Member of Technical Staff - Post Training

Description

About Warp

At Warp Sciences, our mission is to help the world discover new medicines faster—and build the capacity to do it here in America. We bring AI, robotics, and life sciences together in automated labs that design, run, and learn from experiments, with each experiment making the next one better. We’re not developing drugs of our own. We’re building the infrastructure for the people who do.

Warp is looking for a post-training research engineer or scientist who has worked at the frontier and knows how to turn strong models into better products.

Our AI stack spans local robotics and perception models, cloud foundation models, and the runtime connecting them. You’ll own post-training work that improves these systems’ capabilities, reliability, and performance in real use.

The most important qualification is a track record of meaningful results: you’ve built systems that support rapid, rigorous experimentation, achieved convincing state-of-the-art improvements, and shipped those improvements into real products. You lead with evals, treat data as a core part of the work, and bring deep hands-on experience with reinforcement learning.

Responsibilities

• Own the post-training loop from identifying a capability gap through evaluation, data development, training, deployment, and production feedback.
• Build evals grounded in product needs, with clear baselines and held-out tests that distinguish real progress from benchmark overfitting.
• Build experimentation systems that enable fast, reproducible hill climbing and make results easy to compare and diagnose.
• Develop and improve RL training methods, reward signals, rollout generation, and supporting infrastructure.
• Curate, generate, and refine training data based on model failures, evaluation results, and product feedback.
• Improve model behavior across Warp’s robotics and foundation-model stack, working closely with robotics, infrastructure, and product teams.
• Validate gains through ablations, robust comparisons, and testing beyond the training distribution.
• Ship improvements, measure their impact in production, and use what you learn to drive the next iteration.

Requirements

• Experience working at the frontier of model capabilities, with meaningful personal ownership of post-training results.
• A demonstrated ability to achieve state-of-the-art results and substantiate them with rigorous evaluation against strong baselines.
• Deep practical experience with reinforcement learning, including reward design, rollout systems, training stability, and diagnosing failure modes.
• An eval-led approach: you define what success means before optimizing and scrutinize whether measured gains reflect the intended behavior.
• Strong data intuition, backed by experience identifying, creating, and selecting data that produces measurable improvements.
• Strong engineering skills and experience building reliable training and experimentation systems.
• A track record of shipping model improvements into real products and learning from deployment.
• An outcome-driven mindset: you follow the evidence, move quickly, and take responsibility for whether your work improves the product.
• Helpful, but not required
• Experience with robotics, embodied AI, or learning from interaction.
• Experience post-training multimodal, perception, or action-generating models.
• Experience balancing model quality with latency, compute, and deployment constraints across local and cloud environments.
• What success looks like
• You establish a credible baseline, identify the failures that matter, and build a repeatable loop that improves them. Your results hold up under rigorous evaluation and translate into better behavior in Warp’s products. Each deployment teaches us something that makes the next iteration stronger.