What You'll DoAt Context AI, the Forward Deployed Software Engineer (FDSE) role is where cutting-edge AI meets real-world complexity. You'll embed directly with Fortune 100 customers to build AI agents that execute complex, high-stakes work-not just chat or simple automation. As an FDSE, you'll be at the intersection of frontier language models and institutional intelligence, building systems that perform production-quality work knowledge workers do every day.
FDSEs work side by side with our customers, rapidly understanding their most complex workflows and architecting solutions that ground AI in institutional intelligence-the tribal knowledge, business rules, and quality standards that define how organizations actually operate. Whether it's "How do we enable AI to diagnose firmware failures across million-line codebases?" or "How can AI run due diligence on multi-terabyte M&A data rooms with six-figure analyst quality?", you'll use your engineering expertise, creativity, and problem-solving skills to build AI agents that deliver 30-40% productivity improvements and 90%+ cycle time reductions.
You'll have the rare opportunity to gain deep insight into and directly influence some of the world's most critical industries-telecommunications, finance, consulting, biotech, technology. By building on Context's AI platform and grounding it in customer data, you'll help organizations unlock AI that executes real work, operating 24/7/365 as a continuously improving teammate.
As an FDSE, you'll experience the autonomy of a startup with the resources, mentorship, and stability of a well-funded AI company. Your contributions will have direct impact on how enterprises deploy AI and the productivity of knowledge workers. You'll work in small, agile teams and own end-to-end execution of high-stakes deployments, including:
- Collaborating with engineers on architecture and design decisions for AI agents that execute complex workflows
- Wrangling massive-scale data-integrating codebases, operational systems, data rooms, and proprietary datasets into stable pipelines that ground AI in institutional intelligence
- Building custom AI workflows tailored to customer needs: engineering diagnostics, financial analysis, client deliverable generation, code shipping
- Developing integrations that connect Context agents to customer tools and systems-Slack, Linear, Google Workspace, proprietary platforms
- Engineering the learning flywheel-building systems that capture subject matter expert feedback and continuously improve AI agent capabilities
- Engaging directly with customer stakeholders, from engineers and analysts to executives, understanding their workflows and demonstrating AI impact
- Shaping team strategy and driving projects from ideation to deployment, increasing your pain threshold to deliver real value and measurable productivity gains
- Embedding product insights from customer deployments into Context's core platform, turning customer-specific solutions into cross-customer capabilities
What We Value- Agency: Innovation happens when team members think from first principles and go above and beyond to achieve objectives-not by simply completing tasks
- Strong Engineering Fundamentals: A highly analytical approach and eagerness to solve technical problems with data structures, distributed systems, cloud infrastructure, APIs, and modern frameworks
- Obsession with Execution Quality: Understanding the difference between AI that assists and AI that executes production-quality work-and building systems that achieve the latter
- Comfort with Ambiguity: Experience or curiosity about working with massive-scale, unstructured data to solve valuable business problems where "how we do things" isn't documented
- Product Creativity: Our engineers don't just turn inputs into outputs. We expect team members to think creatively and invent ways to improve the product
- Low Ego: We understand that the outcome matters more than who gets the credit. Team members share wins and don't play politics
- Adaptive and Introspective: We operate in a fast-moving environment and accordingly iterate rapidly; team members must be able to learn from their mistakes and improve constantly
What We Require- 2+ years of relevant, post-college work experience in software engineering, preferably in customer-facing or deployment roles
- Strong engineering background, preferred in fields such as Computer Science, Software Engineering, Mathematics, Physics, or related technical disciplines
- Strong coding skills with proficiency in programming languages such as Python, TypeScript/JavaScript, Java, or similar
- Experience building production systems-APIs, data pipelines, web applications, or integrations with enterprise software
- Intellectual curiosity about AI/ML systems and their application to real-world problems
- Ability and interest to travel up to 25-50% as needed to customer sites for onboarding, training, and deployment (flexible based on customer needs and personal preferences)
Nice to Have- Experience with AI/ML systems, LLMs, or agent frameworks
- Prior work in consulting, professional services, or customer-facing technical roles
- Familiarity with enterprise software ecosystems (Google Workspace, Slack, Linear, etc.)
- Background in or curiosity about specific domains: telecommunications, finance, consulting, biotech, engineering systems
- Experience with cloud infrastructure (AWS, GCP, Azure) and modern DevOps practices
- Track record of driving measurable impact in customer deployments or product implementations
