Machine Learning Engineer
Actively Hiring
Full-time Posted 8 months ago
Responsibilities
- check_circle We're a small team wearing many hats, and you'd have a wide variety of responsibilities that include:
- check_circle Design, train, and optimize machine learning models using PyTorch
- check_circle Deploy models to production environments in the cloud and at the edge
- check_circle Build and maintain ML pipelines for training, evaluation, and inference
- check_circle Integrate machine learning models into real-time and batch processing systems
- check_circle Optimize model performance for accuracy, latency, and resource constraints
- check_circle Implement model monitoring, versioning, and deployment strategies
- check_circle Work with signal processing data and time-series analysis
- check_circle Improve local development and CI/CD for ML workflows using modern tooling and GitHub Actions
- check_circle We're looking for someone with strong Machine Learning Engineering skills who shares our most important values:
- check_circle 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
- check_circle You care about correctness. You take pride in the fact that your models perform reliably and downstream consumers trust your predictions
- check_circle You obsess over performance. You daydream about model latency, throughput, and efficient inference pipelines
- check_circle 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
- check_circle 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
- check_circle 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
- check_circle We ship — We don't work on 18-month projects that are irrelevant before they're even finished
- check_circle Our work has impact — We build products that are deployed to U.S. submarines and integrate with the sonobuoys we manufacture
- check_circle 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
- check_circle We're remote — Work from wherever you want. We collaborate in real time on Slack or asynchronously via GitHub
- check_circle 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
- check_circle We care about doing great work — You don't need permission to sweat the details here
- check_circle 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
- check_circle Several years of experience with Python and machine learning frameworks
- check_circle Expertise in PyTorch for building and training neural networks
- check_circle Experience training and serving models in cloud environments (AWS, Azure, GCP)
- check_circle Proficiency with MLOps practices including experiment tracking, model versioning, and deployment
- check_circle Experience with model optimization for production performance and scale
- check_circle Knowledge of Docker and Kubernetes for containerized deployments
- check_circle Familiarity with REST APIs and model serving frameworks
- check_circle Understanding of CI/CD pipelines for ML systems
- check_circle Strong fundamentals in machine learning including model architecture design, training strategies, and evaluation
Preferred qualifications
- 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
Benefits
- check_circle Unlimited PTO — Take the time you need to recharge and maintain work-life balance
- check_circle Dedicated Sick Time — Your health and well-being come first
- check_circle Comprehensive Health & Benefits — Medical, dental, and vision coverage to keep you and your family protected
- check_circle 11 Paid Holidays — Enjoy time off throughout the year to celebrate and spend with loved ones
- check_circle Professional Development — Educational opportunities and resources to help you grow your skills and advance your career
- check_circle Collaborative Environment — Work directly with leadership in our flat organizational structure, where your ideas and contributions matter
- check_circle Mission-Driven Work — Contribute to projects that directly support national security and make a real-world impact
- check_circle Growth Opportunities — Join us during an exciting expansion phase where you can help shape our future
- check_circle 401(k) with company match
- check_circle Onsite / Remote / Flexible work arrangements or hybrid options (position dependent)
- check_circle Relocation assistance (position dependent)
- check_circle Referral bonuses
- check_circle Performance bonuses
- check_circle Life insurance and disability coverage
- check_circle Technology home office setup stipend
- check_circle Professional certification reimbursement (position dependent)
Tags & Focus Areas
Fulltime Remote Ai Machine Learning
About Spear AI
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, ...