Senior Software Engineer

Location

United States or Canada

Fully remote, with company and team offsites approximately every six weeks.

Company Stage of Funding

Series B

Total Funding: $113M

Company Size: Approximately 175 employees, with around one-third of the company in Engineering.

Office Type

Fully remote within the United States or Canada. Candidates should be comfortable attending in-person company and team gatherings approximately every six weeks.

Salary

$130,000–$190,000 base salary

Equity

Competitive equity with the opportunity to participate in the company's long-term growth.

Visa

Must have valid work authorization in the United States or Canada. New visa sponsorship and visa transfers are not supported.

Employment Type

Full-time

Company Description

This company is building AI-powered voice and conversational agents that automate customer interactions for large enterprises. Its platform uses production-grade AI to handle millions of customer calls every month for Fortune 500 companies, helping businesses improve customer service, scale operations, and automate complex conversations.

The company has been building and deploying AI agents in production since 2020, giving engineers the opportunity to work on established AI products rather than experimental features or AI capabilities added as an afterthought. Voice AI is a particularly challenging area of automation, requiring reliable conversational experiences, low latency, cost efficiency, and the ability to operate at enterprise scale.

The company has raised $113M in funding, including investment from Norwest, Salesforce Ventures, Stripes, and other investors. With approximately 175 employees and a substantial engineering organization, it offers the opportunity to work in a lean, high-impact environment where engineers own multiple initiatives and contribute directly to product development.

The engineering organization is divided across four teams, each led by an Engineering Manager. Three teams focus primarily on core product development, while another works on forward-deployed engineering projects. Engineers collaborate across product, engineering, and machine learning to bring new AI capabilities into production.

The Senior Software Engineer role is suited to experienced full-stack engineers who have shipped scalable software, worked hands-on with large language models, and thrive in fast-moving startup environments. The ideal candidate combines strong software engineering fundamentals with practical experience building LLM-powered products, conversational AI, or multi-turn agents.

The culture values initiative, broad technical capability, ownership, and self-reflection. Engineers are expected to take responsibility for meaningful projects, challenge assumptions, and contribute beyond narrowly defined tasks.

What You Will Do

1. Build and Ship Full-Stack Product Features

• Design, develop, and ship full-stack features across AI-powered voice and chat products.

• Build user-facing and backend capabilities that improve the functionality, reliability, and scalability of the platform.

• Work with TypeScript, React, Node.js, Python, and related technologies.

• Own engineering projects from initial problem definition through implementation, testing, deployment, and iteration.

• Contribute to product capabilities that help meet customer needs and support business growth.

2. Develop and Enhance LLM-Powered Products

• Build and improve products powered by large language models.

• Integrate LLM capabilities into production systems and customer-facing workflows.

• Work with prompt engineering, multi-turn agents, tool use, and conversational AI patterns.

• Experiment with new approaches to improve the quality, reliability, and effectiveness of AI-driven interactions.

• Collaborate with product and machine learning teams to turn model capabilities into practical product features.

3. Improve Voice and Conversational AI Systems

• Help integrate and improve speech recognition, text-to-speech, and conversational AI capabilities.

• Build systems that support natural, reliable, multi-turn conversations.

• Investigate challenges related to conversation quality, response latency, system reliability, and operating costs.

• Debug production issues and improve the performance of AI-powered voice and chat experiences.

• Contribute to technology that supports millions of customer interactions for enterprise clients.

4. Own Production Systems at Scale

• Build and maintain software designed to support large-scale, enterprise production workloads.

• Identify bottlenecks and improve system performance, reliability, and scalability.

• Balance latency, cost, reliability, and user experience when making technical decisions.

• Investigate complex production issues and implement durable solutions.

• Apply sound engineering judgment when evolving systems that are already used by enterprise customers.

5. Collaborate Across Engineering Teams

• Work with engineers across multiple product teams to deliver important product capabilities.

• Partner with Engineering Managers, Product, and Machine Learning teams to prioritize and execute projects.

• Contribute to core product development or forward-deployed engineering initiatives, depending on team needs.

• Share technical knowledge, review code, and contribute to engineering standards.

• Take ownership of multiple initiatives in a lean organization where engineers have broad responsibilities.

6. Contribute to Forward-Deployed Engineering Initiatives

• Work on customer-driven engineering projects when needed.

• Translate real-world customer requirements into practical technical solutions.

• Build or adapt product capabilities to address complex customer use cases.

• Collaborate with customer-facing and internal teams to resolve implementation challenges.

• Bring insights from customer deployments back into the core product.

7. Improve Engineering Practices and Systems

• Write maintainable, testable, and production-ready code.

• Participate in code reviews and help maintain high engineering standards.

• Contribute to system design, technical decision-making, and architectural improvements.

• Work with cloud infrastructure, databases, deployment workflows, and CI/CD pipelines.

• Identify opportunities to simplify systems and improve engineering efficiency.

8. Take Ownership in a High-Growth Environment

• Independently drive projects and make progress without requiring constant direction.

• Move between product development, debugging, system design, and cross-functional collaboration as needed.

• Learn unfamiliar technologies and domains quickly.

• Reflect on feedback, identify areas for improvement, and continuously develop engineering skills.

• Contribute to a culture of initiative, accountability, and broad technical ownership.

Ideal Candidate Background

Experience Requirements

• 5–8 years of experience as a full-stack software engineer.

• Demonstrated experience operating at a senior software engineering level.

• Experience owning and shipping software solutions that scale to millions of users or support substantial production workloads.

• Hands-on experience building products or features that use LLMs.

• Experience working in a startup, high-growth technology company, or similarly fast-moving engineering environment.

• Strong ownership of projects from design through deployment and ongoing improvement.

• Ability to work independently, manage multiple initiatives, and adapt to changing priorities.

Education Requirements

• No specific degree or university requirement has been provided for this role.

• Prioritize demonstrated engineering ability, shipped production software, and practical LLM experience over educational credentials alone.

Full-Stack Engineering Experience

• Strong proficiency in Python or TypeScript; at least one is required.

• Experience with full-stack application development and production software systems.

• Familiarity with React and Node.js is relevant for candidates with a TypeScript background.

• Experience building APIs, backend services, and user-facing product features.

• Ability to make sound technical decisions and deliver maintainable software.

• Experience taking features from concept to production rather than working exclusively on isolated components.

LLM and Conversational AI Experience

• Hands-on experience building or integrating LLM-powered products.

• Familiarity with prompt engineering, multi-turn agents, or LLM orchestration tools such as LangChain.

• Ability to explain how LLMs are used in a real product or production workflow.

• Experience addressing practical challenges such as reliability, latency, response quality, and cost.

• Conversational AI, voice AI, or multi-turn agent experience is a strong advantage.

• Candidates should demonstrate applied experience rather than theoretical familiarity with LLMs alone.

Scalability and Production Engineering

• Experience owning and shipping software that serves millions of users or supports high-volume production workloads.

• Ability to diagnose and resolve production issues.

• Understanding of performance, reliability, scalability, and system design.

• Familiarity with relational databases such as MySQL or PostgreSQL.

• Experience with cloud platforms such as AWS, Google Cloud, or Azure.

• Familiarity with CI/CD pipelines and modern deployment practices.

• Ability to balance engineering quality with the need to ship useful product improvements quickly.

Ownership and Problem-Solving

• Strong initiative and a proactive approach to engineering challenges.

• Comfortable taking ownership of ambiguous projects and driving them to completion.

• Able to work across the stack and contribute outside a narrowly defined specialty.

• Uses sound judgment when choosing between different technical approaches.

• Demonstrates curiosity, adaptability, and a willingness to learn.

• Can reflect on mistakes and feedback and apply those lessons to future work.

Collaboration and Culture Fit

• Works effectively with engineering, product, and machine learning teams.

• Communicates technical decisions and trade-offs clearly.

• Gives and receives code review feedback constructively.

• Demonstrates self-awareness and a willingness to improve.

• Takes accountability for results rather than focusing only on assigned tasks.

• Comfortable in a lean organization where engineers own multiple initiatives.

Nice-to-Have Qualifications

• Experience at companies specializing in conversational AI, voice AI, or enterprise contact-center technology.

• Experience at Poly, Decagon, Sierra, Five9, Genesys, ElevenLabs, NICE, Talkdesk, or RingCentral.

• Experience with multi-turn conversational agents or LangChain.

• Experience integrating speech recognition and text-to-speech technologies.

• Experience with Rust.

• Familiarity with Google Cloud Services and Kubernetes.

• Experience with MySQL, PostgreSQL, and cloud infrastructure.

• Experience improving CI/CD pipelines and production deployment workflows.

• Experience in a forward-deployed engineering role or working directly on customer-specific technical challenges.

• Experience building AI products used by enterprise customers.

Compensation & Benefits

• Base salary range of $130,000–$190,000.

• Competitive equity with potential long-term upside.

• Fully remote work within the United States or Canada.

• Company and team gatherings approximately every six weeks.

• 100% coverage of medical, dental, and vision insurance, as described in the role overview.

• Flexible vacation policy.

• Technology and learning stipend.

• Paid sabbatical after five years.

• Opportunity to work on production AI agents serving Fortune 500 companies.

• Significant engineering ownership in a lean organization.

• Opportunity to work with modern LLMs, conversational AI, and voice AI technologies.

Why Join

• Build real AI products that have been operating in production since 2020.

• Work on voice AI, one of the more technically challenging areas of conversational automation.

• Develop systems that handle millions of customer calls monthly for Fortune 500 companies.

• Work with LLMs and conversational agents in production rather than focusing solely on prototypes or research.

• Join a well-funded Series B company with $113M in funding.

• Take ownership of meaningful product features and multiple engineering initiatives.

• Collaborate with experienced engineers across core product and forward-deployed teams.

• Work remotely within the United States or Canada.

• Contribute directly to product quality, scalability, and the future of enterprise conversational AI.