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Founding Machine Learning Engineer - Post Training, RL

Founding Machine Learning Engineer - Post Training, RL

Remote

United states

$ 300,000 /year

Full Time

07-09-2025

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Skills

Python Decision-making Research Training Architecture Machine Learning PyTorch OpenAI Large Language Models

Job Specifications

Founding Machine Learning Engineer - Post Training, RL

A stealth-stage venture backed by Lux Capital (including backers of DeepMind and OpenAI) is on a mission to transform drug development with frontier-scale AI. Their goal: make large language models and multimodal AI systems practical for real-world biomedical applications-accelerating discovery and saving billions in R&D costs.

As a Founding Engineer, you'll work end-to-end-from data engine - training recipe - evaluation - deployment-to make cutting-edge models useful for drug development. You'll own post-training pipelines (SFT, DPO, RLHF), reward modeling, and evaluation systems, while collaborating closely with product and research teams.

Core Responsibilities

Build and optimize post-training workflows for large-scale LLMs and multimodal models.
Architect scalable data processing and filtering pipelines for proprietary biomedical datasets.
Design and implement distributed training systems for foundation models.
Rapidly iterate on prototypes and ship production-ready systems in a fast-paced, collaborative environment.

Skills

Strong software engineering skills and experience building and deploying AI/ML systems at scale.
Deep understanding of LLM training and post-training techniques (RLHF, instruction tuning, reward modeling).
Proficiency in Python and modern ML frameworks (PyTorch, JAX).
Familiarity with distributed training, multi-cloud infrastructure, and data pipeline design.
Bonus: Prior startup experience or background in life sciences. Experience shipping frontier models end-to-end

Why This Role Is Unique

Frontier-Scale Modeling: Architect and train a multimodal biomedical foundation model on a dataset at the magnitude of that used to train GPT-4.
Applied LLMs for Science: Build systems that reason over heterogeneous biomedical data to accelerate decision-making in drug development.
Massive-Scale Data Infrastructure: Design pipelines for ingesting and processing terabytes of structured and unstructured data across modalities.
Founding-Level Impact: Own the core AI stack, shape model architecture, and define scaling laws for applied life sciences AI.

About the Company

3D printing. Gene editing. Artificial intelligence. Big data. New technologies are radically transforming the way that we treat patients and cure diseases on earth and in space; fusing the physical, digital and biological worlds. There has never been a more exciting time to work in life sciences. EPM Scientific are proud to be a leading specialist talent partner in life sciences. Founded in 2012, we help clients solve the number one challenge: talent. Today, we provide permanent, contract and multi-hire talent solutions to s... Know more