Interested in this AI/ML Engineer role at OpenPhone Technologies?
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About This Role
Remote \- United States \& Canada
Engineering
Remote
Full\-time
Small businesses represent 99\.9% of all companies, yet they're still forced to choose between clunky, outdated enterprise phone systems or their personal phones. This multi\-billion dollar market has been overlooked for decades, left to cobble together solutions that slow them down instead of helping them grow.
Quo is changing that. We bring calls, texts, and customer information together into one easy\-to\-use, AI\-powered platform. We’re not just building a phone system. We’re setting a new standard for how businesses connect with their customers.
Driven by our values, we move fast, build with determination, and obsess over delivering value to the businesses that have been underserved for far too long.
Today, Quo is trusted by more than 90,000 companies and rated \#1 in customer satisfaction on G2\. We’re backed by Y Combinator, Tiger Global, Craft Ventures, Slow Ventures, and other top\-tier investors. It’s safe to say we’re onto something big.
About the role
OpenPhone’s AI capabilities power everything from intelligent call assistants to automated workflows that help thousands of businesses communicate better. As a Staff Backend Engineer, you will own the technical strategy for how we design, build, and operate AI features across the product. You’ll partner closely with leaders across engineering, product, and security to translate ambitious product ideas into scalable, dependable systems. Your architectural choices will shape how every customer experiences AI inside OpenPhone—from the very first trial call to fully deployed agents—and you’ll be trusted to balance cutting\-edge innovation with rock\-solid reliability.
Some of the things you’ll do:
- Own the long\-term architecture for AI orchestration frameworks, agent\-based workflows, and model lifecycle management.
- Lead cross\-functional delivery of AI features from proof of concept to production rollout, coordinating engineering, product, design, and CX.
- Establish best practices for prompt engineering, evaluation, versioning, observability, and incident response for AI services.
- Design and optimize low\-latency streaming pipelines for speech and other real\-time data when the customer experience demands it.
- Drive continuous cost, performance, reliability, and security improvements for models and infrastructure.
- Mentor engineers through design reviews, code reviews, and technical coaching, raising the bar for excellence across teams.
- Partner with security and platform leaders to ensure data privacy, compliance, and operational excellence.
- Stay current on advances in LLMs, agent architectures, and emerging tooling, translating insights into actionable roadmap proposals.
Tech Stack \& Tools:
- Our backend is built on Node using Typescript.
- Our AI Infrastructure uses temporal.io, vector DBs, libraries like Langchain and top\-tier llm models.
- We use Kubernetes on AWS to orchestrate our infrastructure setup and deployment.
- The overall architecture is event\-driven microservices with RabbitMQ at the center of it.
- We use a variety of databases for different purposes: Postgres, Mongo, Elastic, and Redis.
- We have the following clients \- Web (React), Android and iOS.
- We use Kong as our public API Gateway.
- Observability Tools: Datadog
- Other Tools: Figma, Linear, Notion, and Slack
About you
- 10\+ years of backend or platform engineering experience, including LLM\-driven systems in production.
- Proven success leading architecture for business\-critical services, balancing innovation with operational pragmatism.
- Deep knowledge of LLM integration patterns, prompt design, vector search, and agent frameworks.
- Expertise in event\-driven and streaming architectures; you can reason about concurrency, ordering, and back\-pressure under load.
- Track record of driving cost optimization, observability, and incident response for AI workloads.
- Excellent written and verbal communicator who aligns diverse stakeholders and produces clear, thorough design docs.
- Collaborative leader who mentors others, fosters psychological safety, and elevates the entire engineering organization.
- Comfortable with ambiguity, you break down complex problems, make informed trade\-offs, and deliver iterative value quickly.
- Empathetic and customer\-focused, you balance technical decisions with user experience and business impact.
Compensation
The annual base salary range for this position is as follow, plus equity and benefits:
- SF Bay Area, Los Angeles, Seattle, Portland, Boston, New York, and Washington, DC Metro: $205,000\-$242,000 USD
- All other US Locations: $185,000\-$217,800 USD
- Canada: $189,000\- $222,000 CAD
The ranges displayed reflect the target for new hire salaries, and within each range, individual pay is determined by your skills and experience, as well as relevant education. Your recruiter can share more and answer questions about the specific salary range during the hiring process.
Salary is just one component of Quo’s total compensation package. Your total rewards package will include equity, extensive medical coverage, a monthly lifestyle stipend, and a flexible PTO policy.
Who we are
As a fully remote company, we thrive as a team. We are curious, ambitious, and dedicated to our work. We value trust above all else, and have a strong bias for action. If you're looking for a place to do your life's work, please get in touch. We'd love to hear from you.
And remember, there's no such thing as a 'perfect' candidate. We're looking for optimists with grit and determination, who are excited to face the challenges of a growing startup. Quo is the type of company where you can grow, and we encourage you to apply for this role even if you don't think you meet all the requirements. *We are committed to creating an inclusive and diverse work environment. It is important that you are able to bring your authentic self to work every day. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.* \#LI\-Remote \#PostLI
Salary Context
This $185K-$242K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At OpenPhone Technologies, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($213K) sits 18% above the category median. Disclosed range: $185K to $242K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
OpenPhone Technologies AI Hiring
OpenPhone Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $242K - $242K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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