All remote jobs

Senior Backend Engineer – CRM (USA Only, 100% Remote)

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

Remote from
USA
Salary
USD 140k–210k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
2 Nov 2026
Listing views
71
Application actions
3
Application toolkit

Make your next move.

Prepare your resume, explore your fit, and draft a cover letter for this opportunity.

AI Summary

The role, at a glance.

Close is hiring a Senior Backend Engineer to evolve the core CRM data model and build high-performance customer-facing backend capabilities. The role centers on Python-based services, complex migrations, search and retrieval, API design, and scalable data systems across MongoDB, PostgreSQL, Elasticsearch, Redis, and AWS. The engineer will also develop AI-native CRM and MCP-facing tools that allow agents to search, traverse, and act on CRM data. This is a highly autonomous remote role requiring sound architectural judgment, incident experience, and a pragmatic shipping mindset.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe role involves a multi-stage rearchitecture of business-critical CRM data models while maintaining performance and availability. It requires senior-level tradeoff decisions across distributed systems, search, migrations, AI integrations, and production reliability.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate
$175,000
US market range$150k–$220k
AI insightThe disclosed annual base salary range is USD 140,000–210,000, producing a midpoint of USD 175,000. A comparable US market range for a senior backend engineer working on distributed data systems, search, and AI-enabled product infrastructure is approximately USD 150,000–220,000 annually, excluding the stated organization-wide bonus.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you approach rearchitecting a core CRM data model without disrupting existing customers?

I would begin by mapping current entities, access patterns, invariants, and migration risks. I would introduce the new model incrementally behind compatibility layers, dual-write or backfill where appropriate, validate with observability and reconciliation checks, and retire legacy paths only after measured adoption and rollback plans are in place.

Describe how you would design search for an agent that needs to navigate CRM records without learning a proprietary query language.

I would expose a structured, well-documented search interface with clear schemas for entities, filters, relationships, sorting, pagination, and permissions. The system should translate agent-friendly intents into validated queries, provide useful metadata and explainable results, and enforce guardrails for tenant isolation, cost, and query complexity.

What factors guide your choice between PostgreSQL, MongoDB, and Elasticsearch for a feature?

I start with the consistency model, relationship complexity, query patterns, write volume, and latency requirements. PostgreSQL is usually the system of record for transactional relational data, MongoDB can fit flexible document-oriented workloads, and Elasticsearch is best used as a derived index for retrieval and relevance rather than the sole authoritative store.

Tell us about a production incident involving latency or reliability and how you handled it.

I would first stabilize the service through mitigation such as rate limiting, rollback, capacity changes, or disabling an expensive path. Then I would use traces, metrics, logs, and recent-change analysis to isolate the cause, communicate impact and status clearly, and follow up with corrective actions, tests, alerts, and a blameless incident review.

How do you decide where LLMs add value in a CRM workflow versus where deterministic systems should remain in control?

I use LLMs for ambiguous, language-heavy, or research-oriented tasks where probabilistic output creates meaningful user value, such as enrichment or summarization. I keep permissions, data integrity, core transactional logic, and irreversible actions deterministic, with validation, human review where needed, and measurable quality and cost controls around AI outputs.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

About Us

Since 2013, we’ve been building a CRM that gets out of your way and helps your team sell more, faster. Now we’re building AI into every part of it, so Close does the busywork and your team does the selling. No manual data entry, no 10-click workflows. Just communication-first, AI-powered sales software designed to help you succeed and scale.

We’re bootstrapped and profitable which means we answer to our customers and play by our rules. We’re proud of our 120-person, 100% remote team, focused on building Close so that no small, scaling business fails because it can’t figure out sales.

Our Stack

Our backend tech stack consists primarily of Python Flask web apps with our TaskTiger scheduler handling many of the backend asynchronous task processing chores. Our data stores include MongoDB, PostgreSQL, Elasticsearch, and Redis. The underlying infrastructure runs on AWS using a combination of managed services like EKS, MSK, RDS and ElastiCache and non-managed services running on EC2 instances. We have CI/CD pipelines that build Docker images, run automated tests and deploy to Kubernetes clusters. We also use these images in our local development environment allowing coding locally against all of our services. We have a well-documented public API that is consumed by our front-end JavaScript app as well as numerous integrations. Our infrastructure is heavily automated using Terraform, Ansible and other AWS tools.

We love open sourcing our code and ideas on our GitHub and on The Making of Close, our behind-the-scenes Product & Engineering blog. Check out our open source projects like SocketShark, TaskTiger, LimitLion and ciso8601.

AI is both how we build and what we ship, and that’s reshaped what engineering looks like. This is a transformation we’re embracing and find deeply exciting.

About the Role

The CRM team owns everything that Close’s customers touch all day, every day: leads, contacts, opportunities, tasks, activities, custom objects, and search. Each time a call is logged, an email is threaded, a meeting is summarized, it lands on the record this team owns, and every agent we ship starts there.

Today, this team is making big moves towards the next iteration of Close’s core data model, our largest engineering effort currently underway. We’re reopening assumptions made a decade ago and asking what a modern CRM’s data model should look like — one that makes Close flexible for our small, scaling business customers. This is a long series of migrations and rearchitecture projects. If you like greenfield and you like hairy, this is both: help design the next version of Close, and untangle the current one.

One thing to know: we do move people between teams as the work shifts. Most engineers here end up on more than one team over their time at Close — this team is where you’d start, but over time you’ll likely have the opportunity to work on many different projects.

You are

  • A seasoned engineer. Python is our backbone, but perhaps you’ve worked across Go, Rust, or TypeScript. You pick the right tool for the workload rather than retreating to what you know.

  • Architecturally minded. This is the thing we care about most. You’ve designed systems others built on, planned a migration that couldn’t take the product down, and know how to sequence a rearchitecture into pieces that ship independently.

  • Drawn to data model and retrieval problems. Schemas, relationships, normalization, query design, type systems. The plumbing decides whether a CRM feels fast and we pride ourselves on being fast.

  • Experienced in search — this is a serious bonus. Elasticsearch especially, and anything adjacent: Lucene internals, query planning, relevance, indexing at scale, building or operating a search engine.

  • AI-native in production. You’ve shipped meaningful LLM-backed or agentic features to real users, and you have a POV on where AI earns its place in a CRM workflow and where a structured system wins.

  • Working with AI in your day-to-day. You use AI tools in your own workflow to ship faster, write tighter code, and reason about unfamiliar parts of the codebase. We fund best-in-class developer tools and treat experimentation as part of the work.

  • Battle-tested. You’ve debugged incidents where latency budgets didn’t hold, owned a system everyone else relied on, or carried a pager for something with real customer impact.

  • A builder first. You’d rather get a sloppy v1 in front of fifty customers than spend three weeks on abstractions. You ship.

You will

  • Rearchitect the core CRM data model. Rework how leads, contacts, opportunities, and custom objects relate to each other so each of our customers can represent their business the way it operates.

  • Make Close usable by agents. The Agents team builds the MCP foundation; we build the tools and endpoints on top of it that expose the CRM’s data and actions. The hard part is search: any MCP-compatible harness should be able to traverse, filter, and reason over a customer’s CRM without learning our private query language first.

  • Ship AI-native CRM features. This team built agentic enrichment: instead of paying a third-party data provider, an agent goes and researches whatever field you need, in bulk or automatically inside a workflow. There’s a lot more of this ahead, including AI-powered data cleaning and richer AI search.

  • Keep the hot paths fast. Finding a lead, updating a deal, scanning a pipeline, running a bulk action. Salespeople do these hundreds of times a week and no amount of clever architecture matters if they’re slow.

Tech you’ll touch: Python, Flask, FastAPI, MongoDB, Elasticsearch, PostgreSQL, Redis, TaskTiger, MCP — plus whichever LLM ships next.

Benefits

  • Compensation: Competitive pay plus an organization-wide goal-based bonus

  • Paid Time Off: ~5 weeks of PTO to start. Plus a 1-week all-company Winter Holiday Break and paid US holidays. You’ll earn 2 extra days for every year you’re with Close.

  • 80% Work Option: Work with your manager to choose between a standard 5-day week or a 4-day week at 80% pay

  • Parental Leave: Paid leave for primary and secondary caregivers

  • Sabbatical: A 1-month paid sabbatical every 5 years with the team

  • Healthcare (US residents): Two medical plans with Close covering 99% of your premium, plus Dental, Vision, HSA, FSA, and company-paid Long-Term Disability

  • 401k (US residents): We match your contributions up to 6%, vested immediately

Our Values

Build a house you want to live in – Examine long-term thinking and action

No BS – Practice transparency and honesty, especially when it’s hard

Invest in each other – Build successful relationships with your coworkers and customers

Discipline equals freedom – Keep your word to yourself and others

Strive for greatness – Constantly challenge yourself and others

Learn More

Listen to our CEO and Founder, Steli Efti, tell the story of Close’s journey in the $0-30m Blueprint.

Watch our culture video from our 2023 team retreat in Milan. Every year our entire team gathers in person to build connection, foster cross-functional collaboration, and have fun. In 2027, we’re headed to Dusseldorf, Germany!

Explore our product. Check out a demo!

Our Hiring Process

We ask a few role-specific questions as part of our application process. These questions are designed to help us learn more about you from the start, so please answer each one thoughtfully. We see this as an opportunity to get to know you beyond your resume.

We use AI tools daily at Close and expect candidates to do the same. In evaluating your application, we aim to get a sense for you – the way you think, how you communicate, the work you’ve done. Applications that read as fully AI-generated will not be considered.

Regardless of fit, you can expect to hear back from our team with an update on the status of your candidacy.

If you progress to the interview process, you’ll receive a full outline of the role-specific steps in your first touchpoint with us. We do our best to make the hiring process clear and human.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Next step

Apply now.

Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information.

Did you apply?Let us know, and we’ll help you track your application.

Continue on the employer website

Protect your personal information and never pay to secure an interview or job offer. .

Log in to save
One quick step before you apply

Sign in to continue.

Sign in or create a free account to continue to the employer's application.

Applying is free. After signing in, return to this job and select Apply Now.
Add alert
Jobs Talent AI Tools Salaries
Menu