AI Software Engineer | Mash | Knowledge Agents Artificial Intelligence

The Problem

We’re lazy.

We hate doing the same thing over and over again. We hate doing work that can and should be automated. We’ve suffered through it. We don’t even want to search for an answer – we want to be proactively told the solution.

The fastest-growing technical companies have a critical group of people that are dealing with this problem – the sales engineers, solution architects, customer success engineers and everyone they serve on revenue teams.

They’re inundated by questions from colleagues & customers. They are stuck filling out similar security questionnaires & RFPs. They spend hours writing “handoff” docs and completing prep for meetings. They’re still waiting for an answer that they asked a colleague yesterday.

This wasted time is expensive and makes providing great experiences to customers and prospects unnecessarily challenging.

This is what we’re solving. At Mash, we’re building the tools to help revenue teams at technical B2B companies scale the tier-one service they provide to customers and prospects. From an encyclopedic knowledge engine that pulls in information from all the scattered, siloed and messy sources, to the automation agents that proactively help before they know they need it.

Help us help them… be lazy!

About Mash

We’re a dynamic team with experience at startups and big tech, including Google, Amazon, PagerDuty, Kobo, Wattpad, and OpsLevel. We don't use any seniority titles, and plan not to for as long as feasibly possible (learn more why).

Mash has ample runway, having raised a US$6M seed round from top VCs and angels. The round was co-led by Whitecap Venture Partners and Castle Island Ventures, with participation from Maple VC, Strategic Cyber Ventures, Aquanow, Spacecadet Ventures, and angel investors including Amjad Masad, Balaji Srinivasan, Austin Hill, and John Pfeffer.

Our Stack

Application: Python, FastAPI, TypeScript, Tailwind CSS, NextJS.
Infrastructure: GCP, Weaviate, PostgreSQL, Terraform, Docker.

Role & Responsibilities

As an AI-focused software engineer on the Mash team, you will:

Ship core AI features within the Mash platform.
- Architect LLM-powered solutions and manage related infrastructure.
- Apply information retrieval techniques for search and retrieval-augmented generation (RAG), working with embedding models and vector databases.
- Develop processes and tooling for measuring and iterating on LLM output quality, leveraging eval frameworks and observability platforms.
- Create tooling to collect and leverage internal datasets for evaluation, few-shot prompting, fine-tuning and more.
- Develop bespoke ML/NLP models, when appropriate.
Build production data pipelines to ingest knowledge sources.
Stay up to date on cutting-edge research in the ML/NLP space and identify opportunities to apply new techniques within the Mash platform.
Work closely with a cross-functional team (product, design, engineering).
Discover and understand user needs.

Desired Skill Set

4+ years of experience in at-scale software development with a focus on AI.
- Expertise building production systems with LLMs and/or traditional ML/NLP models.
- Experience with information retrieval techniques and/or vector databases.
Proficiency in 1+ programming languages, such as Python, Go, Rust, C++, or Java.
Expertise in working with data (both structured and unstructured) and developing data pipelines.
Experience building cloud-native software (with cloud providers such as Google Cloud Platform, Amazon Web Services, Microsoft Azure, etc.).
Strong understanding of backend system design and scalable architectures.
Bonus: Experience with eval frameworks and knowledge of best practices for LLM evaluation and observation.

Cultural Fit

We treat your time as the scarce asset that it is. We respect people being heads-down and having the time to get into a flow. Distractions and context switching are things we work to minimize. We have meetings for specific purposes, and leverage async channels when appropriate.

Mash has an open and direct culture. We believe that not making a decision is a decision – and a terrible one at that. We have strong conviction and a bias for action. We can agree to disagree, and move forward after reviewing the details. As we learn, we will reevaluate, iterate and push forward.

We’re looking for an individual who:

Responds well to open-ended problems and thrives on autonomy – is a self-starter and problem solver.
Thrives in collaborative environments and is laser-focused on team goals.
Has great communication skills.
Has a passion for lifelong learning.

How to Apply

Please reach out to us at careers@mash.com.
Include a bit about yourself, your resume, and three bullet points about why you are specifically interested in the position.