Lead AI Researcher

OutSystems•San Francisco, CA

25d•Onsite

About The Position

There are NO limits to your career: come shape the future and be part of a truly unique global culture at OutSystems! ### Join the AI Research & Solutions Group We're looking for an engineer who builds the models, not just uses them. Someone who has trained neural networks from scratch, constructed datasets that actually work, and implemented papers before the libraries caught up. You understand AI deeply because you've debugged gradient flows at 2am and know why your loss function is misbehaving. As a Lead AI Engineer in the AI Research & Solutions group, you'll work hands-on at least 3 days a week in the office in Menlo Park, leading the development of ML systems that power enterprise AI agents—systems that reason, act, and recover across long-horizon workflows. This is not a research scientist role. This is for the engineer who wrote the code that made the paper possible—the one who turned theory into working systems that ship. --- ### What You'll Do Build & Train Models - Own the full pipeline: architecture design → dataset engineering → distributed training → production deployment - Implement state-of-the-art techniques from papers—often before official implementations exist—translating math into working PyTorch code - Build experimentation infrastructure that enables rapid iteration on architectures, training regimes, and evaluation methods Build Agent Systems - Create orchestration loops that enable AI to reason across multi-file changes, invoke tools (compilers, test runners, debuggers), and recover from failures - Implement RL for code generation: execution-based rewards (RLVR), process supervision, reward models, and execution semantics alignment Ship Production AI - Optimize for inference: quantization, distillation, pruning, and serving infrastructure—understanding quality/latency/cost tradeoffs - Design evaluation pipelines that catch regressions and measure what actually matters; build guardrails and monitoring for production - Define integration architecture: APIs, batching, caching, failure handling Lead Technically - Mentor engineers on ML fundamentals, debugging techniques, and the craft of building reliable systems - Stay current with the field—read papers, run experiments, share findings, separate signal from hype --- ## What You Bring # Required (5+ years building AI that shipped) - Trained real models: Architecture design through deployment—not fine-tuning tutorials, actual model development - Deep ML fundamentals: Backprop, attention, loss functions, optimization—at a level where you can debug them - Production engineering: Expert Python, PyTorch fluency, distributed training, GPU programming, ML infrastructure at scale - Dataset intuition: You've built datasets and can spot data issues from model behavior—you know data quality beats model size - Agent architecture: Built systems that plan, act, and adapt—agentic loops, tool-calling, multi-step reasoning pipelines Strong Signals - Implemented papers from scratch before official code existed - Built or improved custom training infrastructure and experimentation pipelines - Worked on LLMs, diffusion models, or frontier architectures at a low level - Built RL training pipelines with non-standard rewards (execution-based, verification, process rewards) - Implemented code retrieval systems: RAG for code, AST-aware search, repository-level indexing - Worked on SWE-Bench or similar and understand what makes coding agents succeed or fail - Open-source ML contributions or published implementations Paths We Value - Research backgrounds—PhDs, open-source contributors, or anyone who built models behind research - Non-traditional paths from physics, math, or quantitative fields; show us models you've trained, not credentials --- ### Who Thrives Here - Builders, not talkers: You'd rather spend a week implementing a paper than writing a proposal about it - Curious and rigorous: You ask "why does this work?" and don't stop until you understand - Fast learners: New techniques come constantly; you pick them up quickly because you understand fundamentals deeply - Pragmatic engineers: You know when to use existing solutions and when to build custom—optimize for shipping, not elegance --- ### Why This Role? Production AI systems that matter—not demos, not POCs. A team building next-generation AI agents for enterprise applications on OutSystems, where technical depth is valued over impressive-sounding credentials. If you've been frustrated that your team didn't understand why data quality mattered more than model size, this is your team. The Longer Story: OutSystems enables enterprise teams to build AI-powered applications and agents that reduce manual work, streamline internal operations, and accelerate impact. A proven low-code foundation combined with agentic AI and AI app generation capabilities empowers teams to move up to 10x faster with the assurance of security, scalability, and governance built in. As the future becomes agentic, our customers need us now more than ever. AI has opened the door to extraordinary possibilities—but inside the enterprise, things are moving fast and feeling chaotic. Some early adopters are making progress in production, but for many, AI tools are sprawling without governance, data isn’t ready, and talent isn’t there yet. Enterprises are still drowning in application backlogs and struggling with legacy systems. But with the right platform, AI doesn’t have to add to the chaos. It can become the breakthrough that brings clarity—and drives real, enterprise-wide impact. At OutSystems, we’ve built that platform, providing the tools necessary for enterprises to overcome these hurdles. We are looking for passionate, talented, and motivated people to join us in helping our customers build, deploy, and scale apps and agents—fast, helping them accelerate innovation while enabling secure, governed human-AI collaboration. OutSystems is a truly global company, with more than 850,000 developer community members, 1,700 employees, more than 500 partners, and thousands of active customers in over 75 countries and across 21 industries. Founded in 2001, OutSystems has offices in the United States, United Kingdom, the Netherlands, Portugal, Germany, the UAE, Japan, Hong Kong, Malaysia, Australia, India, and Singapore, and of course has a thriving, worldwide community of remote employees. Amongst our customers are some of the world's most recognizable brands across diverse industries—brands like Toyota, Heineken, Bosch, KeyBank, and UCLA. These customers are the reason we have a 4.6 star rating on G2. Their success is ours, and their stories demonstrate tangible ROI and transformational impact. We are a 9x Gartner Magic Quadrant Leader for Low-Code Application Platforms and a multi-year leader in the Forrester Wave. We’re recognized not just as leaders but as visionaries with a strong ability to execute, now extending our leadership into the AI and agentic application development arena. Working at OutSystems Our goal is to ensure that OutSystems is a place for bright, happy, and motivated people who share a common purpose and take pride in doing excellent work to pursue our vision of providing the AI-powered low-code development platform enterprise leaders trust to build, secure, and evolve their business applications, agents, and core systems. Our culture is focused on our core values of trust, customer success, innovation, and alignment. Our team members operate with transparency, integrity, and accountability, define success through the lens of the outcomes we deliver for our customers, push the boundaries with excellence, and work together toward our shared vision to deliver on what matters most. What do we have to offer you? A company that is always growing, changing, and innovating. We challenge each other to innovate in our products, in our team, and how we use our own technology. And we give our teams space to be proactive and creative. Real career opportunities. We care about growth and development. Yes, vertical career progression is a possibility, but it's not the only one. From lateral moves and joining different teams to mastering specialized skills, we support your growth no matter what your goals are. Work colleagues that are as smart, hard-working, and driven as you. We act as one global OutSystems team, taking ownership and working together toward a shared vision. Disrupting the status quo is in our DNA. In fact, it’s why our company exists. We ask “why” a lot. It helps us connect our individual work to the bigger picture and sometimes even uncover a better way. Are you ready for the next step in your career? Then we’d love to hear from you! OutSystems nurtures an inclusive culture of diversity, where everyone feels empowered to be their authentic self and perform at their best. A company that embraces the creativity and innovation that comes through diverse perspectives. We are committed to creating a team that reflects society through inclusive programs and initiatives and are proud to be an equal opportunity employer. All qualified applicants receive equal consideration regardless of race, place of origin, color, age, marital status, religion, sex, sexual orientation, gender expression or identity, protected veteran status, disability status or any other status protected by law. We provide the AI-powered low-code development platform that enterprise leaders around the world trust to build, secure, and evolve their business applications, agents, and core systems. We are looking for talented and motivated people to join us in helping companies solve some of their most strategic business challenges, from modernizing their workplace processes to transforming their employee and customer experiences. As a member of the OutSystems global team, you will be at the forefront of shaping the AI-powered low-code into the agentic future. Don't see the job you are looking for? You can submit a resume for future consideration. OutSystems is a leading AI-powered low-code development platform that enterprise leaders trust to build, secure, and evolve their business applications, agents, and core systems. Our proven low-code foundation combined with Agentic AI and AI code generation capabilities empowers teams to move up to 10x faster with the assurance of security, scalability, and governance built-in. Recognized as a leader by analysts, IT executives, business leaders, and developers around the world, global brands trust OutSystems to tackle their impossible projects and turn their big ideas into the applications, agents, and core systems that move their businesses, people, and the world forward. Founded in 2001, the company’s network spans more than 800,000 community members, over 500 partners, and active customers in 75+ countries across 21 industries. Learn more at www.outsystems.com. Ready to join the global leader in AI-powered low-code and be part of a transformative journey? OutSystems awaits, where your talent meets unparalleled opportunity!

Requirements

5+ years building AI that shipped
Trained real models: Architecture design through deployment—not fine-tuning tutorials, actual model development
Deep ML fundamentals: Backprop, attention, loss functions, optimization—at a level where you can debug them
Production engineering: Expert Python, PyTorch fluency, distributed training, GPU programming, ML infrastructure at scale
Dataset intuition: You've built datasets and can spot data issues from model behavior—you know data quality beats model size
Agent architecture: Built systems that plan, act, and adapt—agentic loops, tool-calling, multi-step reasoning pipelines

Nice To Haves

Implemented papers from scratch before official code existed
Built or improved custom training infrastructure and experimentation pipelines
Worked on LLMs, diffusion models, or frontier architectures at a low level
Built RL training pipelines with non-standard rewards (execution-based, verification, process rewards)
Implemented code retrieval systems: RAG for code, AST-aware search, repository-level indexing
Worked on SWE-Bench or similar and understand what makes coding agents succeed or fail
Open-source ML contributions or published implementations
Research backgrounds—PhDs, open-source contributors, or anyone who built models behind research
Non-traditional paths from physics, math, or quantitative fields; show us models you've trained, not credentials

Responsibilities

Own the full pipeline: architecture design → dataset engineering → distributed training → production deployment
Implement state-of-the-art techniques from papers—often before official implementations exist—translating math into working PyTorch code
Build experimentation infrastructure that enables rapid iteration on architectures, training regimes, and evaluation methods
Create orchestration loops that enable AI to reason across multi-file changes, invoke tools (compilers, test runners, debuggers), and recover from failures
Implement RL for code generation: execution-based rewards (RLVR), process supervision, reward models, and execution semantics alignment
Optimize for inference: quantization, distillation, pruning, and serving infrastructure—understanding quality/latency/cost tradeoffs
Design evaluation pipelines that catch regressions and measure what actually matters; build guardrails and monitoring for production
Define integration architecture: APIs, batching, caching, failure handling
Mentor engineers on ML fundamentals, debugging techniques, and the craft of building reliable systems
Stay current with the field—read papers, run experiments, share findings, separate signal from hype