Two Dots

Company Mission / Why This Matters Two Dots builds verification and risk infrastructure for housing to help solve the housing crisis. Housing is too expensive because America created a single family mortgage machine to cut average people into home price inflation fueled by soft bans on new development. That worked for many decades, but when a small single family home costs several million dollars, it stops being an engine of opportunity and becomes a source of the very resentment modern mortgages were originally created to solve. Housing supply has been restricted so much that people have started fabricating documentation or relying on bypasses and overrides to sign up for a payment they can’t really afford. That conceals the problem instead of solving it. We believe that public and private policy has to change, and that involves breaking the system that conceals our affordability crisis and leaves people without the disposable income required to live satisfying lives, fueling resentment and political instability that turns problems at home into problems for the world. The Role We are looking for a Software Engineer with substantial prior experience working with PDFs and PDF-driven applications. PDFs are an odd legacy format: notoriously frustrating to work with, but critically important for understanding people’s finances. Many important businesses that used to run on paper documents now run on PDFs, including bank statements, paystubs, offer letters, and I-20 proof of F-1 visa documents. This role is a strong fit for someone who has worked at companies that do OCR, and document understanding driven workflows. You should be pragmatic. You should think less in terms of exploration alone and more in terms of: How will this perform? How will this scale? Is this simple? Is this reliable? You should be an adept user of machine learning, with enough fluency to reason about model errors. You know what ROC, precision, and recall mean. You can reason through over-selection and under-selection, and compare false positives and false negatives against business needs. The primary trait we are looking for is enough technical knowledge to execute without guidance when requirements are clear. You do not need to be a product engineer, but you should be able to prepare PDFs for machine learning steps and intelligently use those outputs to make full-stack updates to backend workflows that depend on them. You should have a very strong command of Python, and a strong ability to measure service performance and accuracy with systematic metrics using SQL, such as BigQuery. Machine learning and PDF processing often cross the infrastructure boundary in real-world applications. You should be comfortable debugging Kubernetes pods that are crash-looping or restarting, and understanding the impact of queueing, memory, disk usage, and CPU usage, without infrastructure being your sole focus. The Team Henson (CEO) started his career selling FX derivatives to hedge funds at Goldman, then worked at a real estate tech startup for several years leading sales. This enables him to engage with the largest institutional property managers and real estate investors in the country and create value through those relationships. Max (CTO) started out as a software engineer at Blend, a mortgage application company that went public, and went on to work on the search team at Google. That combination of specific consumer fintech experience and knowledge of how sophisticated ML products succeed in production made big enterprise deals work from day 1. We met in middle school and created a media website together where people could watch and post their flash games and animations. We learned to code, source talent, and forge partnerships - and had 500 active users. Although a tragic addiction to World of Warcraft interrupted work on the website, we got back together to start Two Dots. Other team members include: Meta ML alumnus with decades of experience, a 21 year old UMich grad

Company Mission / Why This Matters Two Dots builds verification and risk infrastructure for housing to help solve the housing crisis. Housing is too expensive because America created a single family mortgage machine to cut average people into home price inflation fueled by soft bans on new development. That worked for many decades, but when a small single family home costs several million dollars, it stops being an engine of opportunity and becomes a source of the very resentment modern mortgages were originally created to solve. Housing supply has been restricted so much that people have started fabricating documentation or relying on bypasses and overrides to sign up for a payment they can’t really afford. That conceals the problem instead of solving it. We believe that public and private policy has to change, and that involves breaking the system that conceals our affordability crisis and leaves people without the disposable income required to live satisfying lives, fueling resentment and political instability that turns problems at home into problems for the world. The Role Chat agents are becoming the primary interaction surface of the future. It sounds easy to make a good chatbot, but many systems fail because they misunderstand users, overfit prompts, hide structural problems, or turn complex workflows into brittle demos. We are looking for a software engineer who can build consumer-facing chat agents that serve as the frontend to complex workflows. This role requires a rare combination of user empathy, strong written English, strong Python ability, and a metrics-driven mentality. You should be comfortable using SQL or BigQuery to understand quality, but also know when to roll up your sleeves and do manual QA rather than treating every product problem like back-propagation. You are essentially a future version of a UX Engineer, but for conversational natural language experiences instead of buttons and forms. What You’ll Work On: Consumer-facing chatbots that serve as the frontend to complex workflows Bridging internal workflow APIs and domain object code with the real-world call patterns of AI agents Making smaller models perform like larger models Designing creative ways to automate product judgment, such as using chatbots to roleplay users instead of relying only on manual QA or fixed test cases Working closely with design and product to balance look and feel, interaction quality, and business objectives What We’re Looking For You understand context management deeply. You know the difference between a workflow that makes LLM calls and a true agent loop with tool calling. You know how to start with a smart model and move to cheaper, faster ones without relying on prompt hacks, “CRITICAL:” advisories, or endless lists of dos and don’ts. You understand what belongs in tools and APIs versus what belongs in natural language. Designing that boundary should be a fixation for you. You also understand what is structural and what is in the domain of tone, framing, or model “dark magic.” You care about the headspace the model is operating in, the quality of the user experience, and whether the product actually works for confused real people. Despite working on agents, you are not in “Gas Town.” You do not believe every problem requires a meta-harness, and you do not outsource your judgment to chatbots. You know when to escalate to MLEs if a problem likely requires fine-tuning or more advanced methods. You care deeply about user outcomes. You measure how your experiments are doing, proactively solve quality problems, and have the frustration tolerance required for ambiguous chatbot engineering. The Team Henson (CEO) started his career selling FX derivatives to hedge funds at Goldman, then worked at a real estate tech startup for several years leading sales. This enables him to engage with the largest institutional property managers and real estate investors in the country and create value through those relationships. Max (CTO)

Join Two Dots to build a stronger financial system. Every time someone applies for a mortgage, car loan, or apartment lease, they submit financial documents that humans use to build a financial profile about them. The quality of these financial profiles is a key input that regulates the body temperature of the economy. Two Dots is building a better system to evaluate consumers consistently and fairly. We prevent fraud that humans can’t see, and we surface value in atypical applications that would otherwise be discarded. Please note that we require all full-time employees to work from our office in San Francisco, CA. Role overview:   Reporting directly to our Co-Founder and CEO, Two Dots is looking for highly skilled individuals who can lead enterprise engagements with key customers. The ideal candidate will be organized, ambitious, and a strategic thought partner as we continue to build technology to improve the consumer underwriting experience.  Key Responsibilities: Actively manage a pipeline of opportunities and monthly forecasts  Own the life cycle of the relationship, creatively engage with new prospects and support existing customers to get the most out of Two Dots Tackle ambiguous, complex customer and product questions, bringing together a deep understanding of user needs and technical capabilities  Desirable Traits :  2-6+ years of experience in consulting, investment banking, or fast-paced B2B startups with proven ability to bring together customer-facing, analytical, strategic, and cross-functional work Exceptional relationship management skills, including with senior-level stakeholders  Ownership mentality within all aspects of your work, you see problems and make them yours Excellent communication, presentation, negotiation and interpersonal skills, capable of explaining complex operational information in an understandable way Ability to thrive in ambiguity, with a proven desire to build out new and existing processes What you get in return: An opportunity to revolutionize the real estate leasing industry and own projects that make a tangible impact An environment with a work culture that is based on trust, ownership, flexibility and a growth mindset A competitive salary, comprehensive equity package, and substantial benefits Closing: Two Dots is an equal opportunity employer. We aim to build a workforce of individuals from different backgrounds, with different abilities, identities, and mindsets. Even if you do not meet all of the qualifications listed above, we encourage you to apply! Compensation is variable and is subject to a candidate’s personal qualifications and expectations. For this role, we offer the following OTE range with a 50/50 split and uncapped commission, in addition to an equity package and full benefits: $200k+ per year.

Company Mission / Why This Matters Two Dots builds verification and risk infrastructure for housing to help solve the housing crisis. Housing is too expensive because America created a single family mortgage machine to cut average people into home price inflation fueled by soft bans on new development. That worked for many decades, but when a small single family home costs several million dollars, it stops being an engine of opportunity and becomes a source of the very resentment modern mortgages were originally created to solve. Housing supply has been restricted so much that people have started fabricating documentation or relying on bypasses and overrides to sign up for a payment they can’t really afford. That conceals the problem instead of solving it. We believe that public and private policy has to change, and that involves breaking the system that conceals our affordability crisis and leaves people without the disposable income required to live satisfying lives, fueling resentment and political instability that turns problems at home into problems for the world. The Role Two Dots is hiring a Machine Learning Engineer for a low-headcount, high-impact role focused on technically difficult applied ML problems in housing verification, underwriting, fraud detection, and document understanding. This is not a research role, although the right person has the depth to develop models from scratch end-to-end. Some of the problems we are facing are genuinely hard: detecting whether a PDF was forged or edited, inferring latent financial profiles from messy payment data, extracting information from noisy documents with very high reliability, and solving chatbot or agent quality problems that big foundation models do not solve out of the box. They should be math literate, comfortable with PyTorch, evaluation, model deployment, quality management, metrics-driven evaluation, and data warehouse-oriented SQL such as BigQuery. What You’ll Work On Document forensics and detecting fraudulent or edited PDFs Cash flow underwriting: inferring a latent financial profile from paystubs, bank statements, business data, or other payment data Extracting information from unstructured or noisy sources with very high reliability Solving chatbot and agent quality problems that are too hard for others to solve Developing models, evaluation systems, and quality management processes from scratch Creating broad-based, systemic improvements in ML, LLM, and agent performance Educating the team on how to evaluate ML pipelines and workflows, including workflows that involve prompting foundation models The Team Henson (CEO) started his career selling FX derivatives to hedge funds at Goldman, then worked at a real estate tech startup for several years leading sales. This enables him to engage with the largest institutional property managers and real estate investors in the country and create value through those relationships. Max (CTO) started out as a software engineer at Blend, a mortgage application company that went public, and went on to work on the search team at Google. That combination of specific consumer fintech experience and knowledge of how sophisticated ML products succeed in production made big enterprise deals work from day 1. We met in middle school and created a media website together where people could watch and post their flash games and animations. We learned to code, source talent, and forge partnerships - and had 500 active users. Although a tragic addiction to World of Warcraft interrupted work on the website, we got back together to start Two Dots. Other team members include: Meta ML alumnus with decades of experience, a 21 year old UMich grad who was a top 2,000 LoL player (he is no longer playing the game, thank god), and a former agave farmer who started a shipping and logistics company while at Stanford. What We’re Looking For You should be able to take an ambiguous problem, like PDF fraud detection, and turn it into a reasonable tec

Company Mission / Why This Matters Two Dots builds verification and risk infrastructure for housing to help solve the housing crisis. Housing is too expensive because America created a single family mortgage machine to cut average people into home price inflation fueled by soft bans on new development. That worked for many decades, but when a small single family home costs several million dollars, it stops being an engine of opportunity and becomes a source of the very resentment modern mortgages were originally created to solve. Housing supply has been restricted so much that people have started fabricating documentation or relying on bypasses and overrides to sign up for a payment they can’t really afford. That conceals the problem instead of solving it. We believe that public and private policy has to change, and that involves breaking the system that conceals our affordability crisis and leaves people without the disposable income required to live satisfying lives, fueling resentment and political instability that turns problems at home into problems for the world. About The Role We’re hiring a Product Engineer to collaborate on a team that will continuously deliver value to customers. This is a role for future founders and people who have a deep sense of ownership over customer outcomes and creating tangible value quickly. It’s a hybrid product, engineering, and customer-facing role for career software engineers that think like a business operator. You’ll work closely with product, engineering, customer teams, and sometimes directly with customers to ship the software that makes Two Dots more useful, more reliable, and easier to adopt. Examples of work might include converting manual onboarding steps to self-service, working on UIs used by hundreds of thousands of consumers, or quickly solving urgent customer workflow issues end to end, either from the office or on-site with the customer. What You’ll Do Integrate Two Dots with property management systems and other messy real-world APIs. Streamline customer acquisition with productized, self-serve workflows. Forward-deployed engineering work and special engagements with high-value customers. Fix urgent bugs and workflow issues quickly when customers are blocked. Communicate status, risks, tradeoffs, and decision points clearly to your manager and teammates. Work across product, customer success, sales, and operations to drive outcomes through teamwork. What We’re Looking For 2+ years of professional software engineering experience. Strong TypeScript and React skills. Comfort building polished, reliable workflows. Enough backend experience to design data models, APIs, and business logic. Experience working with external APIs, integrations, or operationally messy systems where reliability depends on the client, not the server. Strong communication skills and an even temperament under pressure. Good judgment about when to move fast and when to slow down. A practical, customer-oriented mindset. Ability to manage your own work as requirements change. Influence direction while remaining collaborative. Strong alignment with Two Dots’ mission and the customer outcomes we are trying to create. The Team Henson (CEO) started his career selling FX derivatives to hedge funds at Goldman, then worked at a real estate tech startup for several years leading sales. This enables him to engage with the largest institutional property managers and real estate investors in the country and create value through those relationships. Max (CTO) started out as a software engineer at Blend, a mortgage application company that went public, and went on to work on the search team at Google. That combination of specific consumer fintech experience and knowledge of how sophisticated ML products succeed in production made big enterprise deals work from day 1. We met in middle school and created a media website together where people could watch and post their flash games and animations. We learned to code

Join Two Dots to build a stronger financial system. Every time someone applies for a mortgage, car loan, or apartment lease, they submit financial documents that humans use to build a financial profile about them. The quality of these financial profiles is a key input that regulates the body temperature of the economy. Two Dots is building a better system to evaluate consumers consistently and fairly. We prevent fraud that humans can’t see, and we surface value in atypical applications that would otherwise be discarded. Please note that we require all full-time employees to work from our office in San Francisco, CA. Role overview:   Reporting directly to our Co-Founder and CEO, Two Dots is looking for ambitious and highly motivated professionals with a strong work ethic, great personality and natural sales instincts to join our team as a Sales Development Representative. The ideal candidate will be eager to advance in our organization by demonstrating their ability to be tenacious self-starters every day. As an SDR, you will be a key part of our organization, driving Two Dots awareness and supporting our Sales team by generating pipeline and influencing closed won opportunities.  Key Responsibilities: Conduct outbound activities such as cold calling to prospect, educate, and develop target accounts Drive conversion of leads into meetings booked, and follow up on inbound leads via email, LinkedIn, and phone calls Partner with Account Executives to build and iterate on strategies  Desirable Traits :  If you’re a new grad, you’re someone who is hungry and eager to get a jumpstart on their career in a high growth, fast paced startup environment If you have 1-2 years of experience, you’re someone who has tenure as a BDR/SDR and wants to take their career to the next level by joining a high growth company Comfortable with rejection; you’re going to hear a lot of “No’s” Personable and an expert communicator; you’re willing to go the extra mile to understand the needs of your customers and sell the Two Dots platform Eager for personal and professional growth and ready to scale your career with Two Dots! What you get in return: An opportunity to revolutionize the real estate leasing industry and own projects that make a tangible impact An environment with a work culture that is based on trust, ownership, flexibility and a growth mindset A competitive salary, comprehensive equity package, and substantial benefits Ability to grow into an account executive at a rapidly scaling AI company over time with good performance and hard work Closing: Two Dots is an equal opportunity employer. We aim to build a workforce of individuals from different backgrounds, with different abilities, identities, and mindsets. Even if you do not meet all of the qualifications listed above, we encourage you to apply! Compensation is variable and is subject to a candidate’s personal qualifications and expectations. For this role, if you are a high performer you can expect to make $100k+ between base + commission, in addition to an equity package and full benefits.

Company Mission / Why This Matters Two Dots builds verification and risk infrastructure for housing to help solve the housing crisis. Housing is too expensive because America created a single family mortgage machine to cut average people into home price inflation fueled by soft bans on new development. That worked for many decades, but when a small single family home costs several million dollars, it stops being an engine of opportunity and becomes a source of the very resentment modern mortgages were originally created to solve. Housing supply has been restricted so much that people have started fabricating documentation or relying on bypasses and overrides to sign up for a payment they can’t really afford. That conceals the problem instead of solving it. We believe that public and private policy has to change, and that involves breaking the system that conceals our affordability crisis and leaves people without the disposable income required to live satisfying lives, fueling resentment and political instability that turns problems at home into problems for the world. Role We are looking for an engineer to own critical ERP integrations. You will use agents to discover API behavior, make codebase-wide changes that isolate and unify integration points, and turn unreliable third-party APIs into highly reliable integrations. You will also build browser extensions for other apps with minimal QA guidance, where extreme attention to detail and defensive programming matter because extensions are hard to update quickly. This role works closely with customers, sales, customer success, and other engineers. You should know when an integration seam requires changing the product itself rather than creating a forever workaround. Integrations directly determine our total addressable market, so your work will be tied to major revenue and company value changes. You should have strong Python and JavaScript skills, understand relational databases, ideally Postgres, and be comfortable with queues and durable asynchronous workflows. We expect experience with messy integrations, scrapers, bidirectional syncing, or similar systems. The Team Henson (CEO) started his career selling FX derivatives to hedge funds at Goldman, then worked at a real estate tech startup for several years leading sales. This enables him to engage with the largest institutional property managers and real estate investors in the country and create value through those relationships. Max (CTO) started out as a software engineer at Blend, a mortgage application company that went public, and went on to work on the search team at Google. That combination of specific consumer fintech experience and knowledge of how sophisticated ML products succeed in production made big enterprise deals work from day 1. We met in middle school and created a media website together where people could watch and post their flash games and animations. We learned to code, source talent, and forge partnerships - and had 500 active users. Although a tragic addiction to World of Warcraft interrupted work on the website, we got back together to start Two Dots. Other team members include: Meta ML alumnus with decades of experience, a 21 year old UMich grad who was a top 2,000 LoL player (he is no longer playing the game, thank god), and a former agave farmer who started a shipping and logistics company while at Stanford.