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Share Technical Program Manager, End-to-End AI Systems Integration, DeepMind

Dscout

AI Research Engineer

San Francisco, CA, United States

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Job details

Posting language: English
Posted: Oct 29, 2026

Original job description

At Dscout, we’re building the most flexible and powerful UX research platform on the market - trusted by the world’s top brands in finance (JP Morgan Chase, Intuit, Charles Schwab, PayPal), healthcare (Aya, Headspace), consumer goods (Keen, Verizon, Target, Northface), and tech (Google, Amazon, Facebook, Meta, Spotify, AirBnB). Our tools help teams deeply understand the humans behind their products, so they can build better ones. We are expanding our smart and driven team and would love for you to join us.

Dscout AI Training & Evals

Dscout AI Training & Evals builds human data and evals that show whether AI works for real people. For ten years Dscout has captured how people behave in real context, with consent and scientific rigor. AI Training & Evals points that engine at AI: consented human behavior and judgment, turned into the training data, evals, and benchmarks AI teams trust.

Capability has outrun trust. Models ace benchmarks and still surprise people in the wild. A benchmark says how capable a model is. It does not say whether the model works for the person using it, across weeks, in the mess of real life. That gap is where we work.

We serve AI researchers, the product builders who ship alongside them, and the research teams who partner with both. We are a new unit inside an established company: live customers, a mature platform (React, Elixir, Python, Swift, Kotlin; SOC 2 and HITRUST certified), a small senior team, and room to build. We work AI-native. Agents write code, evals gate changes, and we aim to do in months what used to take years.

A note from our Chief AI Officer & GM. The questions that matter most about AI are not answered by a bigger leaderboard. Does a model work for a real person across three weeks of real use? Does a rubric measure what it claims? Does an AI judge agree with the people it stands for? We have a decade of methods for studying people in real context, and we will publish our methods and benchmarks so the field can check our work. Join us to do rigorous science that matters.

Why this seat

Work on a problem the field has not solved: measuring whether AI works for real people, over real time.
Study something most evals do not have: real participants, doing real tasks in their own context.
Own the benchmark. You are founding it, not joining a team that has one.
Publish. Methods and benchmarks go to the research community, with first authorship on the benchmark.
Join a small senior team with direct access to the Head of AI Research and the GM.

Job Description

This is a research-first role focused on questions where the right rubric, benchmark, or method does not yet exist. You will find the gaps, propose bold directions, and take projects from first hypothesis through experiments and benchmark construction to publication. You will also build: the science has to run inside live customer programs.

What you will do

Rubrics and QA science

Design rubrics and test whether they measure what they claim: validity, reliability, and sensitivity.
Measure how reliable human raters are, and when disagreement is noise versus real signal.
Calibrate AI judges against real human judgment, and study when they drift away from it.
Build quality signals that put evidence behind every data point we ship.

Long-horizon, real-user benchmarks

Design Dscout’s first benchmark: real users, in real context, over long horizons. Multi-week tasks across tools, not one-shot prompts.
Define the protocol, task design, scoring, and release. Work with our delivery team on fieldwork.
Work out how to keep a living benchmark honest over time: drift, contamination, difficulty, and what counts as a meaningful change.
Write the paper and own the public release.

Human judgment as training and eval signal

Study when real-world human feedback improves a model and when it adds noise.
Explore how preference and judgment data from representative people differ from expert data.
Turn findings into methods our forward deployed engineers and delivery team can run in the field.

New research directions

These areas are the core, not the whole list. You will have significant latitude to propose new programs, such as calibrating user simulations against real behavior, human-AI collaboration, and behavioral evaluation of agents.
Co-author with outside labs and academics on studies that need real-user data. Help run our paper-reading group.

A good first year

A QA and rubric framework running on live customer programs, with documented validity.
Benchmark v0 released, with a paper submitted to a top venue.
At least one external team using or citing our work.
Delivery colleagues using your methods without needing you in the room.

How we will hire: a conversation with the Head of AI Research; a research talk on a project you led; a design exercise (how would you validate a rubric for a long-horizon task?); a conversation with the GM, forward deployed engineers, and delivery partners.

Requirements

What we care about most. A strong publication record helps. We care most about whether you can find the question that matters, design a rigorous way to answer it, and move fast enough that the answer is still useful.

What you bring

Published research, or strong preprints and workshop papers, in evals, preference learning, reward modeling, human-AI interaction, measurement, or agent evaluation.
A PhD, a PhD in progress, or an MS with two or more strong research internships at labs or top industry groups. If you do not have advanced degrees but have a history of creative machine learning research work, this is just as desirable.
Strong applied statistics: inter-rater reliability, confidence intervals, power, and a nose for when a metric misleads.
Hands-on experience with LLM-as-judge, rubric-based evaluation, and preference data.
Strong Python, and the ability to build research prototypes and evaluation pipelines other people run. You use AI coding tools as a real part of your workflow.
Experience running experiments on messy human data, not only curated datasets.
Clear technical writing, and the ability to explain your work to a lab researcher and to a product manager.
Based in the Bay Area.

Nice to have: long-horizon or agentic evaluation work; psychometrics, item response theory, or human-subjects research; open-source benchmark or dataset contributions.

Compensation: $250-400K OTE + Equity. Includes a built-in bonus and equity program, publication freedom, a conference budget, flexible PTO, 15 holidays, 12 weeks of paid parental leave, 401k match, and an education stipend.

This is an ideal list. If the role excites you and you do not check every box, we would still love to hear from you. Of course, what is outlined above is an ideal set of expectations; however, business needs and other projects and tasks may shift, and additional tasks could be assigned at the discretion of your manager.

About Dscout

Dscout is a team of passionate, empathetic, and curious professionals. As a recognized leader in the Forrester Wave, we’re at the cutting edge of experience research technology. The power of research drives us - how in-context insights from real people can build more enjoyable products and services.

We prioritize learning, sharing, and building. We also deeply value being a diverse and inclusive team and company and look for team members who align with that belief. Join our dynamic team and help shape product roadmaps and business strategies for the world’s most loved brands.

It doesn't stop there. When you join the Dscout team, you will get:

A strong and competitive compensation package with a built-in bonus and equity program.
An incredible and progressive benefits package (for both you and your dependents) to support work/life balance, including flexible PTO, 15 company holidays, 12 weeks of paid parental leave, 401k match, and much more.
An education stipend to support your growth & development, and a remote work stipend.
A company that is open and transparent with our team. You will know what is happening and why it matters.

Dscout is an equal-opportunity employer that values diversity. We do not discriminate based on identity, including race, color, religion, national origin or ancestry, sex, gender identity and expression, age, physical or mental disability, pregnancy, veteran or military status, unfavorable discharge from military service, genetic information, sexual orientation, marital status, order of protection status, citizenship status, arrest record or expunged or sealed convictions, or any other legally recognized protected basis under federal, state, or local law.

If you need reasonable accommodations for any part of the employment process, please email us at accommodations@dscout.com with the nature of your request and your contact information. We'll do everything possible to ensure you're well-prepared for success during our interview process, while also upholding your privacy, including accommodating any special requests. Please note that only inquiries regarding requests for reasonable accommodation will be responded to from this email address.

When you apply at Dscout, we will process your job applicant data, including your employment and education history, transcript, writing samples, and references, as necessary to consider your job application for open positions. For more information about our privacy practices, please visit our Privacy Policy.

Dscout participates in the E-Verify program in certain locations, as required by law.

Location Flexibility: Dscout is proud to support a remote-first workforce and enable employees to work from almost anywhere. At this time, however, we are unable to hire in the following locations: Montana, Hawaii, Alaska, and Washington DC.

NOTE: DSCOUT NEVER CONTACTS JOB APPLICANTS VIA TEXT, MESSENGER, OR OTHER SIMILAR APPLICATIONS. BE AWARE OF PHISHING AND SPOOFING SCAMS, BOTH VIA TEXT AND EMAIL. ONLY RESPOND TO EMAILS FROM DSCOUT.COM

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More about this job

Skills

Research
PayPal
Collaboration
Machine learning
Statistics
Python
Workflow
Technical writing

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