Solution Engineer
RelationalAI
WorldwideremotePosted 1 month ago
Skill Required
Solutions-EngineeringTechnical-Sales-EngineeringCustomer-Success-EngineeringEnterprise-AIGTMSolutions-EngineerSolution-Sales-EngineerSolutions-Sales-EngineerSolution-Design-EngineerProduct-Solution-EngineerSolutions-Application-EngineerIT-Solutions-EngineerSolutions-Design-EngineerTechnical-Solutions-EngineerQuery OptimizationSnowflakeanalyticssecuritybuildingEmbedded CExpressdesignCloudGenerative AIandGoAIFulltime
Key highlights
- Base salary range: $170,000 – $200,000 (plus equity and comprehensive benefits).
- 5+ years of experience building and shipping production software.
- Strong SQL expertise and deep familiarity with cloud data platforms such as Snowflake, BigQuery, Databricks, and Redshift.
- Fully remote work – work from anywhere in the world.
- Open PTO, flexible schedules, recharge weeks, and learning stipends.
- Ownership of end‑to‑end outcomes, not just hand‑offs.
Role overview
Embedded within customers’ hardest problems, you will own the outcome until it works in production, collaborating with executives, domain experts, and data teams to discover high‑impact decisions, model their world in our ontology, write PyRel, and ship solutions that run on their Snowflake accounts. Each engagement delivers a working decision system for the customer and a set of platform innovations that become product improvements.
Responsibilities
- You will be embedded inside our customers' hardest problems, and you will own the outcome until it works in production.
- Sit with executives, domain experts, and data teams to find the decisions that move their business, then 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.
- Own outcomes end to end – discovery, modelling, implementation, performance tuning, production hardening, and the measurement that proves it worked.
- Design and ship decision solutions on our modelling, reasoning, and learning stack: ontologies over customer data, rules, graph analytics, optimisation formulations, predictive models.
- When a customer workflow is blocked on something the platform doesn't do, scope it and build it, then push the general version upstream.
- Bring back reproductions, patterns, and specific failure modes from each deployment to inform product roadmap.
- Run technical discovery workshops, demos, and proofs of concept designed to determine whether we can actually solve the problem.
- Document work in the repo the same week, turning one‑off work into reusable reference implementations.
- Refuse shortcuts that compound: no undocumented config drift, no “it works now” without understanding why it broke, no restarting the service before capturing evidence.
- Operate with unusual autonomy: decide what's worth building, when a workaround is acceptable, when technical debt is regrettable, and when to tell a customer their real problem is not the one they asked about.
- When something breaks in a customer environment, find the root cause yourself rather than filing a ticket and waiting.
Requirements
- 5+ years building and shipping production software, at least some of it inside customer or partner environments.
- Demonstrated end‑to‑end ownership: personally taken something from an ambiguous problem statement to running in production, and can walk 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.
Nice to have
- 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 built for one customer that became standard for everyone.
- Prior experience in enterprise technology, AI, or analytics platforms.
Benefits
- Work from anywhere in the world (fully remote).
- Competitive salary plus equity.
- Open PTO, flexible schedules, and recharge weeks.
- Access to global benefits, mental‑health support, and learning stipends.
- Regular team offsites and global events.
- Culture of transparency and knowledge‑sharing through team standups, fireside chats, and open meetings.
- Comprehensive benefits package.
Additional details
- It is not demo‑and‑handoff pre‑sales; you stay through 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, not maintain someone else’s legacy.
- Country hiring guidelines: RelationalAI hires globally; some locations have specific eligibility requirements and visa support.
- Privacy Policy links for EU and California residents.
- RelationalAI is an equal opportunity employer.
- How we hire: technical screen, system navigation/extending exercise, problem‑decomposition session, ownership conversation.
- The Solution Engineer position offers a base salary range of $170,000 to $200,000, along with equity and comprehensive benefits (salary range is a guideline; total compensation may vary).