About the Role:

We are looking for a founding engineer to have ownership of the public reporting and core evaluation methodology. We expect you to have top technical ability, high autonomy, and the dynamism necessary to adapt to a changing research landscape.

We aim to build a small applied AI lab within Vals AI. You will be the first hire and leader of that, eventually assembling a world-class team of MLEs. You will track the latest developments in research and implement that methodology as it improves the accuracy of our evaluations.

We are building the standard for public evaluation of LLMs in enterprise tasks and you will direct that effort.

Responsibilities

The Head of AI responsible for furthering our public benchmarks. The areas you will investigate are (1) methods for reliably/accurately evaluating generated text at scale (2) training an expert reward function and judge (3) generating synthetic evaluation datasets. This domain is wide open with many challenging problems that are crucial to solve correctly.

It’s worth highlighting that in this role you are a founding team member, not an employee. This means you have ownership in the company and product direction. You don’t take orders, you engage in discussions. You will have resources at your disposal to support your work. This includes the ability to contract experts for data labeling (we have existing partnerships which enable this) and to hire additional researchers or engineers.

Requirements:

• MS/Phd in Computer Science, Artificial Intelligence, Math, Physics or related.

• 2+ years of prior experience in NLP, building and shipping machine learning infrastructure in industry.

• Strong experience with Python, especially in production settings.

• Experience working in teams. This includes working in development sprints, knowledge of best-practices in working with Git, reviewing pull requests.

• Strong communication skills. You can provide input to others and equally receive/integrate feedback.

• A tenacity to iterate and develop quickly.

• We are an in-person team, based in San Francisco. We will support your relocation or transportation as needed.

Nice to haves:

• Research experience with papers published in reputable journals.

• Experience hiring + building a team of MLEs.

• Experience working with Django or other Python-based HTTP servers (e.g. Flask).

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, Member of Technical Staff ) to develop novel evaluation techniques, strong designers and front-end engineers (Founding Engineer (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/vision/dental insurance coverage.

• 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 10k referral bonus and Vals AI merch!