Member of Technical Staff – AI Agents & Internal Platforms

Morgan Pinnacle Group; New York (NY); On-site; USD 150,000 - 300,000; Full time

Benefits offered by this job

Equity: Highly competitive equity

Job summary

Morgan Pinnacle Group in New York, NY is seeking a Member of Technical Staff to advance AI agents and internal platforms. You will embed with teams to deploy intelligent workflows, build infrastructure, and push production-ready agents into real-world use.

Ideal candidates have 2+ years in software engineering, full-stack capabilities, and hands-on experience with AI agents, Agent SDKs, and LLM tools. This onsite role offers strong compensation and open visa sponsorship.

Qualifications

• 2+ years of software engineering experience.
• Experience building products as a full-stack engineer.
• Expertise in at least one full-stack language.
• Hands-on experience with AI agents and LLM tools.
• Strong product mindset and ownership.

Responsibilities

• Build AI agents that perform meaningful work end-to-end.
• Develop evaluation suites and monitor agent behavior.
• Iterate on agents as models improve.
• Build infrastructure and internal platforms for AI workflows.
• Create integrations for agents to access company context.
• Develop systems that keep knowledge synchronized with reality.
• Solve open-ended technical and product problems.

Skills

Software engineering

AI agents experience

Full-stack engineering

Agent SDKs / LLM tools

Ownership & autonomy

Tools

TypeScript

Python

Agent SDKs

LLM tooling

Job description

Member of Technical Staff – AI Agents & Internal Platforms

Location: New York, NY – Flatiron
Employment Type: Full-time
Work Arrangement: Onsite, 5 days/week
Salary: $150K–$300K
Equity: Highly competitive equity
Experience: 2+ years
Visa Sponsorship: Open to visa transfers, including OPT and H-1B transfers
Tech Stack: TypeScript, Python, AI Agents, Infrastructure

About the Role

What does knowledge work look like when we have AGI?

You'll join an internal team dedicated to finding the answer. This is a group of builders, former founders, and engineers who embed directly within teams to build intelligence deeper into their workflows.

As models continue to improve, they change how companies are built and how teams operate. This fast-growing AI company is working to build an AI-native company at scale, and this team is responsible for making that possible.

The team has already shipped agent teammates that push code directly to production, systems that keep company knowledge current automatically, and services that diagnose and repair themselves. These systems do real work today. Every advance in model capability opens another set of questions about how work should be organized—and another set of things to build.

The ambition is much larger than making each person 10% more productive. The team is building zero-human loops that handle entire jobs end-to-end and free people to take on higher-level work. Getting there means inventing new infrastructure, interfaces, operating models, and ways for humans and agents to work together.

The team began when the company had fewer than 40 people. Since then, the company has raised significant funding and has the resources, access, and mandate to pursue this work seriously. The company intends to grow at extraordinary speed, and doing so requires a fundamentally different operating model—one in which agents take on an increasing share of the work.

There is no established playbook for that. Building it is the job.

The Kind of Engineer Who Does Well Here

We hire individuals, not resumes. Given how fast things are changing, it's more important to hire the best people than to hire someone with one specific background.

First principles and systems thinking. Many of the problems we solve are net new. There's no playbook. We want people who can decompose a novel problem, reason from fundamentals rather than pattern match, and design things that compound.

Thinking beyond the engineering. The engineers who do best here understand the domain deeply: the data systems, the workflows, and the work the agents are performing. That understanding is what lets you design better systems, train better agents, and make better product decisions.

Ownership of outcomes. Projects have a single person accountable for whether they ship and whether they work. There are always more things to own than people to own them. Engineers own their own work.

Agency. You have the initiative to pick up the context you need to make good decisions—talking to the right people, reading the data, and understanding the problem deeply enough to know what matters.

Non-Negotiables

• 2+ years of experience building products 01 as a full-stack engineer
• In person in Flatiron, NYC, 5 days a week
• Excited about working with coding agents
• Excited about working at the frontier of applying agents to real-world problems

The team is particularly interested in engineers who are deeply AI-native, with hands-on experience building agents and working with Agent SDKs and LLM tools beyond basic chatbot implementations.

What You'll Work On

The work focuses on building AI agents and the systems needed to make them useful in real company workflows, including:

• Building agents that perform meaningful work end-to-end
• Developing evaluation suites and monitoring agent behavior
• Iterating on agents as models and capabilities improve
• Building infrastructure and internal platforms for AI-powered workflows
• Creating integrations and tooling that allow agents to access and interact with company context
• Developing systems that keep knowledge synchronized with reality
• Solving new, open-ended technical and product problems where established solutions may not exist

This is full-stack engineering, but the emphasis is on AI agents, platforms, infrastructure, innovation, and experimentation rather than primarily UI development.

What We're Looking For

• 2+ years of software engineering experience
• Experience building products from 01 as a full-stack engineer
• Expertise in at least one full-stack language
• Hands-on experience building AI agents and working with Agent SDKs and LLM tools beyond basic chatbots
• Strong product mindset and ability to think beyond the engineering itself
• Ability to work directly with users or internal teams, gather feedback, and iterate
• High tolerance for ambiguity and open-ended problems
• Ability to operate autonomously and take ownership of outcomesStrong first-principles and systems thinking

Experience as a founder, founding engineer, or software engineer at a fast-paced startup is particularly valued.

Nice to Have

• Experience at a fast-paced, VC-backed startup
• Founder or founding-engineer experience
• Interest in becoming a founder in the future
• Strong career progression and longer-term tenure with previous companies
• Undergraduate degree from a top global university

About the Company

This is a fast-growing, AI-native software company building AI agents that perform complex, high-stakes knowledge work.

Rather than replacing the software businesses already use, its agents operate within existing systems and complete complex tasks end-to-end. The company is deploying production-grade, long-horizon agents that do real work in the real economy today.

Founded in the last several years, the company has grown to approximately 120 employees and has raised more than $100M in funding, including a recent major funding round.

The team works together in person from its Flatiron, NYC office. The environment emphasizes high ownership, ambitious technical work, frequent feedback, and collaboration.

Why Join

This is an opportunity to work on a fundamental question:

How should companies operate when AI agents can take on an increasing share of knowledge work?

You’ll have significant responsibility from day one and work alongside engineers and builders tackling problems without established playbooks. The goal is not to add AI around the edges of existing workflows—it is to rethink what those workflows can become.

If the future of knowledge work is a problem you want to spend the next several years helping answer, this is an opportunity to make that answer real.