About The Role: 

We're hiring a Software Engineer to help turn cutting-edge AI research into products our customers rely on every day. You'll sit on a fast-paced engineering team between our research org and our Applied teams, taking new model capabilities from proof of concept to robust, well-documented APIs serving production traffic at scale.

What You'll Do:

• Design and ship customer-facing APIs that expose new model capabilities, owning them from prototype through launch and ongoing iteration.
• Partner with researchers to productionize new techniques. For example, turning a notebook or POC into something with the latency, reliability, and ergonomics customers expect.
• Work with our customers and Applied teams to understand how users are building with our products, and feed that back into product design and research direction.
• Contribute across the stack as needed: backend services, inference infrastructure, SDKs, internal tooling.

What We're Looking For:

• Strong backend engineering experience, including building and supporting customer-facing APIs in production.
• Comfort operating with ambiguity. Research timelines and product timelines don't always line up, and you're energized rather than frustrated by that.
• Genuine curiosity about AI/ML. You don't need to be a researcher, but you should want to understand what the models are doing well enough to make good engineering decisions around them.
• Strong collaboration skills. You'll be working daily with researchers, applied engineers, and sometimes customers, people with very different contexts and priorities, and the role only works if you enjoy that.

Pay Transparency:

AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on paying competitively for our size, stage, and industry, and are one part of many compensation, benefit, and other reward opportunities we provide.

There are many factors that go into salary determinations, including relevant experience, skill level, qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.

The provided range reflects U.S. expectations. Outside of the U.S., ranges may vary and will be communicated during the process. Compensation is flexible based on experience and skill level.

Salary range: $200,000 - $225,000

About the Role:

We’re looking for a Forward Deployed Engineer to join our Success team and ensure customers have an outstanding experience throughout their journey with AssemblyAI. You will onboard new customers, help them realize their product vision, and explore leading-edge use cases within voice AI.  This requires that you work directly with customers to integrate our voice AI models directly into current and future product features. You will use your technical expertise to plan and execute on our customers’ overall needs within voice AI, helping them choose the right plans, models, and use cases.

As part of our Go to Market Team (GTM) team, you’ll collaborate with Sales, Support, Engineering, Research, and Product to ensure customers get the most out of our products. You’re goal is to use customer feedback to drive our product roadmap. Your creativity and problem-solving skills will be key in finding unique solutions for each customer’s needs. Additionally, you will play a pivotal role in ensuring customers are on the right plan and commercial agreement for their use cases with AssemblyAI.

What You’ll Do:

• Serve as the primary point of contact for customers, ensuring their needs are met throughout their journey with AssemblyAI.
• Evaluate customer use cases and proactively identify opportunities to integrate voice AI into their product roadmaps.
• Manage a portfolio of technical customers, primarily communicating asynchronously and ensuring timely, high-quality support.
• Lead commercial and project meetings, focused on demonstrating the value of our voice AI capabilities.
• Drive growth in customer usage of the API across varying product offerings and end customer use cases.
• Build strong connections with our customers to ensure their long-term success with our platform.
• Act as a thought leader and customer advocate, providing insights and feedback to drive the future direction of our product roadmap.

What You’ll Need:

• Have 4+ years of experience as a technical account manager, account manager, solutions architect, sales engineer, or technical consultant.
• Preferred experience working with SQL, Python, React, and web development frameworks.
• Familiarity with AI/ML tools is a strong plus.
• Passion for AI technology and a strong desire to apply it to real-world customer challenges.
• Ability to proactively identify opportunities for voice AI integration and create detailed execution plans.
• Are willing to wear many hats (software engineer, product manager, account manager, and support), passionate about unblocking issues for customers, and excited about problem-solving.
• Operate with high energy, adept at frequent context switching, and capable of managing multiple projects simultaneously with expansive ownership.

Pay Transparency

AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity across our team. Our salary ranges are set to be competitive for our size, stage, and industry, and reflect just one component of the full compensation, benefits, and rewards we offer.

Salary determinations consider a variety of factors, including relevant experience, technical depth, skills demonstrated during the interview process, and maintaining internal equity with peers on the team. The range shared below represents a general expectation for the posted position. However, we are open to considering candidates who may fall above or below the outlined experience level—in those cases, we will communicate any adjustments to the expected salary range.

The range provided applies to candidates located in the United States. For candidates outside of the U.S., compensation ranges may differ; any adjustments will be communicated throughout the interview process.

Salary range: $140k - $240k OTE

The expected base compensation for this role is listed above. Our total compensation package includes competitive equity grants, 100% employer-paid benefits, and the flexibility of being fully remote. 401k match up to 4% for US-based full time team members.

About the role:

We’re looking for AssemblyAI’s founding Enterprise Account Executive to own a book of strategic accounts and drive revenue across AssemblyAI’s Voice AI infrastructure offerings. This person will generate pipeline in target accounts, develop strong champions, lead model evaluations, and win over enterprise engineering and product leaders. The ideal candidate has a strong background in enterprise infrastructure or API sales, thrives on building pipeline from scratch, and is energized by working with technical buyers on products that win on performance and developer experience.

This is a cross-functional role that requires close partnership with Applied AI, Research, Engineering, Marketing, and Deployment to shape go-to-market strategy and deliver an exceptional customer experience from first touch through deployment and beyond.

What You’ll Do:

• Own a book of enterprise accounts, leading potential customers across the full sales cycle from prospecting through initial production rollout
• Generate pipeline through proactive, creative outbound - including personalized outreach, conferences, exclusive events, and finding warm introductions
• Craft outbound communications that deliver real value to prospects - custom benchmarks, competitive insights, use-case-specific demos that earn replies
• Develop authentic champions within target accounts who advocate for AssemblyAI internally and help shepherd deals to close
• Engage multiple leadership stakeholders in parallel - CTO, CPO, VP Engineering, VP Product, VP of AI, and other technical decision-makers - to build consensus and de-risk deals
• Position AssemblyAI’s value and pricing against alternatives with clear, quantitative and qualitative differentiation
• Drive urgency through business value at every touchpoint, keeping deals moving forward with intent
• Quarterback complex, multi-threaded deals - bringing insights, perspective, and momentum to every customer interaction
• Partner closely with Customer and Technology Teams to represent the enterprise voice of the customer and shape go-to-market strategy

What You’ll Need:

• 4+ years of enterprise infrastructure or API sales experience closing large deals with engineering and product audiences
• A track record of building pipeline from scratch - experience breaking into cold accounts and creating opportunities where none existed
• Technical fluency - comfortable discussing API architectures, model accuracy tradeoffs, and deployment options with engineers and technical decision-makers
• Demonstrated ability to identify and develop champions who advocate internally on your behalf
• A high sense of urgency and ownership; you move deals forward and don’t let them go stale
• Comfort selling in a frictionless, product-led motion where customers stay because the product wins
• Deep understanding of enterprise procurement, legal, and security review processes, with the ability to drive velocity through complex commercial cycles
• A willingness to travel to drive impact - expect to be on the road 50%+ of each month for customer visits, industry events, and team offsites

Pay Transparency:

AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity across our team. Our salary ranges are set to be competitive for our size, stage, and industry, and reflect just one component of the full compensation, benefits, and rewards we offer.

Salary determinations consider a variety of factors, including relevant experience, technical depth, skills demonstrated during the interview process, and maintaining internal equity with peers on the team. The range shared below represents a general expectation for the posted position. However, we are open to considering candidates who may fall above or below the outlined experience level—in those cases, we will communicate any adjustments to the expected salary range.

The range provided applies to candidates located in the United States. For candidates outside of the U.S., compensation ranges may differ; any adjustments will be communicated throughout the interview process.

Salary range: $300,000 - $370,000

The expected total compensation for this role is listed above (including base salary and commission). Our total compensation package includes competitive equity grants, 100% employer-paid benefits, and working alongside the team in our San Francisco or New York City office. A 401k match up to 4% is offered to all US-based full time team members.

About the role:

We're looking for a Product Marketing Manager to support the ongoing marketing engine at AssemblyAI. You'll own the connective tissue of our marketing engine — ensuring our website is up to date, reps are enabled, and customer proof points are fresh and easy to find. You'll also go deep on our top use cases — voice agents, medical scribes, AI note takers, and customer calls — with positioning, campaigns, and narratives that speak directly to the buyers who matter.

What You’ll Do:

Website & Positioning

• Serve as the first stop for any website question or idea — triaging, routing to the right partners, and owning follow-through
• Create new solution, use case, and product pages end-to-end — owning the narrative and copy, partnering with brand and web on build and publish
• Keep existing pages accurate and consistent as the product and positioning evolve
• Write and update copy that reflects our infrastructure-first API positioning
• Translate technical capabilities into crisp, compelling narratives that tie to business outcomes

Use Case & Solutions Marketing

• Serve as an internal expert on our top use cases — voice agents, medical scribes, AI note takers, customer calls — including the buyers, competitors, and market dynamics in each
• Develop executive-level positioning and strategic narratives for buyers in those target use cases, and translate them into decks, landing pages, and campaigns
• Translate new product and feature launches into use-case-specific solutions campaigns
• Spot emerging use cases and verticals based on customer feedback, signup trends, and market signals — and flag where we should lean in next

GTM Team Enablement

• Build and maintain decks, one-pagers, battlecards, and messaging guides
• Drive enablement programs that give the org a shared understanding of our target buyers and how to sell to them

Customer Marketing

• Work with growth team on generating customer expansion and upsell campaigns through campaigns 
• Source and produce case studies, customer quotes, and video content

Growth

• Collaborate with lifecycle and demand gen on activation campaigns —email nurtures,  onboarding flows, and personalized targeting based on signup behavior

What You’ll Need:

• 2-3  years in B2B product marketing, bonus points if you have experience at a developer-focused or API-first company
• Strong writer who can make technical products feel clear and compelling
• Ability to translate technical capabilities into positioning that ties to business outcomes for enterprise buyers
• Experience supporting sales teams with enablement materials that move revenue metrics
• Excellent cross-functional project management and communication skills
• Deeply curious about Voice AI and excited to market infrastructure that helps developers build the next generation of voice-powered products
• Fluent with AI and LLMs in your daily workflow — you use them to move faster on research, drafting, analysis, and iteration, and have a stance on where they help vs. where they get in the way

Bonus Points

• Experience in AI/ML, conversation intelligence, or voice tech
• Background working with or marketing to developers and product teams
• Familiarity with developer tooling ecosystems and modern GTM strategies

Pay Transparency:

AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on paying competitively for our size, stage, and industry, and are one part of many compensation, benefit, and other reward opportunities we provide.

There are many factors that go into salary determinations, including relevant experience, skill level, qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.

Salary range: $140k-190k 

The provided range is the expected salary for candidates in the United States. Outside of those regions, there may be a change in the range which will be communicated to candidates throughout the interview process.

About the role:

We’re looking for a Senior Machine Learning Engineer to accelerate our AI research-to-production pipeline. You’ll build and improve the infrastructure that enables our research team to rapidly deploy and safely test new models, while helping ensure our production inference systems remain efficient, scalable, and reliable. You’ll identify gaps and opportunities in our ML infrastructure, scope solutions to ambiguous technical problems, and help set the technical direction for how we bridge research innovation and production reliability. This role requires a strong backend engineering background in distributed systems and containerization, and a track record of independently driving projects from concept to delivery. This is a cross-functional role that requires close collaboration with both research teams developing models and engineering teams supporting the broader platform.

What You’ll Do:

• Design and implement tooling that enables researchers to quickly deploy and evaluate new models in production 
• Design, build, and maintain high-performance, cost-efficient inference pipelines, making architectural decisions about scaling, reliability, and cost trade-offs
• Proactively identify and resolve infrastructure bottlenecks, proposing and scoping improvements to iteration speed and production reliability
• Develop and maintain user-facing APIs that interact with our ML systems
• Implement comprehensive observability solutions to monitor model performance and system health
• Troubleshoot and lead resolution of complex production issues across distributed systems, driving root-cause analysis and implementing preventive measures
• Set the direction for and continuously improve our MLOps practices, identifying the highest-impact opportunities to reduce friction between research and production.
• Collaborate closely with research and engineering teams to align on technical direction, and help onboard and mentor engineers on ML infrastructure best practices.

What You’ll Need:

• Strong backend engineering experience with Python
• Experience building and operating distributed, containerized applications, preferably on AWS 
• Proficiency implementing observability solutions (monitoring, logging, alerting, tracing) for production systems
• Ability to design and implement resilient, scalable architectures
• Track record of independently scoping and delivering complex technical projects from problem identification through production deployment
• Comfort navigating ambiguity and making pragmatic technical decisions when requirements are unclear or evolving

An ideal candidate should also have some of the following:

• MLOps experience, including familiarity with PyTorch and Kubernetes
• Experience working in fast-paced environments where you owned technical direction for an area and drove projects with minimal oversight.
• Experience collaborating with remote, globally distributed teams
• Comfort working across the entire ML lifecycle from model serving to API development
• Experience in audio-related domains (ASR, TTS, or other domains involving audio processing)
• Experience with other cloud providers
• Familiarity with Bazel and monorepos
• Experience with alternative ML inference frameworks beyond PyTorch
• Experience with other programming languages
• Experience mentoring junior engineers or onboarding teammates onto complex systems

Pay Transparency:

AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on paying competitively for our size, stage, and industry, and are one part of many compensation, benefit, and other reward opportunities we provide.

There are many factors that go into salary determinations, including relevant experience, skill level, qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.

The provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range which will be communicated to candidates throughout the interview process.

Salary range: $195,000 - $225,000

About the Role

We are looking for a Senior Research Engineer to join our streaming speech-to-text research team—a new role that sits at the intersection of research, product, and engineering.

You'll be the person who makes sure we're measuring the right things, benchmarking against the right competitors, building and extending evaluation tooling and translating customer pain points into quantifiable research targets. You'll own the evaluation infrastructure that tells us whether our models are actually better—and by how much.

This role is ideal for someone with a Machine Learning / Research Engineering background who is obsessed with understanding what customers actually need, and who gets satisfaction from turning vague feedback ("the model feels slow") into concrete metrics that the whole team can align around. You're comfortable talking to customer-facing teams one hour, designing a new evaluation framework the next, and then convincing researchers why it matters.

You'll also operate at the frontier of the voice agent ecosystem. Our streaming product integrates with orchestration frameworks like LiveKit, Pipecat, and Vapi, and you'll need to understand how ASR fits into the broader voice agent stack—alongside VAD, turn detection, TTS, and LLM components. As this stack evolves rapidly, you'll help ensure our evaluations reflect real-world integration scenarios.

You'll work directly with our research and engineering teams and become the connective tissue between what customers need and what researchers build. If you're entrepreneurial, rigorous about measurement, and want to have an outsized impact on the success of a rapidly growing product, this is your role.

What You'll Do

Evaluation & Benchmarking

• Own end-to-end and integration-level model evaluation across accuracy, latency, and feature-specific metrics (e.g., turn detection latency, endpointing accuracy) 
• Build and maintain competitive benchmarking pipelines against other providers in the market
• Design and run systematic experiments to measure the impact of model changes

Dataset & Test Set Management

• Onboard, curate, and maintain evaluation datasets—both public benchmarks and internal test sets
• Create evaluation subsets that stress-test specific capabilities and edge cases

Metric Development & Research Translation

• Define evaluation metrics that capture real-world performance
• Translate qualitative customer feedback into quantifiable evaluation criteria
• Work with customer-facing teams to understand pain points and convert them into research priorities

Research Velocity

• Reduce friction for researchers by maintaining clean evaluation pipelines and clear documentation
• Identify evaluation gaps proactively and propose solutions
• Move fast—iterate on benchmarking approaches weekly, not monthly

What You'll Need

• ML fundamentals: You understand how ML models are trained and evaluated well enough to interpret results and debug issues. You don't need to train them from scratch.
• Strong Python skills: You can write clean evaluation scripts, work with data pipelines, and are comfortable with SQL and cloud infrastructure.
• Metric intuition: You understand what makes a good evaluation metric, when to use relative vs. absolute improvements, and how to ensure statistical rigor.
• Voice agent stack familiarity: You understand how the components of a voice agent system interact—VAD, ASR, turn detection, LLM, TTS—and can reason about how changes in one affect the others.
• Tinkerer mentality: You'd rather ship something rough and iterate than spend weeks perfecting it. You're energized by variety.
• Communication skills: You can explain technical results to researchers, summarize findings for leadership, and translate customer feedback into requirements.
• Ownership mindset: You don't wait to be told what to evaluate. You see gaps and fill them.
• Will need to work at least 3-4 hours overlapping with Eastern US Time Zone

Nice to Have

• Experience with speech/audio ML or real-time systems
• Hands-on experience with voice agent orchestrators (LiveKit, Pipecat, Vapi, or similar)
• Familiarity with standard ML evaluation practices and benchmarks
• Experience working with customer-facing or product teams
• Background in QA, data science, or applied ML roles

What Success Looks Like

First 30 days: You've onboarded to our evaluation infrastructure, run your first competitive benchmark, and identified one gap in how we measure model quality.

First 90 days: You own our competitive benchmarking process. Researchers come to you to understand how their changes affect real-world metrics. You've proposed a new metric for a capability we weren't measuring well. 

First 6 months: You're the go-to person for "how do we know if this is actually better?" You've built relationships with customer-facing teams. Your work directly influences which research directions we prioritize. You maintain benchmarks that reflect both standalone ASR quality and integrated voice agent performance.

Pay Transparency:

AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on paying competitively for our size, stage, and industry, and are one part of many compensation, benefit, and other reward opportunities we provide.

There are many factors that go into salary determinations, including relevant experience, skill level, qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.

The provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range which will be communicated to candidates throughout the interview process.

Salary range: $210,000 - $260,000

About the Role

We're seeking an exceptional Senior Software Engineer to join our LLM team. This role is focused on building and maintaining our LLM gateway service—a unified API platform that connects customers to multiple LLM providers. You'll work on high-impact projects that directly solve customer problems, improve their AI and agentic workflows, and ensure reliable access to the best models for their use cases.

This is a deeply customer-focused role. You'll work closely with our customer success team to understand customer challenges, help optimize their prompt strategies, build features that address their pain points, and ensure our service reliably delivers value. We're looking for someone who combines software engineering excellence with genuine curiosity about how customers use AI and a drive to make their lives better.

As a Senior Engineer, you'll drive technical execution within the team, taking ownership of significant features and integrations while mentoring more junior engineers. You should be passionate about writing clean, maintainable code, implementing comprehensive testing strategies, and building highly reliable systems in service of solving real customer problems.

This role requires close collaboration with customer success, product managers, external API providers, and other engineering stakeholders. You'll need to balance technical excellence with pragmatic delivery in a fast-paced startup environment where uptime, reliability, and customer success are critical.

What You'll Do

Solve Customer Problems

• Partner closely with the customer success team to understand customer use cases, challenges, and integration needs
• Translate customer pain points surfaced by the CS team into technical solutions and product improvements
• Build features and tooling that directly address customer needs and improve their workflows
• Provide technical guidance and expertise to the customer success team to help them support customers effectively

Drive Technical Execution

• Own and deliver complete features and integrations within our LLM gateway service
• Build and maintain integrations with multiple LLM providers and AI services (OpenAI, Anthropic, Google Vertex, AWS Bedrock etc.)
• Write clean, maintainable, well-tested code following best practices
• Design and implement scalable, fault-tolerant solutions with appropriate abstractions
• Proactively identify and address technical debt, reliability issues, and code quality concerns
• Participate in on-call rotation to ensure service reliability and rapid incident response

Elevate Engineering Standards

• Conduct thorough code reviews focused on maintainability, testing, reliability, and architectural concerns
• Ensure proper test coverage across unit, integration, and end-to-end testing levels
• Improve code maintainability and extensibility through targeted refactoring
• Contribute to runbooks, incident postmortems, customer-facing documentation, and knowledge sharing
• Identify bottlenecks and propose solutions to improve team velocity and system reliability

Build and Collaborate

• Work closely with product, customer success, and engineering teams to deliver solutions
• Collaborate with external API providers to troubleshoot integration issues and improve reliability
• Translate ambiguous customer requirements into well-scoped technical work
• Proactively communicate status, risks, and blockers
• Mentor junior engineers on coding standards, reliability practices, and customer-centric thinking
• Participate in architectural discussions and contribute to technical decisions

What You'll Need

Core Requirements

• 5+ years of professional software engineering experience
• Strong proficiency in Go with demonstrated ability to write production-quality code
• Hands-on experience with LLM APIs (OpenAI, Anthropic, etc.) and understanding of prompt engineering concepts
• Experience building or working with API gateways, proxies, and rate limiting systems
• Deep experience with prompt engineering, chain-of-thought, few-shot learning, and other LLM optimization techniques
• Experience building applications with llms or ai agents with cascading architectures
• Solid understanding of software engineering fundamentals:
• Data structures and algorithms
• System design and architectural patterns
• Testing strategies (unit, integration, end-to-end)
• Code review practices and technical collaboration
• Experience with:
• RESTful APIs and distributed systems concepts
• Building or integrating with third-party APIs
• SQL and database design
• Version control (Git) and CI/CD pipelines
• Containerization (Docker) and cloud infrastructure
• Strong communication skills and ability to collaborate effectively with cross-functional teams
• Track record of delivering high-quality, reliable software in a fast-paced, team environment
• Ability to thrive in a startup environment with changing priorities and rapid iteration

Nice to Haves

• Experience with AWS services (ECS, Lambda, ElastiCache/Redis, RDS, S3, CloudWatch, API Gateway)
• Experience with monitoring and observability tools (Datadog, CloudWatch)
• Experience with request routing, load balancing, and traffic management
• Experience with circuit breakers, retry logic, fallback strategies, and fault tolerance patterns
• Understanding of API design, versioning, and backward compatibility
• Experience working closely with customer success or support teams

What We're Looking For

This role requires someone who is:

• Customer-obsessed - You genuinely care about solving customer problems and making their lives easier, working through the customer success team to understand and address their needs
• Excellent at software fundamentals - You write code that others want to emulate
• Quality-focused - You care deeply about testing, documentation, and maintainability
• Reliability-obsessed - You understand that uptime and performance directly impact customer trust
• LLM-savvy - You understand prompt engineering, model capabilities, and how to help customers get the best results
• Integration-savvy - You've wrestled with third-party APIs and know how to build resilient integrations
• Collaborative - You work well with diverse stakeholders and partner effectively with customer success teams
• Communicative - You can explain complex technical concepts clearly to teammates and cross-functional partners
• Growth-minded - You're curious, eager to learn, and want to expand into platform and infrastructure engineering
• Pragmatic - You balance perfection with delivery and understand trade-offs
• Team-oriented - You improve not just the code, but the team's overall effectiveness
• Proactive - You anticipate problems before they become incidents and build systems defensively
• Empathetic - You put yourself in the customer's shoes and design solutions accordingly

We're looking for the best person for this role - someone who can hit the ground running while growing with the team. The ideal candidate brings strong software engineering discipline, a passion for AI/LLMs, and genuine excitement about solving customer problems through reliable, thoughtfully designed infrastructure.

Pay Transparency:

AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity across our team. Our salary ranges are set to be competitive for our size, stage, and industry, and reflect just one component of the full compensation, benefits, and rewards we offer.

Salary determinations consider a variety of factors, including relevant experience, technical depth, skills demonstrated during the interview process, and maintaining internal equity with peers on the team. The range shared below represents a general expectation for the posted position. However, we are open to considering candidates who may fall above or below the outlined experience level—in those cases, we will communicate any adjustments to the expected salary range.

The range provided applies to candidates located in the United States. For candidates outside of the U.S., compensation ranges may differ; any adjustments will be communicated throughout the interview process.

Salary range: $180,000 - $240,000 

The expected base compensation for this role is listed above. Our total compensation package includes competitive equity grants, 100% employer-paid benefits, and the flexibility of being fully remote. 401k match up to 4% for US-based full time team members.