---
title: 'Forward Deployed Engineer - MTS at Context'
canonical: 'https://feeny.ai/job/forward-deployed-engineer-mts-context-san-francisco-g9798haj5h5m'
type: 'job'
last_seen: '2026-09-06'
---

# Forward Deployed Engineer - MTS at Context

- **Company:** Context
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-04-15
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/context/51f8186a-b203-4e09-a047-564e7e993f60

## Job description

## ABOUT THE COMPANY

Context is the AI platform to redefine knowledge work. We:

- 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’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. 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.

## WHAT YOU'LL DO

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

## 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

## About Context

## Company Overview
- **One-liner**: Context provides an enterprise AI execution layer platform that captures internal workflows and expert judgment to deploy agents for task completion.
- **Entity Type**: Private (Seed Stage)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024
- **Founders**: Not publicly available

## Core Business
- **Primary industry**: Enterprise AI / Agentic AI Platform
- **Target customers**: Large enterprises in financial services, legal, insurance operations, consulting, telecom, and the public sector—teams whose work is high-volume, document-heavy, and judgment-dependent.
- **Mission or purpose**: "Change computing forever" — the company aims to advance the state of the art in computing and bring those advances to the broadest possible audience.

## Products & Services
- **Workspace**: A collaborative environment where human teams and AI agents complete tasks side by side using the same documents, spreadsheets, decks, and kanbans.
- **Engine**: A permissioned runtime that runs inside the customer’s perimeter (hosted, VPC, on-prem, or air-gapped), giving agents identity, tools, and compute with zero standing credentials.
- **Unify**: The institutional-context layer that accumulates knowledge across runs in a traversable filesystem, with a `.context/` directory at every level where agents write what they learn.
- **Evals**: A system that scores every agent run against rubrics authored by the customer’s own experts, turning accepted work into golden sets and corrections into measurable improvement.

## Market Standing
- **Valuation**: Not disclosed
- **Key Metric**:
  - **Total Funding**: $11.0M (Seed Round, led by Lux Capital, announced May 2025)
  - **Annual Revenue**: ~$500,000 (estimated, per LinkedIn data)
- **Notable Investors/Partners**: Lux Capital (lead investor in seed round)
- **Growth Signals**:
  - Headcount: 34 employees with +3.3% monthly growth
  - 12,473 LinkedIn followers (+0.2% monthly)
  - 800+ pre-built connectors across data warehouses, documents, CRMs, ticketing, and internal systems
  - Multiple press mentions in TechCrunch, The Information, VentureBeat, and PR Newswire in 2025-2026
  - Announced GA of enterprise agent platform (May 2026)
  - Launched air-gapped AI agents for regulated industries (April 2026)

## Competitive Advantages
- **Closed-loop learning system**: Every completed task makes the next one more accurate and cheaper to serve, creating a compounding data moat.
- **Enterprise-grade security and compliance**: Agents inherit user permissions from the customer’s IdP at every action, hold zero standing credentials, and can run fully air-gapped or on-prem.
- **Model-agnostic routing**: Each step routes to the cheapest model that clears a custom rubric, allowing frontier models to handle only genuinely novel tasks while keeping costs low.
- **Plain-English runbooks**: Workflows are defined in plain English that operators, engineers, and compliance teams can all read—no prompt engineering required.
- **Data sovereignty**: Customer traces, corrections, and institutional context belong solely to the firm and never leave its control or feed other models.

## Strategic Focus
- **Deployment speed**: Most teams run their first production workflow within a few weeks—pick one process, connect the systems, have experts review early runs.
- **Vertical expansion**: Pre-built agents and use cases for semiconductors, financial services, consulting, telecom, public sector, healthcare, legal, insurance, and supply chain.
- **Self-improving systems**: Evals gate every change; accepted outputs become reusable standards and training data for custom models the customer owns and serves.
- **Flexible deployment**: Hosted, in customer VPC, on-prem, or fully air-gapped, with identity and compute staying on the customer’s side of the perimeter.

## Why Work Here
- **Culture & values**: The company emphasizes "Kick butt, have fun, don't cheat, love our customers and change computing forever." Principles and values are described as the "soul" of the company.
- **Team size**: Small (~34 employees), offering significant ownership and impact.
- **Work environment**: Collaborative—teams and agents work on the same files in the same environment.
- **Growth trajectory**: Rapidly growing startup (Seed-stage, $11M raised, GA announced in May 2026) with strong media attention and enterprise traction.
- **Engineering culture**: Focus on advancing the state of the art in AI, working with frontier models (Claude, GPT, Gemini, open weights), and building infrastructure for enterprise-grade agent deployment.
- **Remote/hybrid policy**: Not explicitly stated; headquarters in San Francisco.
- **Notable perks**: Opportunity to work on cutting-edge enterprise AI infrastructure with a small, high-impact team; direct exposure to customers and real-world deployment challenges.

## Sources
1. [context.ai](https://www.context.ai/)
2. [Context About Page](https://context.ai/about)
3. [Context Press Page](https://www.context.ai/press)
4. [Context FAQ](https://www.context.ai/faq)
5. [LinkedIn - Context](https://www.linkedin.com/company/contextworkspace)
6. [Ashby Careers Page](https://jobs.ashbyhq.com/context)

## Other roles at Context

- [Summer Intern](https://feeny.ai/job/summer-intern-context-san-francisco-4fze4c1wnzwp) — San Francisco, CA
- [Deployment Strategist](https://feeny.ai/job/deployment-strategist-context-san-francisco-ss9jcxyrs2se) — San Francisco, CA
- [MTS](https://feeny.ai/job/mts-context-san-francisco-16e7633nekf9) — San Francisco, CA