About the Role:

We're looking for a highly organized and versatile operator to join our founding team as Head of Operations. In this role, you'll be the operational backbone of the company, managing customer proposals and sales operations, strategic partnerships and communications, team coordination, and everything that keeps the lights on. Ideally, you can build AI automations and processes to make the team more efficient over time.

Sales & Customer Operations: You'll write compelling proposals for enterprise customers (critical to closing deals), manage client relationships, and handle legal documentation and contract management.

Company Operations: You'll manage day-to-day finances, handle HR administration, coordinate team operations, and support talent acquisition—keeping all the systems running that enable the team to focus on building.

Strategic Projects & Communications: You'll develop proposals for research partnerships and funding opportunities, and manage relationships with strategic partners and collaborators.

This role is perfect for someone who thrives on variety, can context-switch seamlessly, and finds satisfaction in both closing customer deals and ensuring the team has what they need to do their best work.

Requirements:

• You'll be writing customer proposals, partnership applications, and documentation that represent the company. Your writing needs to be clear, compelling, and professional.

• You're comfortable talking to enterprise customers, understanding their needs, and positioning our product effectively. Bonus if you've written proposals or managed pilots.

• You can manage multiple workstreams simultaneously from taxes to customer meetings to vendor relationships—without dropping balls. You're naturally organized and create systems to stay on top of everything.

• You don't wait to be told what to do. When you see something broken or inefficient, you fix it. When a customer needs something, you make it happen.

Nice to haves:

• Ability/interest in developing AI automations.

• Experience working in analyst, consulting or strategy roles.

• Technical understanding of what we are building.

About Us

Measuring model ability is the most challenging part of creating applications that are capable of automating any given part of the economy. There are no good techniques or benchmarks for evaluating LLM performance on business-relevant tasks, so adoption for enterprise production settings has been limited (see Wittgenstein’s ruler).

This problem materializes in each place where LLMs have potential: in understanding whether the AI tool companies are building a product will satisfy a customer demand, determining how feasible models and vendors are for a given enterprise in making purchasing decisions, for researchers who need a north star to which to expand model ability.

Today, answering these questions amounts to hiring a human review team to manually evaluate model outputs. This is prohibitively expensive and slow.

Vals AI is building the enterprise benchmark of LLM and LLM apps on real-world business tasks. In doing so we are creating the infrastructure + certification to automatically audit LLM applications, verifying they are ready for consumption.

See our benchmarks and launch announcement in Bloomberg. We aim to build the barometer for whether AI is useful, and in doing so, accelerate the automation of all knowledge work.

What we are building:

Our core technology enables us to review + automatically audit LLM applications in high-value industries (legal, insurance, finance, healthcare). With this and our own data, we maintain a public benchmark of the major LLMs on enterprise tasks. Our success will be based on three components:

1. Our evaluation performs at human-level accuracy on the relevant axes for each industry/application.

2. Our platform has an intuitive interface that acts as a shared platform between human reviewers and engineers.

3. We become the industry-standard benchmark, maintaining a loss-leading effort by publishing free reports and collaborating with credible data partners.

To achieve each of these, we are looking for machine learning engineers (Head of AI, Members of Technical Staff) to develop novel evaluation techniques, strong designers and front-end engineers (Founding Product Engineer) to contribute to the platform, and a tenacious operator to write reports and maintain our social media (email rayan@vals.ai if this is of interest).

What we offer:

• Highly competitive salary and meaningful ownership. Excellence is well rewarded.

• Relocation and transportation support.

• Health/dental insurance coverage. 401k

• Lunch and dinner provided, free snacks/coffee/drinks.

• Unlimited PTO.

About us:

Founding team: The core methodology behind this platform comes from NLP evaluation research we had done at Stanford. We raised a 5M seed from some of the top institutional and angel investors in the valley. Our team has prior work experience at NVIDIA, Meta, Microsoft, Palantir and HRT. Collectively, we have over 300 citations in our published work.

Tech stack: Our frontend is built in React with TSX. We use Django as our back-end framework. All of the infra is on AWS.

What we’re looking for:

• Intelligence is more important than a good-looking resume. Industry experience and pedigree valuable only insofar as it is a proxy for talent itself.

• Ownership to create products. We don’t have the scale or time to actively “manage” every project or task. Working in a small, talent-dense team, we expect everyone to show initiative to build where it’s needed, not where it’s asked. We strive for autonomy over consensus.

• Intensity. The LLM landscape is constantly changing. Foundation model labs are continuously pushing the frontier, enterprises are seeing massive pressure to adopt technology, startups are hungry to chase the white space. The unicorn companies that will emerge from this technology shift are being built now. Those that win will have an incredibly high speed of execution.

• See solutions not problems. We’re not looking for people that pass hard problems to others or admit defeat, but instead only see the opportunity to craft solutions at each juncture.

Further Reading:

• Hugging Face blog on evaluation

• Anthropic’s blog on challenges in evaluation

• New York Times article on issues in benchmarking

• Stanford HAI report showing hallucinations in legal tech tools

Referral Bonus

Know someone who would be a good fit? Connect them with rayan@vals.ai. If we hire them and they stay on for 90 days you’ll get a $10,000 referral bonus and Vals AI merch!