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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Spear AI - **Location:** Washington, DC, United States (Remote available) - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Artificial Neural Networks, Microsoft Azure, Batch Processing, Cloud Computing, Code Coverage, Software Code Optimization, Continuous Delivery, Continuous Integration, Distributed Computing Environment, Github, Monitoring of Systems, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Tensorflow, Signal Processing, SONAR (Symantec), Management of Software Versions, Reinforcement Learning, Data Processing, Scripting, Graphics Processing Unit (GPU), Google Cloud, Cloud Platform System, Pytorch, Discretization, Low Latency, Deployment Automation, Data Management, Machine Learning Operations, Restful APIs, Software Version Control, Docker - **Published:** September 30, 2026 - **Apply:** https://www.careerbuilder.com/job-details/machine-learning-engineer-washington-dc--a20f7ad1-9e1b-48a5-ba06-bad071dae680 ## About the Role * Several years of experience with Python and machine learning frameworks * Expertise in PyTorch for building and training neural networks * Experience training and serving models in cloud environments (AWS, Azure, GCP) * Proficiency with MLOps practices including experiment tracking, model versioning, and deployment * Experience with model optimization for production performance and scale * Knowledge of Docker and Kubernetes for containerized deployments * Familiarity with REST APIs and model serving frameworks * Understanding of CI/CD pipelines for ML systems * Strong fundamentals in machine learning including model architecture design, training strategies, and evaluation Nice To Have * Experience with reinforcement learning algorithms and applications * Digital signal processing experience * Background in time-series analysis or sensor data processing * Experience with edge deployment and model optimization for resource-constrained environments * Familiarity with distributed training across multiple GPUs/nodes * Experience with model compression techniques (quantization, pruning, distillation) * Contributions to open-source ML projects or research publications * Experience in defense, aerospace, or other regulated industries, Aerospace and Defense, Amazon Web Services (AWS), Application Programming Interface (API), Architectural Design, Artificial Intelligence (AI), Cloud Computing, Continuous Deployment/Delivery, Continuous Improvement, Continuous Integration, Correctional Health, Cross-Functional, Data Collection, Data Management, Data Processing, Digital Signal Processing (DSP), Docker, Engineering, GCP (Good Clinical Practices), GPU (Graphics Processing Unit), Geography, GitHub, Leadership, Leading Edge Technology, Machine Learning, Machine Tool, Microsoft Windows Azure, Neural Networks, Open Source, Performance Modeling, Performance Tuning/Optimization, Product Development, Production Systems, Prototyping, Publications, Python Programming/Scripting Language, REST (Representational State Transfer), Reimbursement, Reinforcement Learning, Signal Processing, Strategic Analysis, Team Building, Team Lead/Manager, Team Player, Time Series Analysis, Training/Teaching, United States Navy (USN) ## Description Spear AI builds sonobuoy sensors that are deployed into the water and collect edge data. We also work with the U.S. Navy to collect and process their SONAR data. You'll have an opportunity to work on real-world projects that directly impact warfighter capabilities and mission success. What You'll Do * We're a small team wearing many hats, and you'd have a wide variety of responsibilities that include: * Design, train, and optimize machine learning models using PyTorch * Deploy models to production environments in the cloud and at the edge * Build and maintain ML pipelines for training, evaluation, and inference * Integrate machine learning models into real-time and batch processing systems * Optimize model performance for accuracy, latency, and resource constraints * Implement model monitoring, versioning, and deployment strategies * Work with signal processing data and time-series analysis * Improve local development and CI/CD for ML workflows using modern tooling and GitHub Actions Who You Are * We're looking for someone with strong Machine Learning Engineering skills who shares our most important values: * You're fanatical about polish. Every detail matters. You love to make sure your code is linted, formatted, fully typed, and has comprehensive test coverage. * You care about correctness. You take pride in the fact that your models perform reliably and downstream consumers trust your predictions. * You obsess over performance. You daydream about model latency, throughput, and efficient inference pipelines. * You dive deep. It's important for you to really know how things work. You're always building prototypes and setting up experiments to reinforce your understanding. * You live on the bleeding edge. You've got a long list of upcoming ML techniques and frameworks you're excited about and can't wait to experiment with new approaches. * You're a great teacher. You know how to break down complex ML concepts for a specific audience and make it click with them in a way that gets them excited. Why Work With Us * We ship - We don't work on 18-month projects that are irrelevant before they're even finished. * Our work has impact - We build products that are deployed to U.S. submarines and integrate with the sonobuoys we manufacture. * We're growing responsibly - We have the resources to hire a lot more people, but we don't want to build a massive team of people who don't share our values. * We're remote - Work from wherever you want. We collaborate in real time on Slack or asynchronously via GitHub. * We're profitable - We aren't burning through cash trying to make the business work. But we also have investors who believe in us and are committed to our success. * We care about doing great work - You don't need permission to sweat the details here. * We don't take ourselves too seriously - We're building products that make the world safer. But we don't let that get to our heads.