Machine Learning Engineer

About Bland

At , our goal is to empower enterprises to make AI-phone agents at scale. Based out of San Francisco, we're a quickly growing team striving to change the way customers interact with businesses. We've raised $65 million from Silicon Valley's finest; Including Emergence Capital, Scale Venture Partners, YC, the founders of Twilio, Affirm, ElevenLabs, and many more.

About the Role

As a Senior ML Engineer at Bland, you'll own the intelligence behind our voice AI platform. You're not just optimizing models—you're architecting the ML systems that make our agents sound genuinely human and drive real business outcomes for enterprise customers. Your work directly impacts whether our agents can handle complex, nuanced conversations or sound like corporate robots.

What You'll Do

• Own the full ML stack: Lead engineering and optimization efforts for our self-hosted STT, LLM, and TTS systems from research through production deployment.

• Build production-grade inference systems: Design and implement high-throughput ML infrastructure serving millions of daily voice interactions with sub-second latency requirements.

• Drive model performance: Research and implement novel approaches to improve our models' conversational quality, RAG pipelines, and reduce latency,

• Optimize for enterprise scale: Handle complex inference optimization challenges—model quantization, efficient serving architectures, and cost optimization for large-scale deployments.

• Collaborate across teams: Work closely with Deployment Engineers to understand customer requirements and translate business needs into ML solutions that actually work in production.

• Push the boundaries: Experiment with cutting-edge techniques in conversational AI, real-time speech processing, and multi-modal understanding to keep Bland at the forefront of voice AI.

What Makes You a Great Fit

• Deep ML expertise: 3+ years in machine learning with 1+ years focused on speech, or conversational AI. You've shipped ML systems that real users depend on.

• Experience with TTS/STT systems: You get your hands dirty with any new emerging technologies in this space, and are implementing novel solutions.

• Specialist: You don't have to have experience with the entire STT, LLM, TTS stack. We want someone who can narrow down on a specific problem and understand it in and out.

• Production experience: You've built and scaled ML infrastructure from 0-1 and You know the difference between a research prototype and a system that works at enterprise scale.

• Full-stack mindset: Comfortable working across the entire ML pipeline—data, training, inference, monitoring, and everything in between.

• Startup DNA: You've thrived in fast-moving environments where you own outcomes, not just tasks. You're comfortable with ambiguity and excited by the challenge of figuring things out.

Bonus Points If You Have

• Experience with real-time speech processing, TTS/ STT, or telephony systems

• Background in large-scale distributed training and inference

• Experience with conversational AI, chatbots, or voice assistants

• PhD in ML/AI or equivalent research experience

How You Show Up

• Ownership mindset: You take full responsibility for your systems' performance and never wait for someone else to solve problems you can tackle.

• Quality obsessed: You care deeply about the craft—our agents should sound truly human, not like phone trees.

• Data-driven: You measure everything, run rigorous experiments, and let results guide decisions.

• Collaborative: You work seamlessly with engineers, deployment teams, and customers to deliver solutions that actually work.

• Relentless: You push through ambiguous challenges and complex technical problems until you find solutions.

Benefits and Pay:

• Healthcare, dental, vision, all the good stuff

• Meaningful equity in a fast-growing company

• Every tool you need to succeed

• Beautiful office in Jackson Square, SF with rooftop views

If you don't have the perfect experience that is fine We're a bunch of drop-outs and hackers. Working at a start-up is really hard. We work a lot and we figure things out on the fly. Please note, however, for this position machine learning experience at an United States based company is required.

Compensation Range: $140,000-$250,000