The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-VIETNAM
#LI-BRAZIL
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-BRAZIL
#LI-INDIA

About the Role

The AI Integrations team builds the tools that bring AI into Telnyx's core products — think voice agents, intelligent messaging, and real-time AI features that improve our customer experience. We need a strong fullstack engineer to help us move faster.

This is a mid-level IC role. You'll own features end-to-end: building React interfaces, writing Node.js services, wiring up databases, and shipping things that work. AI experience isn't required — you'll pick that up here. What matters is that you can build solid fullstack products and want to learn.

What You'll Work On

• Fullstack features across our AI product suite — frontend to database
• Integrating LLMs and AI capabilities into real world applications (with support from the team)
• Building interfaces that make complex AI tools feel simple
• Shipping iteratively — we value working software over perfect plans

Tech Stack

Core (you should know this well):
React 18+, TypeScript, Tailwind CSS, Node.js, PostgreSQL, Prisma, TanStack Query, Docker, Kubernetes

Bonus (nice to have, happy to teach):
Next.js 15+, LLM integration, LangChain/LangGraph RAG/Embeddings, Vector Databases, MCP, WebSockets, WebRTC, Radix UI, Shadcn UI, Framer Motion

What We're Looking For

You're genuinely comfortable across the stack — not a frontend dev who can "do backend" or vice versa. You've built and shipped real features. You take ownership, communicate clearly, and don't need hand-holding.

AI interest is a plus, but we'd rather have a strong fullstack engineer who's curious than an "AI enthusiast" who can't ship.

We move fast on modern web tooling (React/Next, TypeScript, Tailwind). Expect frequent upgrades.

Values

• Ownership — you see things through
• Collaboration — you work well with others and communicate proactively
• Adaptability — comfortable with ambiguity and changing requirements
• Growth mindset — eager to learn, takes feedback well


#LI-KG1 #LI-VIETNAM
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems
  
  
  

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-VIETNAM
#LI-BRAZIL
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-BRAZIL

About the Role

We are looking for a Senior Engineer to join the Pricing, Costs & Routing (PCR) squad. You will own the automation and data pipelines that power pricing, billing, and financial reporting across Telnyx's product portfolio — voice, messaging, wireless, and more. This is a backend-heavy role working with Airflow DAGs, rate deck management, cost analysis, and pricing APIs at scale.

What You Will Do

• Own and maintain complex Airflow DAGs for pricing automation, margin reporting, and cost analysis
• Build and manage rate deck pipelines across multiple product lines
• Develop and maintain pricing APIs and services in Python (async)
• Analyze CDR/MDR data to identify vendor discrepancies and drive refund/reconciliation workflows
• Build automated discrepancy resolution and self-healing pipelines
• Create and maintain financial reporting: monthly margin reports, negative margin alerts, GP calculations
• Design and build new automation for quoting workflows

What You Bring

• 5+ years of Python experience with strong async programming (aiohttp, asyncpg, FastAPI)
• Airflow production experience — you've built and maintained complex DAGs, not just run them
• SQL fluency across PostgreSQL and analytical databases (Athena, BigQuery, or similar)
• Comfort working with financial and pricing data (margins, markups, cost basis, currency)
• Ability to read and debug Java services when needed
• CI/CD experience with Docker, Jenkins, or GitHub Actions

Nice to Have

• Go experience
• Telecom or CPaaS background (CDRs, termination rates, origination prefixes)
• Salesforce or CRM integration experience
• Experience with pricing engines or billing systems
• AI coding agent experience — the team uses AI-assisted tooling heavily

The Impact You'll Drive

As a Senior ML Engineer (Speech Synthesis), you’ll be a founding member of the team building Telnyx’s next-generation speech synthesis systems. This is a greenfield opportunity — you’ll define the stack, architecture, and best practices for training and deploying state-of-the-art multilingual text-to-speech (TTS) models that power our voice AI agents.

You’ll build everything from distributed training pipelines to inference services that generate ultra-low-latency, lifelike voices across dozens of languages. Your work will bridge research and production — shaping how millions of people experience real-time conversational AI.

What You’ll Work On

• Own the stack from day one: Design and implement the ML training and inference pipelines for multilingual speech synthesis.
• Low-latency TTS: Engineer systems optimized for real-time, streaming speech generation with sub-100ms response targets.
• Train cutting-edge models: Build and fine-tune multilingual TTS systems using modern architectures — including LLM-based, diffusion, and flow-matching approaches.
• Massive-scale data processing: Develop pipelines for ingesting, aligning, and normalizing text, audio, and phonetic data across dozens of languages.
• Experimentation at scale: Run distributed training across multi-node GPU clusters, tracking results and iterating quickly.
• Cross-functional collaboration: Work with infrastructure and voice platform teams to deploy models that scale globally.
• Research meets production: Evaluate emerging techniques (LLM-guided synthesis, zero/few-shot voice cloning, full-duplex modeling) and bring them to life in production-grade systems.

What You’ll Work With

• Infrastructure: Docker, Kubernetes, Ray, Kubeflow, MLflow, Weights & Biases
• Data Systems: Kafka, Redis, PostgreSQL, Parquet
• You’ll define it: You’ll help select and implement the stack that supports distributed training, data processing, and inference for global deployment.

What We’re Looking For

• 6+ years of experience in machine learning or speech systems engineering
• Hands-on expertise with neural TTS, speech synthesis, or adjacent areas (ASR, voice cloning, multilingual modeling)
• You’ve obsessed over one or two hard problems, whether it’s building multilingual TTS from noisy data, teaching LLMs to speak, designing self-supervised audio encoders, or making diffusion models run in real time.
• Experience with LLM-based approaches to speech synthesis or prosody control
• Strong proficiency in Python and PyTorch
• Ability to deploy models efficiently (ONNX, TensorRT)
• Experience leading small teams and defining technical direction or team executables
• Production mindset: You build systems that run fast, stay stable, and are easy to maintain

Why Telnyx

You’ll be joining a company where voice, infrastructure, and AI converge. Telnyx is building the foundation for real-time, intelligent global communications — and your work on multilingual TTS will be at the core of that vision.



#LI-KG1
#LI-REMOTE

The Role

We're looking for a Junior PM to embed in our product team and get stuck into real work across multiple squads. You'll own features end-to-end, work closely with engineering, and use data and AI to sharpen product decisions. The squad you work with may shift — that's the point. We want someone who can pick up context fast, adapt, and deliver regardless of the domain. No telecom or wireless experience needed, but you do need to be technical and comfortable with ambiguity.

What you'll do

Product delivery

• Own features end-to-end: discovery, spec, ship, measure — no hand-holding
• Write specs and acceptance criteria that engineers can actually build from
• Prioritise backlogs based on customer impact and strategic fit

AI & data

• Identify where AI/ML can move the needle across whatever product area you're working in
• Make decisions with data: funnels, churn signals, usage patterns — opinions are a last resort

Cross-functional

• Work across engineering, design, and go-to-market to get things out the door
• Talk to customers and translate what you hear into product priorities

What we're looking for

• 1–3 years in a product, technical, or analytical role — you've shipped something and learned from it
• Technically fluent — APIs, SQL, and reading technical docs don't scare you
• AI/ML background — degree, project work, or hands-on experience applying it to real problems
• Analytically sharp — you default to data, not gut feel
• Clear, concise communicator — your specs don't leave people guessing
• Adaptable and resilient — you'll move between problem spaces and hit the ground running each time

Nice to have

• Experience with developer-facing or infrastructure products
• Familiarity with Mixpanel, Amplitude, or similar analytics tools
• Exposure to telecom, IoT, or connectivity concepts

#LI-KG1 #LI-DUBLIN 

The Role

We’re looking for a Mobile Core Network Engineer to join the team responsible for Telnyx’s global mobile core network. You’ll own the infrastructure that powers our wireless platform — keeping it reliable, scalable, and ready for what comes next. This is a hands-on role spanning network operations, deployment automation, and incident response.

What You’ll Do

Network Operations & Infrastructure

• Deliver and maintain core network services across global datacenters — ensuring high availability of PGWs, DRAs, GTP proxies, HSS, and STPs

• Design redundancy patterns (failover, active-active, disaster recovery) and drive service localisation in new regions

• Manage the GTP proxy fleet and DRA infrastructure: routing rules, IMSI translation, realm routing, and peer management

• Monitor traffic growth, forecast demand, and scale infrastructure ahead of need

Automation & Deployment

• Build and maintain Ansible playbooks and CI/CD pipelines for consistent, repeatable infrastructure changes

• Operate and evolve the signalling plane — Diameter, GTP-C/U, SS7 MAP — across roaming partner interconnects

• Design and maintain network connectivity: GRX/IPX peering, inter-datacenter tunnels, SCTP multihoming, and firewall rules

Observability & On-Call

• Build dashboards and alerting to catch issues before they become incidents

• Participate in on-call rotation for wireless infrastructure incidents

What We’re Looking For

• Solid understanding of 3GPP EPC architecture: PGW, SGW, HSS, DRA, PCRF, STP, and their interfaces (S6a, S8, S5, Gx, S1-MME)

• Experience operating and troubleshooting mobile core network elements in production

• Strong networking fundamentals: IP routing, BGP/OSPF, VLANs, GRE/IPsec, SCTP, and packet-level troubleshooting (tcpdump/tshark)

• Strong Linux skills: systemd, network configuration, performance tuning, and scripting

• Hands-on experience with infrastructure automation — Ansible, Terraform, or equivalent

• Familiarity with containerised environments (Docker, Kubernetes) and CI/CD pipelines

• Experience with monitoring stacks: Prometheus, Grafana, Alertmanager

• Experience with AWS or similar cloud infrastructure

Nice to Have

• Experience with Diameter Routing Agents (freeDiameter or commercial DRAs) or GTP proxy platforms

• Knowledge of multi-IMSI SIM architectures, VoLTE/IMS, or roaming models (S8HR, LBO)

• Familiarity with IPX/GRX peering, GSMA roaming standards (IR.34), or network automation tooling (Netbox, NAPALM)

• Experience with Elixir, Go, or Python in a network infrastructure context

#LI-KG1
#LI-DUBLIN