Research Engineer

The Impact You'll Make
Our
research team is expanding to keep pace with a wave of frontier-facing work:
internal research streams, client engagements that require real ML depth, and
emerging opportunities at the cutting edge of the field. As a Research
Engineer, you'll take a research direction and run with it - finding the right
papers, benchmarks, and prior work, reimplementing what's relevant, and
building out the process to reproduce and improve on it internally.

You'll own initiatives end to end: partnering with strategic project and
technical leads to scope the work, building MVPs to validate ideas (including
through human annotation and agents), and turning that work into something
concrete - a customer dataset, a pilot, an internal dataset that becomes a
paper or blog post, or a joint publication with a partner. You won't be handed
a fully specified task list; you'll be given a direction and the autonomy to
turn it into a research plan.

This is a full-time, hybrid position based in San Francisco.

What You'll Do

·         Take
a research direction and independently identify supporting resources - papers,
benchmarks, blog posts - then implement or reimplement the relevant methods

·         Build
and own the process to reproduce prior work internally and identify ways to
improve on it

·         Own
projects (for example, an RL/agentic environment build for a partner or a novel
multimodal benchmark) end to end, including scoping, MVP implementation, and
validation

·         Partner
with strategic project leads and technical leads to translate ambiguous
requirements into a concrete, testable research plan

·         Validate
ideas through hands-on implementation, including annotating, evaluating, or
sourcing data

·         Turn
research directions into tangible outputs - a paid customer dataset, a customer
pilot, an internal dataset, or a paper/blog post for publication or conference
presentation

·         Bring
an ML perspective to new opportunities — assessing technical feasibility of
incoming requests and helping shape proposals where research depth is needed

What You'll Bring

·         MS
or PhD in ML, CS, or a related quantitative field - or equivalent demonstrated
research experience (publications, significant open-source research work,
industry research)

·         Real
ML depth: you understand how models are trained and evaluated, not just how to
call an API. You can read a paper, judge whether its claims hold, and
reimplement the method

·         Hands-on
experience with at least one of: RL/agentic systems, AI/ML evaluation and
benchmarking, or multimodal ML

·         Strong
Python and the engineering ability to build and ship your own experiments -
eval harnesses, environments, infrastructure - without relying on a platform
team

·         High
autonomy: you can turn an ambiguous direction into a concrete research plan and
notice when something's off before being told

·         Clear
technical writing

Nice To Have

·         Publication
track record (first-author preferred)

·         Experience
with agent or multimodal benchmarks (OSWorld, MMMU, WebArena, SWE-bench, or
similar) or building RL environments/gyms

·         Familiarity
with reward modeling, reward hacking, or verifier/judge reliability

·         Familiarity
with synthetic data generation or human-in-the-loop (HITL) workflows

·         Experience
with cloud infrastructure and containerized environments

Requirements