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# Forward Deployed Software Engineer

- **Company:** MTS Partners, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Systems Engineering, Microsoft Azure, Cloud Computing, Encodings, Computer Programming, Data Files, Data Transformation, Data Structures, DevOps, Distributed Systems, Firmware, Python (Programming Language), Language Modeling, Office Suite, Software Engineering, Systems Integration, TypeScript, Unstructured Data, Web Applications, Web Application Frameworks, Scripting, Google Cloud, Enterprise Software Applications, Large Language Models, Multi-Agent Systems, Information Technology, Slack, Operational Systems, Machine Learning Operations, Gsuite, Virtual Agents, Software Coding, Data Pipelines, Automation Anywhere, Programming Languages
- **Published:** July 31, 2026
- **Apply:** https://www.careerbuilder.com/job-details/forward-deployed-engineer-mts-san-francisco-ca--87925fce-be9f-4b42-8ef2-d5aac0b2b266

## About the Role

* 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

Skills:
Analysis Skills, Apple, Application Programming Interface (API), Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Automation, Biotech and Pharmaceutical, Building Systems, Campaigns, Cloud Computing, Computer Firmware, Computer Programming, Computer Science, Consulting, Continuous Improvement, Customer Relations, Customer/Client Research, Data Sets, Data Structures, Distributed Computing, Due Diligence, Ecosystems, Enterprise Applications, Financial Analysis, Financial Planning, Fortune 500 Customers, Frontier Programming Language, Identify Issues, Java, JavaScript, Mathematics, Mentoring, Mergers and Acquisitions, Modeling Languages, Office Suites, Onboarding, Physics, Politics, Problem Solving Skills, Productivity Management, Professional Services, Programming Languages, Python Programming/Scripting Language, Quality Metrics, Slack, Software Engineering, Standards Development, Startup, Telecommunications, Telecommunications Industry, Unstructured Data, Willing to Travel

## Description

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


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