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About This Role
AI TEVV Engineer (Test, Evaluation, Verification \& Validation)
About Ursa Space Systems
Ursa Space Systems is building an AI\-native geospatial insights platform that guides the acquisition, analysis, and integration of satellite and geospatial data into customer workflows, giving decision makers an edge. Leveraging hundreds of data sources, AI agents, and proprietary analytics, Ursa Space provides fast, actionable information to a range of industries, including finance, energy, and defense. Our customers receive contextual, comprehensive reporting that goes beyond surface\-level observations.
Job Summary
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.
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.
- 30% travel.
- Perform all other duties as assigned.
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
Preferred Skills
- 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
Compensation
- Ranges: $180,000 \- $220,000
- Compensation range includes base salary and is eligible for an annual bonus.
- New hires salaries are typically between the range minimum and the salary range midpoint. Actual placement in the range will depend on a candidate’s job\-related skills, experience, and expertise, as evaluated during the interview process.
Inclusion Statement
We are dedicated to the belief that all lives have equal value. We strive for a global and cultural workplace that supports ever greater diversity, equity, and inclusion — of voices, ideas, and approaches — and we support this diversity through all our employment practices.
All applicants and employees who are drawn to serve our mission will enjoy equality of opportunity and fair treatment without regard to race, color, age, religion, pregnancy, sex, sexual orientation, disability, gender identity, gender expression, national origin, genetic information, veteran status, marital status, and prior protected activity.
Location
- We are headquartered in Ithaca, NY and have a remote workforce in other locations throughout the United States.
Please note: applications without a relevant cover letter will not be considered. In your cover letter, we would like to hear your personal voice and learn about your sincere interest in Ursa Space Systems.
Benefits and Perks
- Competitive Compensation
- Discretionary PTO \& Flexible Scheduling
- Stock Options
- 401(k) Match
- Medical, Dental and Vision Coverage for you and your dependents
- FSA \& HSA Plans
- Employer\-paid Life Insurance
- Employer\-paid LTD and STD for Parental and Family Care
- 11 Paid Holidays
- Employee Resource Groups
- Educational Assistance Program
- Professional Development Opportunities
- And more…
Company Values
- Use the team
- Figure it out and own it
- Aim for elegant simplicity
- Empower diversity \& inclusivity
- Do the right thing
- Be scrappy
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Salary Context
This $180K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ursa Space Systems, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($200K) sits 7% below the category median. Disclosed range: $180K to $220K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Ursa Space Systems AI Hiring
Ursa Space Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Ithaca, NY, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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