Machine Learning Engineer

The Arena; United States; On-site; USD 100,000 - 130,000; Full time

Benefits offered by this job

Equity participation

Unlimited PTO

Parental leave

Paid holidays

Job summary

A leading advanced maritime technology company in the United States is looking for a Machine Learning Engineer. The role involves training and optimizing ML models for maritime sensing, with a focus on building cloud-based training pipelines and deploying models to both cloud and edge environments. Candidates should have over 3 years of experience, strong skills in deep learning with PyTorch, and a solid understanding of signal processing and cloud training. The position offers equity participation and unlimited PTO.

Qualifications

• 3+ years of ML engineering or applied ML experience.
• Hands-on experience with cloud training.
• Experience with acoustic data and signal processing.

Responsibilities

• Train and optimize deep learning models using PyTorch.
• Build cloud-based training pipelines.
• Deploy models to cloud and edge environments.

Skills

Machine learning

Deep learning

Cloud training (AWS/Azure/GCP)

Signal processing

GitHub Actions

Tools

PyTorch

Docker

Job description

Clearance: U.S. Citizen, eligible for Secret or TS/SCI

About the Company

Our client builds advanced maritime sensing and artificial intelligence systems used across national security missions. Their work blends machine learning, real-world sensor data, and high-performance edge computing to deliver tactical awareness in challenging ocean environments.

This isn’t research-only or “prototype” ML work.

Models here get trained, deployed, iterated, and evaluated against real acoustic data and real operational feedback.

If you want to build production ML systems that directly support mission outcomes—not academic benchmarks—you’ll thrive in this environment.

What You’ll Do

You’ll be a core ML engineer working across the full lifecycle of models used in maritime sensing:

• Train and optimize deep learning models using PyTorch
• Build cloud-based training pipelines (AWS/Azure/GCP)
• Convert acoustic waveforms into spectrograms and other signal-derived features
• Develop models for detection, classification, and target motion prediction
• Apply transformer architectures to real-world time-series and acoustic problems
• Deploy models to both cloud and edge environments
• Build MLOps workflows using GitHub Actions, Docker, and modern tooling
• Collaborate with domain experts on ML architecture, preprocessing, and evaluation

This is the kind of role where your models ship, get used, and immediately inform what you build next.

What We’re Looking For

• U.S. Citizenship (clearance required)
• 3+ years ML engineering or applied ML experience
• Hands-on cloud training experience (non-negotiable)
• Experience with time-series, sensor, or acoustic data
• Ability to build training, inference, and data pipelines
• Familiarity with CI/CD (GitHub Actions preferred)
• Signal processing: STFT, spectrograms, filtering
• Acoustic / SONAR or other remote sensing experience
• Distributed training (multi-GPU or multi-node)
• Equity participation
• Unlimited PTO, parental leave, paid holidays