{"id":3848351,"url":"https://alion.io/job/physicsx-ai-engineer-post-training","title":"AI Engineer: Post-Training","company":{"id":251,"name":"PhysicsX","domain":"physicsx.ai","url":"https://alion.io/company/physicsx","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":88,"open_postings":35,"ghost_share":0.114,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":98,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"Model Distillation","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLAIF","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Function Calling","optional":true},{"name":"Reward Modeling","optional":true}],"status":"live","first_seen_at":"2026-11-05T22:58:11Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-07T04:34:16Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":1},"description":"About us\nRe-architecting Engineering for the Age of Intelligence\n\nPhysicsX 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.\nThe role\nYou will own the product capability that lets PhysicsX and its customers improve LLMs for their specific work.\nYou 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.\nThe 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.\nWhat you will do\nOwn the path from a base model and customer workflow to a measured, deployable model improvement.\nBuild reliable post-training harnesses covering data preparation, training, evaluation, model artifacts and hand-off to serving.\nImprove 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.\nDevelop and operate methods spanning SFT, preference optimisation, RLHF/RLAIF, RL with verifiable rewards, distillation, rejection sampling and related techniques.\nBuild reproducible synthetic-data and expert-data pipelines, including quality filtering, provenance, licensing and contamination controls.\nDesign reward signals, verifiers and rollout workflows while detecting reward hacking, benchmark overfitting, model collapse and unintended regressions.\nMake post-training available through a mix of managed workflows and expert-facing interfaces.\nEnsure customer data and resulting model artifacts can remain within the customer's platform environment across SaaS, VPC and air-gapped deployments.\nWork on cross-product and public benchmarks. Own experiment diagnostics with Research, with shared approval where results support product or external claims.\nWork with the LLM Self-Hosting & Model Routing Lead to move trained models into supported production serving and routing.\nSet the product roadmap for model improvement, balancing research opportunity with customer value, operability and time to production.\nHelp determine how this capability should grow and, over time, shape the team around it.\nWhat you bring\nWhere you need to be strongest\nYou have measurably improved model behaviour through post-training and turned that work into a capability used in production.\nYou are deeply hands-on across data, training, evaluation and debugging; you can take an experiment from hypothesis to deployed artifact.\nYou understand modern post-training methods well enough to choose among them based on the task and evidence rather than fashion.\nYou have built high-quality training datasets from expert, behavioural or synthetic sources and understand that data design is part of model design.\nYou can diagnose training and behavioural failures, including instability, drift, reward exploitation, leakage and overfitting to evals.\nYou are product-minded: you start from a user workflow, define success, make trade-offs and build an experience others can operate.\nYou can work as the accountable product owner of a technical capability while collaborating closely with research scientists, platform engineers and product teams.\nFoundations\nStrong Python and PyTorch or equivalent model-training frameworks.\nDistributed training, accelerator utilisation, checkpointing, recovery, experiment tracking and reproducibility.\nEvaluation of model and agent behaviour, including controlled experiments and ablations.\nData governance, privacy, provenance and safe handling of customer-owned training data and artifacts.\nModel packaging and the operational path from training to evaluation and production serving.\nClear technical writing and communication across research, product, engineering, delivery and customer teams.\nNice to have\nExperience post-training open-weight models for tool use, reasoning or long-horizon agent workflows.\nExperience with large-scale rollout generation, reward models, verifiers or online reinforcement learning.\nExperience building managed and expert-facing model customisation products.\nContributions to post-training research or open-source training frameworks.\nExperience with engineering, simulation, design, manufacturing or other expert workflows.\nExperience operating in regulated, customer-controlled or air-gapped environments.\nWe value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. \nWe collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. 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