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.
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