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Member of Technical Staff

Join Modus as a Member of Technical Staff to build AI-powered agents for financial audits in a fast-paced startup environment.

Location
New York
Compensation
$150k–$350k/yr
Level
mid
Type
full time · On-site

Posted by employer 7 months ago

First seen on Joblaze 1 week ago

Last verified on the company career page 4 hours ago

Apply at Modus → Save job Scanned from modusalliance.com

Role intensity

70% hands-on coding

AI in the day-to-day

You'll work directly with early customers to ship production-grade AI-powered agents.

Not disclosed in this posting: years of experience, visa sponsorship.

Benefits

Equity/Stock Options Health Insurance

Joblaze summary

In this role, the Member of Technical Staff at Modus will focus on building and deploying AI agents that enhance the efficiency of financial audits through document parsing and workflow automation. The position requires proficiency in Python and familiarity with AI frameworks, as well as a hands-on approach to developing both backend systems and user-facing tools. Ideal candidates are those with a strong track record in shipping AI and infrastructure features quickly, and who thrive in a fast-paced, collaborative environment. Modus is a growing startup backed by Lightspeed Venture Partners, emphasizing a culture of rapid iteration and real-world impact.

Joblaze insights

Quick facts

Is the Member of Technical Staff role remote?
No — this is an on-site role in New York.
What's the salary range?
Modus lists $150,000–$350,000 for this role.
Where is the role based?
Modus is hiring for this position in New York.
What's the tech stack?
Joblaze extracted these technologies from the posting: AI/ML, LLMs, Python, RAG, agent frameworks, browser automation.
What seniority level is this role?
Modus targets mid-level candidates for this position.
Is this full-time or contract?
Full-time for this Member of Technical Staff role at Modus.

From the original posting

About Modus

We’re building the future of audit.

Modus Audit is backed by Lightspeed Venture Partners to invest in and modernize accounting firms using Agentic AI. We streamline financial audits from the ground up — starting with real customers and real revenue. We've partnered with our first firm, and are rapidly growing our engineering team to help us build and deploy our agents at scale.

We’re a small, execution-focused team. If you're excited by fast feedback loops, messy real-world data, and applied AI with business impact — we’d love to talk.

Why this role matters

Our core thesis: audits should be faster, more accurate, and more scalable — and agents are the way to get there.

You’ll work directly with our early customers and founders to ship production-grade AI-powered agents. You'll design and own zero-to-one systems across LLM pipelines, document parsing, browser automation, and backend infrastructure. You’ll shape the product and the company.

What you'll do

  • Build and deploy AI agents that parse documents, extract entities, cross-check financials, and complete workflows end-to-end

  • Work across the stack — from Python backends and vector stores to browser-based agents and internal tools

  • Ship fast: iterate on v1s quickly, and come back to compound the wins

  • Own real outcomes with our design partners — your work will hit production

  • Evaluate and integrate emerging AI frameworks, tools, and best practices

  • Help us build the engineering culture you’d want to work in

What we look for

  • Strong builder instincts and end-to-end ownership

  • Track record of shipping AI/infra/backend features fast (Python preferred)

  • Experience or interest in LLMs, Agent Frameworks, RAG, browser automation, or doc parsing

  • Bias for action, debugging in prod, and learning from real-world usage

  • Not above any task — write the scraper, deploy the server, call the customer

Compensation

  • Competitive base salary ($150K–$350K depending on experience)

  • Meaningful early-stage equity

  • Full healthcare benefits

  • In-person office in NYC with team meals, fast iteration cycles, and real user exposure

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