Principal Machine Learning Engineer

About this role

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.
Originally posted on Himalayas