Forward Deployed Engineer - MTS

At 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

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

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)

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