[Hiring] Software Engineer @Telnyx

Role Description

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.

Qualifications

• 3+ years of professional software development experience
• Strong understanding of async programming, concurrency, and parallel execution
• Experience building and operating microservice-based systems
• Solid understanding of distributed systems and production reliability
• Comfort working with databases at scale (relational, graph, or document)
• Strong experience building backend or platform systems that support AI/ML workloads, or familiarity with AI tooling and inference 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
• Experience working with GPU stacks such as AMD ROCm
• Experience with graph databases
• Curiosity and enthusiasm for staying up-to-date with modern AI and ML practices

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.