Forward Deployed Engineer

New York, NY · San Francisco, CA · Toronto, Canada · Hybrid · Full-time

$185K–$240K base + competitive equity

We are a software holding company that buys, builds, grows, and holds vertical market software businesses for the long term.

This is an operating company with permanent capital, not a financial sponsor. The model is designed to compound through durable software assets, not to optimize for a short exit.

The thesis is to scale through software, not headcount, and that means deployment engineering is a core function of the business model.

The company was founded in 2024, is about 85 people, and recently announced a $250M Series B.

Backers include General Catalyst, D1 Capital, and family offices tied to technology operators including John & Patrick Collison, Tony Xu, Eric Glyman, and Karim Atiyeh.

The team is led by operators who scaled Instacart and invested at Sequoia.

This is a forward deployed engineer role in the Palantir sense: embed with portfolio companies, identify the highest-leverage workflow to automate, ship the first production wedge, and stay with it until it works in live operations.

You are not writing decks or handing the problem back to headquarters. You are building running systems that have to survive in real operations.

You will work directly with portfolio company leaders, operations teams, and the central engineering team. There is no heavy PM layer to translate the problem for you.

The scope is end to end: discovery, integration, prompts, evals, UI surfaces, launch, tuning, handoff, and the playbook that can be reused in the next deployment.

This is not a services role in the usual sense. The code and workflows you ship should become reusable product and platform assets for the rest of the portfolio.

These are not tidy greenfield products. You will be working inside real operating environments with existing systems, uneven data quality, manual process debt, and stakeholders who care about business outcomes more than technical elegance.

The hard part is not building a demo. The hard part is making a machine-assisted workflow reliable enough that an operator will change how they work.

That means reasoning about integrations, failure modes, evals, autonomy thresholds, rollback paths, and the human handoff between software and operations.

• Workflow discovery: sit with GM and ops teams, map the actual process, and identify the 2–3 highest-leverage points where software can remove time, error, or coordination cost.
• Production wedges: build the first agentic loop using the shared platform, including integrations, prompts, evaluation logic, and the minimum UI needed to put it in production.
• Autonomy controls: define when the system acts, when it asks for approval, and when it falls back to a human, with explicit gates rather than vague guardrails.
• Outcome tracking: instrument the deployment so success is measured by operational results, not by model output alone.
• Post-launch operation: monitor deployed systems, adjust behavior as the workflow changes, and hand off cleanly once the system is stable.
• Reusable playbooks: turn what works in one portfolio company into patterns, components, and operating notes that shorten the next deployment.
• Technical trust: serve as the technical face of the company to portfolio company GMs and operations leaders.

You are likely a strong fit if you have:

• 3–6 years of experience in full-stack engineering, customer-facing product engineering, or forward deployed engineering.
• Fluency in TypeScript or Python, React, and SQL, with enough range to own delivery end to end.
• Shipped production LLM or agentic systems with evals, autonomy gating, and outcome tracking.
• Experience embedded with non-technical teams where trust had to be earned in the field, not in a design review.
• The ability to read a business process and translate it into a concrete technical wedge.
• Comfort reading a P&L or enough business context to identify where labor, error, or cycle time is expensive.
• Judgment to move forward with incomplete requirements without turning the deployment into a science project.
• Comfort being accountable for what happens after launch, not just what ships in week one.
• Willingness to work from New York, San Francisco, or Toronto, with regular travel to portfolio company sites.

• Core languages: TypeScript or Python
• Frontend: React
• Data: SQL
• Deployment shape: embedded integrations, agentic workflows, internal tools, and narrow operator-facing UI
• Working style: solo ownership of projects that cross product, engineering, and operations

The exact implementation will vary by portfolio company, but the standard is the same: ship something operators will use in production, then make it reliable enough to keep using.

The company has enough capital to invest in deployed software, not just one-off experiments. That matters because the value is in turning a successful workflow wedge into a repeatable deployment motion across the portfolio.

As the portfolio grows, the forward deployed function becomes a multiplier: one deployment informs the next, and the next one should land faster because the playbook got sharper.

This is the right time to join if you want to help define how agentic software gets shipped into real businesses, not just prototype it.

• You want a pure product engineering role with no customer exposure.
• You need fully specified tickets before you can start.
• You want to optimize for demos instead of deployed systems.
• You are not comfortable with travel or with being embedded on site.
• You prefer to stay inside one codebase rather than own the full deployment loop.

• Base salary: $185K–$240K
• Equity: competitive
• Employment: full-time
• Workplace: hybrid, with at least 3 days per week in office; 4 preferred
• Travel: 2–3 days per week of travel to portfolio company locations
• Visa support: available
• Relocation support: available
• Locations: New York, San Francisco, Toronto

• Are you able to work in New York, San Francisco, or Toronto and come into the office at least 3 days per week?
• Walk us through a time you shipped agentic or LLM-powered software in production. What did you build, and how did you handle evals and autonomy gating?
• Describe a situation where you embedded with a non-technical team to solve an operational problem. What was the problem, and what did you ship?
• Are you able to travel to portfolio company sites 2–3 days per week?

Aurora helps exceptional engineers find the right role at some of the most ambitious startups worldwide.

We work with teams that value high ownership, strong technical standards, and clear scope.