Responsibilities

Applied Scientists own the mastery of new frontier model capabilities. This includes developing post-training recipes for learning a wide range of dexterous tasks — from pushing the frontiers of science lab automation, to generalizing manufacturing and warehouse logistics, to designing open-ended evaluations that challenge what the models can do, and more. You are responsible for hill-climbing broad sets of manipulation capabilities until they are robust, general, and reusable across multiple applications. This role is a hybrid of research, engineering, and operations, and we expect excellence in all three areas.
This is an empirical, model science-first role. You will define what "good" looks like for a capability, build the evaluations that measure it, run experiments across model variants and data recipes, analyze the results, and drive the post-training loop. You will leverage post-training, data, task design, and rigorous experimentation, to compound model capabilities: anything mastered for one application can become a strength of the foundation model for entire industries.
You'll be responsible for:
Owning end-to-end improvement of target capabilities, from baseline performance to mastery
Designing task suites and evaluation protocols that faithfully capture what research, science partners, and customers need, and that generalize beyond any single deployment
Coordinating data collection, training and fine-tuning models, sweeping data mixtures and configurations, and orchestrating large-scale evaluations
Running rigorous experimental analysis across model variants — isolating variables, quantifying statistical confidence, and diagnosing failure modes from rollout data
Using the stack in new ways rather than redesigning it — and when a capability is fundamentally restricted, you'll work with the pretraining and post-training teams to improve the model
You might thrive in this role if you:
Are comfortable with data analysis at scale: slicing roll out logs, computing metrics, visualizing results, and investigating what happened and why
Care deeply about evaluation, benchmarking, and failure analysis — and are continuously diligent in the face of seemingly repetitive, but subtly changing task evaluations
Enjoy rapid iteration with real-world feedback loops, and are willing to get hands-on with robots and physical task setups when the experiment demands it

Applied Scientist at Generalist · LumiJobs

Applied Scientist

Responsibilities

Benefits

Company

Generalist

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