Technical Program Manager, RL Research

About the company the company’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Team Our Reinforcement Learning teams are central to advancing our AI systems, contributing to every Claude model and driving the autonomy and coding gains in our latest releases. The work spans computer use, code generation through RL, fundamental RL research for large language models, scalable infrastructure and training methodologies, and model reasoning. Research TPM team supports the full model development lifecycle, from pre-training through post-training, operating at the frontier of AI development. About the Role As a Technical Program Manager on the reinforcement learning team, you will own the systems and programs that determine how fast our research moves: a trustworthy read on the state of RL research, the review and prioritization processes that turn that read into critical decision for production RL runs. Strong candidates should have an ML engineering or research background and have grown into program leadership. You'll need real technical depth: the ability to debug data pipelines, read RL transcripts to spot issues, and make allocation and quality decisions in real time when research or production runs hit problems. You'll need organizational effectiveness in equal measure: the ability to navigate a fast-growing organization, quickly identify the critical people and teams across research, infrastructure, product, and data operations, and coordinate across them without losing velocity. Join us in our mission to build AI systems that are safe, reliable, and beneficial to humanity. Responsibilities Deliver a regular read on the ground truth in RL research, covering performance against baselines, experiment results, day-to-day health, and incidents Work with RL org leads on prioritizing, ranking, and tracking the state of experiments Drive research reviews end to end in partnership with set the agenda, make sure the right context is in the room ahead of time, and close the loop on what gets decided Establish processes and frameworks that bring structure to an unstructured research setting without slowing researchers down Collaborate with research leads, infrastructure engineers, and data operations to identify blockers, prioritize competing needs, and make technical trade-off decisions You May Be a Good Fit If You Have a background in ML engineering or ML research before transitioning to technical program management Have deep, hands-on experience with ML training pipelines, RLHF systems, and large-scale data infrastructure in production Have a track record of building execution plans and inventing high-leverage processes that reduce operational overhead and let researchers focus on research Are a fast learner who builds deep contextual understanding in unfamiliar technical domains and can contribute meaningfully to discussions with researchers Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in a fast-moving research setting Have excellent stakeholder management and communication skills, with the ability to influence senior technical staff through clarity, competence, and consistent delivery Are excited about pushing the frontier of what RL can do at scale The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary:

$365,000—$435,000 USD

Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. How we're different We believe that the highest-impact AI research will be big science. At the company we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to the company, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problem #J-18808-Ljbffr United States Digital Space LLC