Computer Scientist; preferably Level 2

Veracity Engineering is an industry leader in aviation engineering and management consulting. We apply proven principles and innovative approaches to solve tomorrow’s problems today. Since 2001, Veracity’s staff of experienced, passionate and creative professionals has provided our government and commercial clients with innovative solutions to modernize air traffic systems and advance global initiatives in safety, efficiency, and capacity.

Location:

Atlantic City, New Jersey

Salary Range: $82,000-$118,000 per year

Remote Work:
Remote work may be permitted when consistent with project needs. On-site presence will be required as dictated by FAA project needs.

• End-to-end neural speech-recognition systems using wav2vec 2.0, Whisper, and Deep Speech 2-style acoustic models.
• Sequence-to-sequence and speech models for multilingual and domain-specific applications.
• Experience with curating real and synthetic datasets for training, validation, and model comparison.
• Building datasets for repeatable benchmarking and regression testing.
• Designing benchmark suites measuring model accuracy, inference latency, package size, and release readiness.
• Designing hybrid recognition architectures that combine local processing with remote API calls.
• Applying language models and NLP techniques to correct likely recognition errors and improve contextual interpretation.
• Building scalable ETL and data-enrichment pipelines using, for example, Python, PySpark, Rust, Go, and FastAPI.
• Developing production ML systems using PyTorch, Tensor Flow, Keras, MLflow, ZenML, and related tools.
• Implementing artifact validation, deployment controls, and AI governance for production model releases.
• Applying anomaly detection, statistical modeling, and error analysis to identify operationally significant model failures.
• Rapid AI prototyping and translating research models into reliable, production-grade software pipelines.

• Design, implement, and maintain a centralized repository for CASPER training data, validation data, transcriptions, command labels, model outputs, and evaluation results.
• Provide full-stack, Dev Ops-oriented development across the training and validation environment, including front-end data-management interfaces, back-end services and APIs, databases, processing infrastructure, automated builds, deployment, monitoring, and operational support.
• Develop an aviation-specific model harness for LLM API integration.
• Develop and train custom language models for simulated pilot speech and behaviors.
• Build scalable ETL, labeling, and data-enrichment pipelines that map recorded and synthetic speech to CASPER commands, callsigns, fixes, digits, and other recognition targets.
• Maintain dataset, label, model, and command-schema versioning so training and evaluation data remain aligned as CASPER capabilities and phraseology mappings change.
• Curate representative training and held-out validation datasets covering different speakers, phraseology, audio conditions, callsign types, commands, and operational contexts.
• Automate end-to-end model training, replay, benchmarking, validation, and nightly or per-build regression testing of the CASPER recognizer and parser.
• Measure and report word error rate, callsign accuracy, digit accuracy, command and intent accuracy, precision and recall, latency, regressions, and release readiness.
• Use exploratory, hypothesis-driven, and experiment-based methods to identify, prototype, and evaluate alternative approaches for improving voice-recognition performance.
• Design controlled experiments and ablation studies comparing acoustic models, language models, contextual sources, preprocessing methods, confidence thresholds, and…