Staff Machine Learning Engineer

ForeFlight•Austin, TX

About The Position

Jeppesen ForeFlight builds industry-leading aviation software used by pilots, aircraft operators, and major airlines worldwide. As a high-growth, private equity-backed company, we are focused on scaling our operations, strengthening our financial infrastructure, and driving operational excellence across the business. Our team combines deep domain expertise with a collaborative, high-performance culture to solve complex challenges and support continued growth. Jeppesen ForeFlight is seeking a Senior Machine Learning Engineer to help build and scale domain-specialized automatic speech recognition (ASR) systems for aviation and operational audio workflows. This role focuses on developing vertical ASR models optimized for high-accuracy transcription in noisy, safety-critical environments, including aviation communications, cockpit interactions, operational dispatch, maintenance coordination, and related specialized audio domains. You will work across the full ML lifecycle — data engineering, model training, evaluation, deployment, and optimization — to deliver production-grade speech intelligence capabilities integrated into ForeFlight and broader aviation platforms. This position is ideal for someone with deep expertise in speech AI, acoustic modeling, large-scale transcription pipelines, and domain adaptation techniques for specialized vocabularies and constrained communication environments.

Requirements

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Electrical Engineering, Linguistics, or related field.
3+ years of experience in speech recognition, audio ML, or applied machine learning.
Strong experience training and fine-tuning ASR models using frameworks such as PyTorch or TensorFlow.
Experience with modern ASR architectures including: Transformer-based ASR, Conformer, RNN-T, CTC-based systems, Encoder-decoder speech models
Experience working with: Speech/audio preprocessing, Forced alignment, Language model adaptation, Beam search decoding, Noise robustness techniques
Familiarity with NVIDIA NeMo, Kaldi, ESPnet, Hugging Face, Whisper, DeepSpeed, or equivalent ecosystems.
Strong Python engineering skills and experience building production ML systems.
Experience with cloud infrastructure and ML deployment workflows (AWS, Kubernetes, Docker, CI/CD).
Ability to work with large audio datasets and distributed training environments.

Nice To Haves

Experience building ASR systems for aviation, air traffic control, public safety, defense, or other mission-critical domains.
Familiarity with VHF/UHF radio communications and noisy-channel audio processing.
Experience with multilingual or code-switching ASR systems.
Background in speech enhancement, keyword spotting, diarization, or speaker verification.
Knowledge of LLM-assisted transcription correction and retrieval-augmented speech systems.
Experience optimizing models for real-time streaming inference.
Active pilot experience or familiarity with aviation operations is a plus.

Responsibilities

Design, train, and optimize domain-specific ASR models for aviation and operational communications.
Develop verticalized speech models tuned for specialized terminology, accents, abbreviations, call signs, and noisy radio/audio conditions.
Build and maintain large-scale transcription and labeling pipelines for supervised and semi-supervised learning workflows.
Fine-tune foundation speech models (e.g., Whisper, wav2vec, Conformer, RNN-T, Citrinet, NeMo-based architectures) for aviation-specific use cases.
Improve transcription quality through language model adaptation, pronunciation lexicons, contextual biasing, and decoding optimization.
Develop evaluation frameworks and benchmarking methodologies using WER, CER, domain entity accuracy, latency, and robustness metrics.
Collaborate with product, avionics, data engineering, and platform teams to deploy scalable real-time and batch transcription systems.
Optimize inference pipelines for edge, cloud, and low-latency streaming environments.
Research emerging techniques in speech enhancement, diarization, speaker adaptation, multilingual ASR, and audio foundation models.
Ensure compliance with security, privacy, and operational reliability standards required in aviation environments.