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Zarif Automates

AI Careers

Forward-deployed engineering, GTM engineering, and the roles the AI era is creating: what they are, who hires, how to get in.

Start with

A focused reading order to help you choose your next step.

  1. What Is a Forward Deployed Engineer? The Complete Guide

    Learn what Forward Deployed Engineers do, how the role works, why AI companies hire them, and when the FDE model makes business sense.

  2. How to Become a Forward Deployed Engineer

    Build the engineering, product, and customer skills needed for FDE roles with portfolio projects, a 12-week roadmap, and job-search guidance.

  3. Forward Deployed Engineer Interview Guide

    Prepare for FDE interviews across coding, systems design, customer discovery, product judgment, ambiguity, and behavioral evaluation.

  4. The Forward Deployed Engineering Playbook: Discovery to Production

    Run FDE engagements from qualification to production with clear artifacts, exit criteria, adoption measures, handoffs, and product feedback.

  5. Forward Deployed Engineer vs Solutions Architect vs Consultant

    Compare FDEs, solutions architects, consultants, implementation engineers, and product engineers by ownership, coding depth, and outcomes.

  6. How to Transition Into an AI Career: Complete Guide

    A realistic 2026 roadmap for transitioning into an AI career: skills to learn, projects to ship, salaries to expect, and how long it actually takes.

Explore related engineering roles

GTM Engineer

Revenue systems, integrations and commercial experiments.

AI Engineer

Model-powered applications, retrieval, evaluations and operations.

Agent Engineer

Tools, permissions, durable state and recoverable execution.

Career field guide

Forward-deployed engineering

FDE work connects a customer problem to software that runs in the real environment. The role combines discovery, implementation and responsibility for the outcome. Use this path to understand the job, build evidence of the skills and evaluate a team before joining it.

A twelve-guide reading path

Ten FDE guides, with two broader guides for career transition and technical foundations.

  1. What Is a Forward Deployed Engineer? The Complete Guide

    Understand the work and the ownership boundary.

  2. Forward Deployed Engineer vs Solutions Architect vs Consultant

    Compare the role with adjacent customer-facing jobs.

  3. How to Transition Into an AI Career: Complete Guide

    Map existing experience to a practical transition plan.

  4. How to Learn AI from Scratch: Free Resources Guide

    Fill gaps in the underlying technical skills.

  5. How to Become a Forward Deployed Engineer

    Choose a portfolio project that demonstrates implementation.

  6. Forward Deployed Engineer Interview Guide

    Practice discovery, design and debugging conversations.

  7. The Forward Deployed Engineering Playbook: Discovery to Production

    Follow an engagement from discovery through production.

  8. Forward Deployed Engineers for Enterprise AI: Why the Model Works

    Understand the enterprise integration context.

  9. When Should You Hire a Forward Deployed Engineer?

    Decide whether the delivery model fits the problem.

  10. How to Build a Forward Deployed Engineering Team

    Define team interfaces and recurring responsibilities.

  11. How to Measure FDE Teams: Metrics, ROI, and Unit Economics

    Connect delivery measures to customer outcomes.

  12. Why Forward Deployed Engineering Teams Fail

    Review failure patterns before repeating them.

Compensation in current employer postings

Checked September 17, 2026. Annual USD ranges from three specific US listings. These are not market averages, comparable levels or guaranteed offers. Read the linked posting for the current range and package.

Employer-posted FDE compensation checked September 17, 2026
Employer and roleLocationPosted rangeWhat the range covers
OpenAI
Forward Deployed Software Engineer
San Francisco$185,000–$325,000Posting labels this compensation and lists equity separately.
Anthropic
Forward Deployed Engineer
New York / San Francisco / Seattle$280,000–$320,000Posting labels this annual salary; confirm the package and level with the employer.
Palantir
Forward Deployed AI Engineer
New York$135,000–$200,000Estimated annual salary; posting excludes potential bonuses, benefits and long-term incentives from this range.

The FDE and GTM hiring directory links directly to five employers. Compare implementation ownership, travel and post-launch responsibilities alongside pay.

Monthly FDE hiring index

Observed 2026-09-17 (UTC). 65 matching advertisements across five employer boards.

A five-employer sample of public advertisements, not a count of vacancies, people hired or the whole FDE market. Separate posting IDs can describe similar work in different locations; one posting can cover several openings. This is the first snapshot, so no growth rate is reported.

Scroll horizontally to see the full hiring table.

Public FDE title sample by employer, 2026-09
EmployerMatching advertisementsBoard postings inspectedSource observed (UTC)
OpenAI17817
Anthropic4607
Baseten299
Clay058
Palantir42313

Download posting-level CSV · Snapshot and methodology data

How the sample is counted

Method fde-title-sample-v1: count unique posting IDs within each employer whose title begins with Forward Deployed Engineer, Forward Deployed Software Engineer or Forward Deployed AI Engineer (hyphens accepted). Exclude unlisted postings and titles containing manager, management, director, head, strategist, sales, intern or internship. New-graduate roles are included. Titles such as Forward Deployed Infrastructure Engineer and GTM Engineer do not match; domain or location suffixes after an eligible prefix can match. This is a title rule, not an assessment of every job's responsibilities or seniority. A failed board is unavailable, never zero; no combined total is shown for an incomplete sample.

Source dates have different meanings: Ashby reports last publication, Greenhouse first publication when supplied, and Lever a creation timestamp. Creation is not a verified publication date. Missing dates remain blank. The checked date records collection, not when a job opened. Primary and additional locations are preserved; no seniority or vacancy count is inferred from them.

Ashby uses its full-board v1 response; Greenhouse's declared total must match the returned posts; Lever pages are collected until an empty page. Response hashes, counts and request URLs are in the JSON. This is a timed observation, not an atomic snapshot across employers; changes while a board is being read can still affect it.

Missing publisher dates are left blank. The JSON records eligible advertisements and reconciled exclusion counts. Closed or removed posts leave the next approved snapshot; their disappearance does not prove a hire. Changes in title, location or board structure can change the count.

Monthly collection creates a proposed update for review. This table remains on the last approved snapshot until that review is complete.

Interview practice bank

Original practice prompts, not leaked questions or a claim about any employer’s interview loop.

A customer asks for an AI assistant, but no one agrees what success means. What do you do first?

Clarify the user, current workflow, costly failure and measurable acceptance criterion before proposing a model.

A demo passes, but production answers are unreliable. How do you investigate?

Collect representative failures, inspect retrieval and tool traces, separate system faults from model faults, and build a regression set.

The required integration is rate-limited and occasionally unavailable. How would you ship it?

Explain idempotency, retry limits, backpressure, observable failure states and a fallback that preserves user work.

A customer-specific shortcut would make delivery faster but complicate the product. How do you decide?

Describe the tradeoff, who owns the exception, its expiry or migration path, and what should become a reusable product capability.

A deployed agent can modify business records. Where should approval happen?

Identify irreversible actions, constrain tool permissions, preview concrete changes, and preserve an audit trail with recovery behavior.

What would you show at the end of the engagement?

A working artifact, acceptance results, known limitations, operational ownership and documentation someone else can use.

All posts in AI Careers(15)

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