Strider Technologies
strategic intelligence
StaffAIEngineer
Neural analysis suggests this role is
optimal for Lead candidates.
“Staff AI Engineer at Strider Technologies. Skills: AI, LLM, data processing, agentic engineering. Build and own services. Build and own features”
What You'll Achieve.
Deliver strategic intelligence; Transform open-source data into actionable insights; Shape, stage, and prioritize data for consumption; Amplify leverage using AI; Increase velocity and quality using AI coding tools
Industry & Context.
excellent problem-solving skills; solving problems; hard problems
What They're Looking For.
Must Have
Bachelor’s degree in Computer Science, Engineering, or related field, 10+ years of experience, Track record of setting technical direction, Demonstrated examples of making architectural/technical bets, Experience shipping features and solutions that use LLMs in data heavy environments, Experience with frontier model SDKs (OpenAI, Anthropic, etc. ), Experience with open-source model providers and frameworks, Experience with agentic frameworks, Ability to communicate complex technical concepts, Engage with senior stakeholders across disciplines, Self-motivated, excellent problem-solving skills, attention to detail
Nice to Have
opinions about best practices for agentic engineering, opinions about best practices for AI coding tools, enthusiasm for further developing best practices, Experience mentoring engineers, developing communities of practice within engineering groups, Comfort and competence deploying features into AWS cloud infrastructure, Master’s degree or PhD in Computer Science, Engineering, or related field
What You'll Do.
Build and own services
Build and own features
Optimize LLM-based data processing tasks
Design technical solutions
Provide feedback on design documents
Provide feedback on architectural decisions
Participate in code reviews
Help improve practices for AI-assisted code review
How You'll Work.
Team & Collaboration
Engage with senior stakeholders across disciplines (engineering, product, executives); Collaborate openly across disciplines; ML engineers work closely with software engineers and product stakeholders; Contribute to and learn from conversations regularly
Communication Scope
Communicate complex technical concepts
Full Job Description
Strider Technologies delivers strategic intelligence that helps organizations make faster, more confident decisions in an increasingly complex global environment. Using cutting-edge AI and proprietary methodologies, we transform open-source data into actionable insights that help protect technology, talent, and supply chains from nation-state risks. We are looking for an exceptionally motivated Staff AI Engineer to join Strider’s Context Engineering team. Context Engineering exists to help shape, stage, and prioritize data for consumption by our analysts, customers, and agentic analytical production processes. We are looking for candidates who are comfortable working across our full data processing pipeline and are motivated by solving problems and having mission impact. We want individuals who are excited to use AI to amplify their leverage and are enthusiastic about the evolving practice of software and agentic engineering. You will: - Build and own services and features to support our Context Engineering organization, focusing on hybrid data search capabilities (using ElasticSearch and S3Vectors) at million to billion document scale. - Optimize a variety of LLM-based data processing tasks: for example, entity disambiguation, document quality and relevance scoring, text extraction tasks. - Design technical solutions and provide feedback on design documents and architectural decisions across the organization. - Participate in code reviews and help improve practices for AI-assisted code review. What you need to be successful: - Bachelor’s degree in Computer Science, Engineering, or related field - 10+ years of experience - Track record of setting technical direction and executing beyond the small team level. - Demonstrated examples of making architectural/technical bets that paid off, or course-corrected when they didn’t. - Experience shipping features and solutions that use LLMs in data heavy environments. Experience with frontier model SDKs (OpenAI, Anthropic, etc
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