Machine Learning Engineer - Product
Cognitive Space uses the power of AI to help organizations forecast, optimize, and orchestrate their satellite constellations.
You are skilled in machine learning (ML) development, and data science, and interested in building intelligent space systems to modernize satellites' operations. For this role, you should be ready to work in a fast-paced, dynamic, startup environment. You will be our ML engineer, integrating new research, and deploying ML models to production. You have professional experience deploying and monitoring ML models in production environments and stay updated with the field's latest developments. You must be comfortable taking ideas from theory into deployment. You will also:
- Own, train, build and deploy cutting edge, deep learning models for satellite mission planning, and other aerospace applications.
- Break down business objectives and design models that help to achieve them, along with metrics to track their progress.
- Deploy ML models to production continuously and at scale to support a disparate customer base.
- Version ML models per use-case and customer, and track baseline performance metrics.
- Manage hardware resources for production and development environments.
- Monitor production ML models, analyze performance and improve product performance.
- Collaborate with aerospace domain experts, and software developers to understand customer needs, and produce excellent products.
- US Citizenship required.
- 2+ years of proven professional experience as a Data Scientist, Machine Learning Engineer, Development Operations Engineer, Applied Scientist, or similar role.
- Bachelor’s, Master’s degree, or Ph.D. in a relevant field: Statistics, Applied Mathematics, Computational Physics, Computer Science (CS), Engineering, etc.
- Exceptionally strong knowledge of CS fundamental concepts, such as object-oriented development skills, and ML languages, such as Python, C, C++, R.
- Extensive experience with scientific libraries in Python (NumPy, pandas) and machine learning tools and frameworks (scikit-learn, TensorFlow, Keras, PyTorch, etc.)
- Knowledge of a variety of machine learning techniques (semantic segmentation, clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
- Familiar with large language models (LLM), transformers like BERT, GPT-3|4, T-5 etc.
- Familiar with the latest commercial AI platform offerings (e.g., OpenAI)
- Ability to communicate highly technical concepts to non-technical people.
- Ability to work in a team and work collaboratively on complex tasks.
- Outstanding analytical and problem-solving skills.
One of the most interesting aspects of working at a startup company is gaining equity in the company, which means our success is your success. In addition to equity in the form of options, we also offer:
- Flexible Time-Off policy, company holidays, and company retreat
- Cost-effective health care, dental, and vision with company contributions.
- 401K plan
- Life insurance
- Short term and long-term disability
- Home Office Fund & Swag Stipend
Salary: $90K - $150K
Work Location: Remote
Multiple studies have found that a higher percentage of women and BIPOC candidates won't apply if they don't meet every listed qualification. Cognitive Space values candidates of all backgrounds. If you find yourself excited by our mission but you don't check every box in the description, we encourage you to apply anyway!
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