🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
Fully remote Immediate joiners only, non-negotiable NeuroDrift builds production voice AI for enterprise contact centres. Our platform handles 300+ production calls a day and we have shipped over 350 voice AI deployments. Models we pick and tune sit in a live call path with a real customer on the other end, where a 200ms regression is something people hear. We are hiring one senior, hands-on engineer to own the model layer: choosing models, serving them ourselves, fine-tuning them, and proving they hold up under load. This is a deep individual contributor role. You write code every day and you are measured on what runs in production, not on managing people. What you'll do - Benchmark open-weight and hosted models against real workloads on quality, latency, throughput and cost per call, and make the call on what ships - Deploy and serve models yourself on GPU using vLLM, Triton, TGI or similar, containerized, including into a client's own cloud account - Fine-tune and adapt models, including LoRA and QLoRA and supervised fine-tuning, and build the data pipelines behind it - Build evaluation harnesses and datasets that catch regressions before a customer does - Tune inference for production: quantization, batching, KV cache, concurrency and GPU utilisation - Get all of it into a live call path alongside our voice and product engineers Who this is for - 3 to 7 years engineering, still writing code daily, solid in Python - You have deployed and served open-weight models in production yourself, not only called a hosted API - You have run structured evaluations, built your own eval sets, and made model decisions from data rather than vibes - You have fine-tuned a model and shipped the result - Comfortable on GPU infrastructure: memory limits, quantization, throughput tuning, cost - You deploy and operate your own services on AWS with Docker and CI/CD Nice to have - Speech models: ASR and TTS, self-hosted or containerized - Real-time or streaming inference, where .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.