Senior Machine Learning Engineer, Agentic Systems
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StriveworksMachine learning
Location: Remote or Austin, Texas, United States. This position offers a fully remote work environment, or you can work hybrid/on site at our office in northwest Austin, TX.Full-TimeSenior
SalaryThe anticipated base pay range for this position is $160,000–$200,000/year. Striveworks’ total compensation package includes a competitive base salary, equity grants, and cash bonuses.
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Job Details
- Experience
- 6+ years relevant experience
- Required Skills
- PythonETLPyTorchTensorflowCI/CDscikit-learn
Requirements
- Hold a BS degree in computer science, machine learning, or a related discipline.
- Have 6+ years of relevant experience.
- Have demonstrated experience delivering data-centric systems, such as data engineering, data cleaning, ETL pipelines, machine learning, or production analytics.
- Have experience designing, building, evaluating, and optimizing LLM-powered agents and agentic workflows.
- Have experience integrating AI systems with external tools, APIs, MCP servers, and enterprise data sources.
- Have experience developing evaluation approaches and frameworks to measure agent performance, reliability, and safety.
- Be familiar with retrieval-augmented generation (RAG), tool use, planning, and multi-agent architectures.
- Be proficient in Python and machine learning programming languages and libraries, including TensorFlow, PyTorch, and/or scikit-learn.
- Have strong software engineering fundamentals, including algorithms, data structures, and design patterns.
- Be proficient in at least one systems programming language, such as Go, Rust, C++, Java, or Scala.
- Be proficient with modern software engineering tools and processes, including Agile, version control, issue tracking, CI/CD, and debugging.
- Be a US citizen and eligible and willing to obtain and maintain a Secret US security clearance.
Responsibilities
- Develop machine learning solutions for customer-driven projects and company products.
- Orchestrate complex data and agentic workflows.
- Design, build, evaluate, and optimize LLM-powered agents and agentic workflows.
- Integrate AI systems with external tools, APIs, MCP servers, and enterprise data sources.
- Develop evaluation approaches and frameworks to measure agent performance, reliability, and safety.
- Analyze and improve AI system performance and usability.
- Contribute to capabilities and improvements for Chariot, the company's AI operations platform.
- Work with customers, data scientists, software engineers, and DevOps engineers.
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