The posting
Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building.
About the Role
You'll be responsible for turning research direction into working, production-grade ML systems. This role owns the execution layer of the company's intelligence — training pipelines, inference systems, evaluation tooling, and deployment.
What You'll Work On
- Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment
- Fine-tune and adapt models using state-of-the-art methods — LoRA, QLoRA, SFT, DPO, distillation
- Architect and operate scalable inference systems, balancing latency, cost, and reliability
- Design and maintain data systems for high-quality synthetic and real-world training data
- Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership
- Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies
- Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products
- Make pragmatic trade-offs and ship improvements quickly, learning from real usage
- Work under real production constraints: latency, cost, reliability, and safety
Requirements
- Strong background in deep learning and transformer-based architectures
- Hands-on experience training, fine-tuning, or deploying large-scale ML models in production
- Proficiency with at least one modern ML framework (PyTorch, JAX), and ability to pick up others quickly
- Experience with distributed training and inference frameworks (DeepSpeed, FSDP, Megatron, ZeRO, Ray)
- Strong software engineering fundamentals — you write robust, maintainable, production-grade systems
- Experience with GPU optimization, including memory efficiency, quantization, and mixed precision
- Comfort owning ambiguous, zero-to-one ML systems end-to-end
- A bias toward shipping, learning fast, and improving systems through iteration
Nice to Have
- Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer
- Contributions to open-source ML or systems libraries
- Background in scientific computing, compilers, or GPU kernels
- Experience with RLHF pipelines (PPO, DPO, ORPO)
- Experience training or deploying multimodal or diffusion models
- Experience with large-scale data processing (Apache Arrow, Spark, Ray)
What to Expect
The best products in the world are built by small, world-class teams. Decisions are made collectively, at rapid speed — balancing high-quality shipping with fast learning. You'll be expected to bring structure, exercise judgment, and execute independently.
Compensation and benefits are competitive and vary by location; the package includes base salary and equity, discussed openly with you as part of the process.
If there's a fit, expect 3–4 interviews total, followed by a prompt, transparent decision.
