[![Image]() Meet Jobicy Copilot — free AI autofill for job applications + remote job alerts ›](#) [All remote jobs](https://jobicy.com/jobs.md)Open role[![RelationalAI logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/476c6344-221.jpg)](https://jobicy.com/company/relationalai.md)Remote opportunity at[RelationalAI](https://jobicy.com/company/relationalai.md) # Solution Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/relationalai.md)Share5 Sep 2026Published55Listing views4Application actions5 Oct 2026Apply before Opportunity details ## About this role. AI SummaryRelationalAI is hiring a senior, highly technical Solution Engineer to own enterprise decision-intelligence implementations from discovery through production. The role combines Python and SQL development, semantic and ontology modeling, cloud data platforms, optimization or reasoning applications, and hands-on debugging in customer environments. This is not a conventional pre-sales or support position; the engineer is expected to deliver production systems and turn recurring customer needs into reusable product capabilities. The position requires strong executive-facing communication alongside the ability to work directly with data engineers and unfamiliar codebases. It is a remote role described as available from anywhere in the world, with location-specific eligibility potentially assessed during recruiting. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThe role demands end-to-end ownership of ambiguous, high-stakes enterprise implementations while working across data modeling, application development, performance tuning, and production troubleshooting. Success also requires converting field discoveries into scalable product improvements and communicating effectively with both technical and executive stakeholders. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$185,000US market range$150k–$220k0$242k AI insightThe posted base salary range is $170,000 to $200,000 USD yearly, producing a midpoint of $185,000. For a US-based senior solution engineer focused on enterprise AI, data platforms, and production delivery, a typical base-salary market range is approximately $150,000 to $220,000 USD yearly; equity and benefits may add meaningful total compensation. ## Core skills Skills and capabilities most closely associated with this opportunity. [Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[Cloud Data Platforms](https://jobicy.com/jobs?search_keywords=Cloud%20Data%20Platforms.md)[Semantic Modeling](https://jobicy.com/jobs?search_keywords=Semantic%20Modeling.md)[Ontology Design](https://jobicy.com/jobs?search_keywords=Ontology%20Design.md)[Graph Analytics](https://jobicy.com/jobs?search_keywords=Graph%20Analytics.md)[Optimization](https://jobicy.com/jobs?search_keywords=Optimization.md)[Production Debugging](https://jobicy.com/jobs?search_keywords=Production%20Debugging.md)[Enterprise AI](https://jobicy.com/jobs?search_keywords=Enterprise%20AI.md) Sample interview questionsDescribe a time you took an ambiguous customer problem from discovery through a production deployment.Start by explaining how you clarified the business decision, success metrics, stakeholders, data sources, and operational constraints. Then describe the architecture and implementation, the failures or tradeoffs encountered, how you validated the solution in production, and the measurable business outcome. How would you approach modeling an enterprise decision problem using structured data and an ontology? I would first identify the entities, relationships, business rules, constraints, and decision outputs that matter. I would map those concepts to the available source data, define a semantic layer with clear ownership and lineage, validate it with domain experts, and iteratively test reasoning outputs against known business cases. A deployment in a customer Snowflake environment is failing, but you did not write all of the underlying platform code. What do you do? I would capture evidence before changing anything, including logs, stack traces, configuration, query plans, permissions, data samples, and reproducible steps. I would form and test hypotheses across the integration boundary, isolate the root cause, implement or recommend a durable fix, and document the findings so the issue becomes easier to prevent and diagnose. How do you decide whether to build a customer-specific workaround or advocate for a platform feature? I evaluate the urgency and customer impact, implementation risk, likely reuse, maintenance burden, and whether the requirement represents a general product gap. If a workaround is necessary, I keep it explicit and well documented, while bringing a reproducible use case and proposed abstraction to Product and Engineering for a scalable solution. How would you communicate a technical limitation or changed recommendation to a VP-level customer stakeholder? I would state the business implication clearly, avoid unnecessary technical detail, and explain the evidence behind the recommendation. I would present practical options with their risks, timelines, and expected outcomes, then align on the decision while ensuring the technical team understands the required next steps. Who We Are At RelationalAI, we’re solving one of the most important challenges in artificial intelligence: how to teach large language models the logic, semantics, and business context of the modern enterprise. Frontier models are trained almost entirely on public data — they can speak about the world, but they don’t understand your business. We fix that. RelationalAI has pioneered a breakthrough called Superalignment — technology that enables LLMs to learn natively from private, structured enterprise data inside the data cloud. By combining this with relational knowledge graphs and our proprietary neuro/symbolic-relational reasoners, we deliver trustworthy decision intelligence: systems that use semantic models to truly understand how a business operates and can reason across its data to drive better outcomes. We’re a globally distributed team of engineers, scientists, and builders redefining how AI learns from data. We believe that high-stakes decisions deserve frontier intelligence — intelligence that’s explainable, aligned, and grounded in reality. If you’re driven by curiosity, thrive in complexity, and want to help build the system that brings true understanding to enterprise AI, you’ll feel right at home here. ### The Role You will be embedded inside our customers’ hardest problems, and you will own the outcome until it works in production. You’ll sit with executives, domain experts, and data teams to find the decisions that actually move their business – inventory that’s in the wrong place, risk concentrations nobody can see, fraud patterns that only emerge across three systems, capacity plans built on guesses. Then you’ll model their world in our ontology, formulate the reasoning problem, write the PyRel, and ship something that runs against their real data in their own Snowflake account. Every engagement here produces two deliverables. The first is the one the customer sees: a working decision system that changes how they operate. The second is the one that matters most to us: the pile of things you had to invent because our platform didn’t have them yet. The modelling pattern you hand-rolled. The constraint formulation that should have been a primitive. The three-hour workaround where an API should have existed. You bring those back, you argue for them, and the strongest of them become product. That second deliverable is why this role exists. If you only ever deliver the first one, we’ve hired a consultant. We’re not hiring consultants. You’ll operate with unusual autonomy: you decide what’s worth building, when a workaround is acceptable and when it’s technical debt we’ll regret, and when to tell a customer their real problem is not the one they asked about. You’ll be technical enough that when something breaks in a customer environment, you find the root cause yourself rather than filing a ticket and waiting. ### What You’ll Do * Own outcomes end to end – discovery, modelling, implementation, performance tuning, production hardening, and the measurement that proves it worked. Not a handoff at each stage. Yours. * Build, not describe – design and ship decision solutions on our modelling, reasoning, and learning stack: ontologies over customer data, rules, graph analytics, optimisation formulations, predictive models * Fill the gaps yourself – when a customer workflow is blocked on something the platform doesn’t do, scope it and build it. Then push the general version upstream: read our source, form a hypothesis before you escalate, open the PR. * Close the loop with Product – every deployment generates a signal. Bring back reproductions, patterns, and specific failure modes (“the only way I could express this was by abusing X in this way”), not vibes. You are one of the loudest inputs into our roadmap. * Run technical discovery that gets to the truth – workshops, demos, and proofs of concept designed to find out whether we can actually solve the problem, not to look impressive. * Leave things better than you found them – document as you go, in the repo, same week. Turn one-off work into reusable reference implementations so the next person starts where you finished. No branch of yours should be diverging for a month. * Refuse shortcuts that compound – no undocumented config drift, no “it works now” without knowing why it broke, no restarting the service before you’ve captured the evidence. ### Who You Are You thrive in ambiguity and move with intent. You’re motivated by deep understanding and meaningful impact. * Owner, not participant. You take full accountability for the outcome, not your slice of it. When something is broken and it’s nobody’s job, it becomes yours. * You build. Your instinct in the face of a hard problem is to open an editor, not a deck. You’d rather show a working prototype on real data than a diagram of one. * High conviction, low ego. You argue hard for what you believe, you’re direct about what’s wrong, and you change your mind quickly when the evidence turns. You challenge ideas without making it personal – people leave arguments with you feeling sharper, not smaller. * Rigorous. You root-cause things. You can explain both why it broke and why your fix works. Surface symptoms don’t satisfy you. * Fast in unfamiliar territory. Dropped into a codebase, a domain, or a data model you’ve never seen, you’re useful within days. “I only do backend” and “that’s not my job” are phrases you don’t use. * High tolerance for friction. Enterprise environments are messy – broken data, VDI access, security reviews, politics. You route around it and keep shipping. * Impact-driven. You want the thing you built to still be running, and still be load-bearing, two years from now. ### What This Role Is Not We’d rather be blunt than waste your time: * It is not demo-and-handoff pre-sales. You don’t disappear after the POC; you’re there when it goes to production. * It is not staff augmentation. You own outcomes, not hours or ticket queues. * It is not advisory. We deliver working software, not recommendations. * It is not a support role. You deploy new things; you don’t maintain someone else’s legacy. ### Qualifications * 5+ years building and shipping production software, at least some of it inside customer or partner environments * Demonstrated end-to-end ownership: you have personally taken something from an ambiguous problem statement to running in production, and you can walk us through the whole arc, including what went wrong * Strong SQL and deep familiarity with cloud data platforms (Snowflake, BigQuery, Databricks, Redshift) * Strong programming ability – Python primarily; comfort with declarative or logic-style languages is a real advantage * Comfortable reading unfamiliar source code, interpreting stack traces, and debugging systems you didn’t write * Able to hold your own with both a VP of Supply Chain and a staff data engineer, in the same meeting * Comfortable operating in high-autonomy, high-velocity, low-instruction environments ### Preferred Qualifications * Built analytical, decision, or reasoning applications that reached production and stayed there * Experience with optimization, constraint solving, rule engines, graph algorithms, or ML on structured data * Semantic modelling, data pipelines, and governance in real enterprise settings * Track record of upstream contribution – features, tools, or abstractions you built for one customer that became standard for everyone * Prior experience in enterprise technology, AI, or analytics platforms ### How We Hire Our loop is designed to test the job, not trivia. Expect a technical screen; a session where you navigate and extend a system you’ve never seen before; a problem-decomposition session on a realistic customer scenario; and a conversation about ownership with the hiring manager. We’re looking for how you think when you don’t know the answer. The Solution Engineer position offers a base salary range of $170,000 to $200,000, along with equity and comprehensive benefits. Please note that this range serves as a guideline; actual total compensation may vary based on factors such as experience, skill set, qualifications, and geographic location. ### Why RelationalAI At RelationalAI, you will: * Work from anywhere in the world * Earn competitive salary + equity * Enjoy open PTO, flexible schedules, and recharge weeks * Access global benefits, mental-health support, and learning stipends * Join a transparent, inclusive, and globally connected culture that values curiosity, excellence, and impact * Regular team offsites and global events – Building strong connections while working remotely through team offsites and global events that bring everyone together. * A culture of transparency & knowledge-sharing – Open communication through team standups, fireside chats, and open meetings. Country Hiring Guidelines: RelationalAI hires people from around the world. All of our roles are remote; however, some locations might carry specific eligibility requirements. Because of this, understanding location & visa support helps us better prepare to onboard our colleagues. Our People Operations team can help answer any questions about location after starting the recruitment process. ### How to Apply If you’re driven by understanding, powered by curiosity, and ready to help shape the next era of enterprise intelligence — we’d love to hear from you. Join us and help build the reasoning layer for the modern enterprise. Privacy Policy: EU residents applying for positions at RelationalAI can see our Privacy Policy [here](https://relational.ai/gdpr). California residents applying for positions at RelationalAI can see our Privacy Policy [here](https://relational.ai/ccpa-notice) RelationalAI is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. 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