Research Engineer/Scientist (LLM Modeling) Percepta's mission is to transform critical institutions with applied AI. We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology. To make that happen, we embed with industry-leading customers to drive AI transformation. We bring together: Forward-deployed expertise in engineering, product, and research Mosaic, our in-house toolkit for rapidly deploying agentic workflows Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day to day lives. Percepta is a direct partnership with General Catalyst, a global transformation and investment company. As a Research Engineer/Scientist (LLM Modeling) at Percepta, you will advance the frontier of large-scale language model building and deployment. You will work on pre-training, post-training (e.g., instruction tuning, alignment, distillation), reinforcement learning (RL), and the design of specialized architectures to push the boundaries of reasoning, decision-making, and adaptability in critical industries. You'll collaborate closely with our Embedded Product Managers (EPMs) and engineers to ensure our most sophisticated decision systems are both innovative and pragmatically useful in transforming how companies operate. Identify impactful problems and design research strategies across pre-training, post-training, RL, and specialized model development. Prototype and scale training pipelines for large language models, experiment with model architectures, optimization techniques, and post-training strategies. Contribute to infrastructure for high-performance distributed training. Conduct in-the-wild evaluations at scale that drive millions of dollars in value. Partner with our applied AI engineers to transition successful research ideas into robust features of our Mosaic platform. Communicate research outcomes to both technical and non-technical stakeholders, making sure everyone understands the "so what" of research and how to apply it. You may be a good fit if you: Have an MS/PhD in Computer Science, ML, a related field, or equivalent experience. Have depth in LLM and ML fundamentals from optimization to large scale training to Reinforcement Learning Are comfortable implementing and debugging large-scale ML systems. Are motivated by impact in critical industries including healthcare, supply chains, energy, and finance. Have a proven track record of execution. Are an excellent communicator with both technical and non-technical stakeholders. Enjoy extreme ownership. Are passionate about AI's transformative potential. We're working against an incredibly ambitious mission. It won't be easy, but it will likely be the most fulfilling work of your career. If this excites you, let's chat, even if you don't meet all of the qualifications above.
Our Values:
Dream bigger: We have the unique privilege of taking on the most ambitious problems and we should chase them with optimism, responsibility, and genuine belief that we can make it happen. We have to embrace the hard things when no one else will. Heart in the game: What we're doing matters and we have to give a shit. Internally, that means fixing badness when you find it. Externally, it means honoring the trust our customers place in us with their most important problems. This isn't a 9-5, nor is it a job we're ever going to monitor your hours. We promise to put work in front of you that matters and in return, we ask you to promise to care. Win for the customer: Everyone is an engineer and the job of an engineer is to deliver outcomes, not outputs. Everything we do—the products we build, the partnerships we launch, the strategy we set—exists to make our customers successful. Delivery is the strategy. Make the call: Organizations are only as strong as the pace at which they make decisions. Everyone at Percepta should feel empowered to commit and shape the ambiguity in front of them. But "make the call" cuts both ways: make the decision and make the phone call. High-agency decision-making only works with high-bandwidth communication and we commit to never operate in silos. Intensity with kindness: We believe in excellence in execution, candor in feedback, ruthlessness in prioritization, and survivalist urgency. We also believe you don't need to be an asshole to deliver on any of this. The trust built through shared kindness and vulnerability is what makes the intensity sustainable.
