The posting
ABOUT GENERALIST
At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done.
We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world.
The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, https://research.google/blog/palm-e-an-embodied-multimodal-language-model/ RT-2 https://deepmind.google/blog/rt-2-new-model-translates-vision-and-language-into-action/, Gemini Robotics https://deepmind.google/models/gemini-robotics/), launched and scaled ChatGPT https://chatgpt.com/ and GPT-4 https://openai.com/index/gpt-4-research/ to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas https://bostondynamics.com/atlas/, Spot https://bostondynamics.com/products/spot/, Stretch https://bostondynamics.com/products/stretch/) and pushed the limits of what they can do (from parkour https://www.youtube.com/watch?v=tF4DML7FIWk to manipulation https://bostondynamics.com/blog/large-behavior-models-atlas-find-new-footing/, and testing robustness https://www.youtube.com/watch?v=aFuA50H9uek).
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
THE ROLE
We are actively building our teams across research science, ML infrastructure, robotics controls, hardware, data operations, and go-to-market. Most of these roles are senior, and we focus heavily on reaching talented professionals who may not be actively job-seeking.
This role is about creating thoughtful talent strategies and intentionally mapping candidate ecosystems rather than simply focusing on volume.
Partnering closely with our Head of Talent Acquisition, you will help design and scale our hiring engine. For any given domain, you will develop a deep understanding of key contributors, influential research, impactful engineering work, and upcoming career milestones. You will bring these insights to hiring managers with a clear perspective to help attract and engage top talent.
Equally important is your internal partnership. Our hiring managers are primary researchers and engineers who value efficient, data-informed guidance. By sharing clear pipeline metrics, channel insights, and candidate feedback, you will build strong trust with technical leaders and continuously refine our hiring practices.
As one of the initial members of our talent team, you will have a unique opportunity to lay the groundwork, lead priority searches, and shape our overall recruiting methodology from the ground up.
WHAT YOU'LL OWN
- End-to-end search ownership. Manage intake, strategy, sourcing, screening, loop design, closing, and seamless handoffs to onboarding across key critical searches.
- Proactive talent mapping. Map target domains early to identify prospective candidates thoughtfully, especially in areas where our network is still expanding.
- Targeted sourcing. Explore publications, conferences, repositories, and referral networks to craft meaningful, highly personalized candidate outreach.
- Modern tooling & AI integration. Leverage AI tools for research, enrichment, and workflow enhancement while maintaining high accuracy and sound quality standards.
- Data-informed insights. Track pipeline conversion, channel effectiveness, and feedback loops to guide team decisions and optimize our overall strategy.
- Hiring manager partnership. Collaborate closely on role scoping, calibration, interview loops, interviewer preparation, and debrief facilitation.
- Candidate engagement & closing. Articulate our mission, technical scope, and team density to effectively invite candidates to join our growth journey.
- Structured operations. Maintain clean data, reliable scheduling, and structured interviewing practices within Ashby to ensure an efficient experience for everyone.
- External partnerships. Collaborate with and manage external agencies effectively to supplement internal sourcing capabilities as needed.
- Multi-site collaboration. Support in-person hiring efforts across our San Francisco and Boston locations, including occasional site visits and key industry events.
MINIMUM QUALIFICATIONS
- 5+ years full-cycle technical recruiting,
- A track record of closing senior technical hires who had competing offers — with specifics you can walk us through.
- Real passive-sourcing depth. You have built named-target searches from a blank page in a domain you did not previously know, and hired from them.
- Data fluency. You can build a funnel, find the leak, and use the result to change a hiring manager's mind. Comfort with ATS reporting and with a spreadsheet you built yourself.
- Working, current use of AI tooling in your own workflow, with judgment about where it helps and where it fabricates.
- Exceptional written communication. Outreach, briefs, and internal recommendations that get read and acted on.
- Genuine technical curiosity. You do not need to have trained a model, but you should want to know why one architecture beat another, and you should be able to read an abstract and work out whether the author is relevant to us.
- Strong relationship management under pressure — candidates, hiring managers, and agencies — while holding a bar.
- Based in San Francisco or Boston, in person.
PREFERRED QUALIFICATIONS
- Hiring for a frontier research organization or AI lab: research scientists, pre-training and post-training, RL, ML infrastructure.
- Robotics, autonomous vehicles, or hardware — anywhere the physical world constrains what the software can do.
- Experience as an early or founding recruiter, where you built the function and not just the pipeline.
- Ashby, or fluency in a comparable structured-interview ATS and a view on what makes one good.
- Agency or search-firm training early in your career, combined with in-house judgment later.
- Familiarity with both the Bay Area and Boston markets, which behave differently and are not staffed the same way.
