Fully remote | Complete engagement job
Founded in Palo Alto by Dr. Andrew Ng and Israel Niezen, Factored helps U.S. companies build and scale world-class AI, ML, and Data teams, powered by the top 1% of LATAM talent, with a defining purpose: To empower brilliant humans, unleash their potential, and amplify their impact in the world.
At Factored, you’ll be part of a community that values learning, ownership, and authenticity, where your growth is personal and your ideas matter. We’re transparent, curious, and collaborative. We strive for excellence, celebrate diversity, encourage curiosity, and build an environment where you can truly thrive.
We are seeking a Sales Engineer with deep hands-on experience in AI engineering and machine learning system design to join our go-to-market team. You will partner with Account Executives across the full sales motion, leading technical discovery, designing reference architectures live with prospects, scoping engagements, and translating ambiguous client problems into concrete, deliverable solutions across our service portfolio: data engineering, machine learning, data science, data analytics, and AI engineering.
Calls often pull toward ML/AI system design, RAG architectures, agentic systems, LLM productionization, evaluation strategy, inference cost/latency trade-offs. You will be the technical authority in those conversations: credible enough to push back on prospects, structured enough to convert a whiteboard sketch into a scoped statement of work, and pragmatic enough to know when a simpler solution wins.
You will operate with an owner mentality, closing the loop between sales conversations and engineering and delivery teams, codifying repeatable solution patterns, and raising the technical bar of how Factored sells.
Functional Responsibilities:
- Lead technical discovery calls with prospects, surfacing real requirements, constraints, and success criteria across data engineering, ML, data science, analytics, and AI engineering use cases.
- Design and present ML, AI and Data Science system architectures live with clients — including RAG, agentic workflows, LLM evaluation pipelines, model serving infrastructure, and end-to-end MLOps.
- Translate ambiguous business problems into scoped, deliverable engagements: define solution approaches, identify risks, size effort, and shape statements of work alongside Account Executives.
- Serve as the deal-side technical authority, defending architectural choices and pushing back when prospects propose suboptimal designs.
- Build technical proposals, reference architectures, and proof-of-concept plans that win deals and set delivery teams up for success.
- Partner with delivery leadership to hand off won deals cleanly — ensuring scope, assumptions, and technical context transfer without loss.
- Develop and maintain a library of reusable solution patterns, demos, and reference implementations across our service lines.
- Stay sharp on the rapidly evolving AI/ML landscape (foundation models, agentic frameworks, inference infra, evals) and bring that point of view into client conversations.
- Provide structured feedback to product, delivery, and marketing on what prospects are asking for and where Factored should sharpen its offering.
- Represent Factored externally when needed: webinars, technical content, conference conversations, and select customer events.
Qualifications:
- 5+ years of total professional engineering experience, with a clear track record of increasing technical responsibility and scope.
- 3+ years working hands-on as a Senior Engineer in AI Engineering, Machine Learning Engineering, Data Engineering or an adjacent production-focused role.
- Demonstrable expertise in machine learning system design: ability to whiteboard end-to-end ML/AI systems live, reason about trade-offs (latency, cost, accuracy, maintainability), and defend choices under technical scrutiny.
- Deep practical experience with GenAI/LLM systems: RAG architectures, agentic patterns, evaluation frameworks, prompt and context engineering, fine-tuning approaches, and inference infrastructure.
- Working fluency across the broader data stack: data engineering pipelines (batch and streaming), warehouses/lakehouses, feature stores, and analytics workflows — enough to credibly scope work outside pure AI/ML.
- Advanced English proficiency, with exceptional written and verbal communication — capable of holding the room with both senior technical stakeholders and executive buyers.
- Strong client-facing instincts: comfortable presenting, listening for the real problem under the stated one, and converting ambiguity into a concrete plan.
- Track record of execution in fast-paced, ambiguous environments — owns outcomes end-to-end without needing tight supervision.
- Structured thinking under time pressure: can decompose a half-formed client problem into a clean architecture in a 60-minute call.
Nice to have:
- Prior experience in a Sales Engineer, Solutions Architect, or pre-sales technical role at a services firm, AI/ML platform, or cloud provider.
- Experience scoping and writing technical proposals or statements of work for consulting engagements.
- Hands-on experience with major LLM providers (Anthropic, OpenAI, Google), inference platforms (Bedrock, Vertex, SageMaker, Databricks Mosaic), and agentic frameworks.
- Familiarity with evaluation tooling and methodologies for GenAI systems (offline evals, online metrics, human-in-the-loop).
- Domain depth in one or more verticals where Factored sells (financial services, healthcare, retail/e-commerce, logistics, etc.).
- Public technical presence — talks, posts, open-source contributions, or community engagement in the AI/ML space.
- Experience working with US-based clients across LATAM time zones.
- Production experience taking ML or AI systems from prototype to deployment — including model serving, observability, cost/latency optimization, and handling failure modes.
Our Benefits:
- Ownership through equity participation.
- Annual company retreat.
- Education bonus for continuous learning.
- Company-wide winter break.
- Paid time off.
- Optional in-person events and meetups.
- Tailored career roadmaps.
- High-performance culture.
At Factored, we believe that passionate, smart people expect honesty and transparency, as well as the freedom to do the best work of their lives while learning and growing as much as possible. Great people enjoy working with other passionate, smart people, so we believe in hiring right, and are very selective about who joins our team. Once we hire you, we will invest in you and support your career and professional growth in many meaningful ways. We hire people who are supremely intelligent and talented, but we recognize that intelligence is not enough. Perhaps more importantly, we look for those who are also passionate about our mission and are honest, diligent, collaborative, kind to others, and fun to be around. Life is too short to work with people who don’t inspire you.
We are a transparent workplace, where EVERYBODY has a voice in building OUR company, and where learning and growth are available to everyone based on their merits, not just on stamps on their resume. As impressive as some of the stamps on our resumes are, we recognize that human talent and passion exist everywhere, and come from many backgrounds, so stamps matter much less than results. All of us are dedicated doers and are highly energetic, focusing vehemently on execution because we know that the best learning happens by doing. We recognize that we are creating OUR COMPANY TOGETHER, which is not only a high-performing fast-growing business but is changing the way the world perceives the quality of technical talent in Latin America. We are fueled by the great positive impact we are making in the places where we do business and are committed to accelerating careers and investing in hundreds (and hopefully thousands) of highly talented data science engineers and data analysts.
In short, our business is about people, so we hire the best people and invest as much as possible in making them fall in love with their work, their learning, and their mission. When not nerding out on data science, we love to make music together, play sports, play games, dance salsa, cook delicious food, brew the best coffee, throw the best parties, and generally have a great time with each other.
