Staff Machine Learning Engineer
S
StriveworksNational Security AI
Austin, Texas or RemoteFull-TimeStaff
Salary200000 - 250000 USD per year
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Job Details
- Experience
- 10+ years of relevant experience
- Required Skills
- PythonAgileJavaKafkaKubernetesPyTorchRabbitmqC++GoRustTensorflowCI/CDScalascikit-learn
Requirements
- Advanced degree in data science, machine learning, computer science, or a related discipline
- 10+ years of relevant experience
- Broad proficiency in Python and libraries like TensorFlow, PyTorch, and scikit-learn
- Knowledge of systems programming (e.g., Go, Rust, C++, Java, Scala)
- Proficiency in the design and delivery of algorithms, data structures, and production analytics
- Proficiency in the use of design patterns in cloud environments
- Demonstrated experience defining, scoping, planning, and delivering complex technical solutions in production environments
- Proficiency with modern software engineering tools and processes (Agile, version control, issue tracking, CI/CD, debugging)
- Demonstrated ability to lead, manage, and mentor small cross-functional teams that work across office, remote, and customer sites
- Ability to communicate complex topics with professionalism, competence, and clarity to internal and external stakeholders
- Active Secret (or above) US security clearance
Responsibilities
- Work with customers, engineers, and other stakeholders to define clear requirements that solve customer problems and leverage AI operations platform capabilities.
- Translate requirements into a technical approach, design, scoping estimate, and execution plan.
- Lead execution teams to achieve on-time completion of project deliverables mapped to customer business value while making key individual contributions.
- Design, orchestrate, and automate complex data pipelines and algorithms within modern architectures (cloud, event-driven, microservices, etc.).
- Guide the development of machine learning models and custom analytics applied to image, video, text, geospatial, time series, and structured data.
- Raise insights, opportunities, challenges, and feedback in order to improve group-level practice, capture reusable functionality, expand company opportunities, and accelerate time to value.
- Conduct mission-critical fieldwork and interface with customers and other stakeholders at their work sites.
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