Who we are:
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.
About the role:
As an Embedded Research Engineer/Scientist at Percepta, you will advance the frontier of AI research while working directly with customers to drive real-world impact. You will work on post-training, reinforcement learning, agentic modeling, and specialized architectures but with a focus on translating research breakthroughs into production systems that transform how critical industries operate.
Unlike traditional research roles, you will be embedded with customers, working closely with their teams to understand business problems, design research strategies that address real needs, and rapidly iterate based on feedback from the field. You'll partner with our Embedded Product Managers, Research Product Managers and Applied AI Engineers to ensure your research creates measurable value and gets deployed into production.
What you'll do:
Work directly with customers to identify high-impact research problems at the intersection of frontier AI capabilities and critical business needs.
Design and execute research strategies across LLM post-training, RL, agentic modeling, and specialized model development that address customer-specific challenges.
Prototype and scale training pipelines, experiment with model architectures, optimization techniques, and post-training strategies with rapid iteration cycles based on customer feedback.
Conduct customer-facing evaluations at scale that demonstrate clear business value and drive adoption of AI systems in production environments.
Translate ambiguous business problems into concrete research questions, and translate research outcomes back into actionable insights for both technical and non-technical stakeholders.
Partner closely with Applied AI Engineers to transition successful research ideas into robust, production-ready features of our Mosaic platform.
Build trusted relationships with customer technical teams and executives, helping them understand how cutting-edge research can solve their specific problems.
Contribute to infrastructure for high-performance distributed training and establish reusable patterns that accelerate future research and deployment.
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.
Are comfortable implementing and debugging large-scale ML systems.
Can work directly with customers: translate business problems into research problems, communicate complex technical concepts to non-technical stakeholders, and iterate based on customer feedback.
Motivated by seeing your research deployed in production and creating value 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 and bringing structure to evolving requirements.
Are passionate about AI's transformative potential and bridging the gap between research and real-world deployment.
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 that excites you, let's chat, even if you don't meet all of the qualifications above.
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.