Context
Forward Deployed Engineer - MTS
San Francisco Office
Sponsorship not specified$30k-$40kDetected 98 days ago
JavaScriptTypeScriptPythonJavaDistributed SystemsData StructuresAWSGCPAzureCloud PlatformsDevOpsMachine LearningData EngineeringLLMsAgentic AIFinancial AnalysisM&AProblem SolvingMentoring
About the role
- We've built applications with 1M+ users, launched campaigns reaching 180M+ people, and forward deployed products in some of the world's largest teams.
- We're fortunate to be backed by Lux Capital, Qualcomm Ventures, General Catalyst, and BoxGroup.
- Most companies must choose between iterating on product fast and working with the biggest enterprises on earth.
Responsibilities
- 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.
- 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.
- 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.
- Collaborating with engineers on architecture and design decisions for AI agents that execute complex workflows
- 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
- Shaping team strategy and driving projects from ideation to deployment, increasing your pain threshold to deliver real value and measurable productivity gains
- 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
Requirements
- team members must be able to learn from their mistakes and improve constantly
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
- 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
Compensation
- $30k-$40k
Benefits
- Engineering the learning flywheel -building systems that capture subject matter expert feedback and continuously improve AI agent capabilities
Company info
- Context is the AI platform to redefine knowledge work.
- Build agents that continuously learn to capture companies' proprietary intelligence, including procedures, data, and objectives
- Provide the work surface for them to perform complex, long-horizon tasks alongside humans in a native office suite
- Deploy them in secure environments to Fortune 500 companies
- We are a fast-moving team of engineers and researchers from Apple, Ramp, Stripe, Meta, BAIR, and SAIL.
- We have the rare privilege of doing both.
- Features we ship this week are in the hands of teams at Fortune 100 companies next week, meaning every product bet we make gets immediate, high-stakes signal from some of the most consequential corporate teams in the world.
- You'll embed directly with Fortune 100 customers to build AI agents that execute complex, high-stakes work-not just chat or simple automation.
- 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.
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