AI Engineer: Post-Training

About us Re-architecting Engineering for the Age of Intelligence PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area. The role You will own the product capability that lets PhysicsX and its customers improve LLMs for their specific work. You will be a hands-on product ML engineer embedded deeply with our Research group and accountable for turning post-training methods into something customers can use. The first goal is to make open models perform strongly on the evals and benchmarks that matter to us. The longer-term goal is a repeatable platform capability through which customers can train models on their own workflows keeping their data, learned behaviour and intellectual property under their control. The remit is broader than supervised fine-tuning. You will choose and apply the right post-training methods, including preference optimisation, reinforcement learning, distillation, synthetic data and verifier-based approaches. You will own the capability roadmap and production outcome; Research will be a close partner in developing methods, and platform product teams will be partners in integrating the experience. What you will do • Own the path from a base model and customer workflow to a measured, deployable model improvement. • Build reliable post-training harnesses covering data preparation, training, evaluation, model artifacts and hand-off to serving. • Improve open models against PhysicsX's agent and engineering benchmarks, using evidence to decide whether training is better than changes to prompts, tools or the agent design. • Develop and operate methods spanning SFT, preference optimisation, RLHF/RLAIF, RL with verifiable rewards, distillation, rejection sampling and related techniques. • Build reproducible synthetic-data and expert-data pipelines, including quality filtering, provenance, licensing and contamination controls. • Design reward signals, verifiers and rollout workflows while detecting reward hacking, benchmark overfitting, model collapse and unintended regressions. • Make post-training available through a mix of managed workflows and expert-facing interfaces. • Ensure customer data and resulting model artifacts can remain within the customer's platform environment across SaaS, VPC and air-gapped deployments. • Work on cross-product and public benchmarks. Own experiment diagnostics with Research, with shared approval where results support product or external claims. • Work with the LLM Self-Hosting & Model Routing Lead to move trained models into supported production serving and routing. • Set the product roadmap for model improvement, balancing research opportunity with customer value, operability and time to production. • Help determine how this capability should grow and, over time, shape the team around it. What you bring Where you need to be strongest • You have measurably improved model behaviour through post-training and turned that work into a capability used in production. • You are deeply hands-on across data, training, evaluation and debugging; you can take an experiment from hypothesis to deployed artifact. • You understand modern post-training methods well enough to choo