimes
Strong quantitative skills and Python proficiency for analysis and evaluation (Pandas/Polars, NumPy, ML evaluation libraries)
Comfort using AI-assisted tools for rapid development and testing
Organized and self motivated, able to work successfully with a remote team
A creative, flexible mindset for complex problems
A fast, reliable internet connection if working remotely

Nice To Haves

Aligning evaluation methodology to the NIST AI RMF, ISO/IEC 42001, and emerging NIST AI TEVV consortium / ISO/IEC SC 42 work; standards participation a plus
Defining credibility-assessment frameworks tied to context of use rather than fixed, context-free thresholds
Evaluating image and signal processing outputs across modalities (SAR, electro-optical, RF), including the proxy nature of geospatial reference data
Evaluating generative and agentic systems: rubric design, human-adjudicated evaluation, and claim-level groundedness
Heritage V&V/assurance standards (IEEE 1012, DO-178C, ISO/IEC 25010, CMMI) and formal IV&V experience
GIS tools and libraries; SpatioTemporal Asset Catalog (STAC) experience
NoSQL and/or SQL databases (Mongo, MySQL, Postgres)
Test automation and CI/CD: Python and/or JavaScript, frameworks (e.g., pytest, Jest), and regression/monitoring suites in pipelines
Common AWS services (e.g. S3, Lambda, ECS, ECR, DynamoDB) and microservice-based architectures
Software tooling (e.g. Git, Docker, Anaconda, virtual environments)
Data quality, observability, and monitoring tooling
Experience with customer-facing software products

Responsibilities

Design, develop, and plan the TEVV strategy across the platform's algorithms, AI/ML models, agentic workflows, data pipelines, and analytic products, aligned to the NIST AI RMF Measure function
Design statistically defensible evaluations: error metrics and acceptance criteria on representative input distributions, with explicit confidence intervals
Define and document the platform's context of use — the validated operating envelope (modalities, geographies, resolutions, conditions, target classes) within which accuracy claims hold
Evaluate ground-truth and "golden" datasets, including annotation and adjudication protocols, inter-rater reliability, and quantified uncertainty in the reference data itself
Implement a layered evaluation posture: a verifiable core (accuracy, groundedness, format), a rubric-scored middle layer with documented inter-rater reliability, and an honest residual of expert holistic review
Evaluate generative and natural-language outputs for claim-level groundedness whether each assertion is traceable to a citable source alongside rubric-based, human-adjudicated assessment
Stand up continuous monitoring and re-validation certification gates plus ongoing surveillance watching for model, prompt, retrieval, and agent-behavior drift
Author and maintain the TEVV evidence set: test plans, traceability matrices, metrics, acceptance criteria, and credibility-assessment documentation
Support DoD AI test-and-evaluation expectations (including DoD Directive 3000.09), contractual milestones, acceptance testing, and demonstrations to government stakeholders.
Distinguish internal TEVV from organizationally independent IV&V, and partner with external IV&V agents where required
Partner with Engineering teams to embed evaluability, observability, and traceability from design onward.
Contribute to emerging standards (NIST AI TEVV consortium, ISO/IEC SC 42 / 42001), aligning our methodology so evidence packages map to customers' compliance frameworks.
Perform all other duties as assigned.