Applied ML Engineer

About This Opportunity

We are representing a confidential machine learning technology company seeking an Applied ML Engineer based in the United Kingdom. This role sits at the intersection of ML research, rigorous experimentation, and production engineering. You'll transform emerging techniques from academic research into measurable experiments and reliable production systems.

Our talent team is looking for someone who can work hands-on with modern ML models, design evaluations that reveal what methods can genuinely demonstrate, and build production-grade tooling that makes complex experiments repeatable and accessible. This is an opportunity to help transform emerging ML verification and evaluation techniques into practical systems.

Responsibilities

• Reproduce and evaluate machine learning research methods using open-weight and API-accessible models
• Design evaluation datasets, probes, scoring approaches, baselines, calibration tests, and experiment harnesses
• Work directly with model weights, logits, hidden states, activations, and inference infrastructure
• Build and extend evaluation infrastructure including experiment runners, persistence, orchestration, and reporting systems
• Turn research workflows into intuitive product experiences with experiment configuration, execution traces, comparisons, and review workflows
• Investigate how verification methods behave when models undergo fine-tuning, merging, quantization, distillation, or other modifications
• Design controlled experiments that distinguish meaningful signals from artifacts and confounders
• Produce clear technical reports separating measured evidence from interpretation
• Deliver production-quality systems with APIs, asynchronous jobs, databases, observability, testing, and deployment
• Contribute across research, experimentation, engineering, and product as priorities evolve

What We're Looking For

Required:

• Strong Python engineering skills with hands-on PyTorch and Hugging Face Transformers experience
• Solid understanding of ML evaluation including dataset design, baselines, metrics, calibration, and reproducibility
• Ability to read ML research papers critically and implement methods from first principles
• Professional software engineering experience beyond notebooks—APIs, async jobs, databases, testing, deployment, and documentation
• Familiarity with open-weight models and practical understanding of modern LLM inference systems
• Ability to work across backend and frontend with sufficient React/TypeScript knowledge to build user-facing experiment interfaces
• Strong experimental and analytical judgment, particularly distinguishing what evidence demonstrates versus suggests
• High ownership and initiative with ability to identify problems and drive projects independently
• Comfort in fast-moving startup environments where priorities shift and engineers operate across multiple functions

Preferred:

• Experience with model provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluation, or interpretability
• Experience with activation analysis, probing, model hooks, or other model-internals techniques
• Familiarity with evaluation infrastructure such as DSPy, LiteLLM, Temporal, Ray, vLLM, or PostgreSQL/pgvector
• Experience with Next.js, React, TypeScript, data visualization, or experiment dashboards
• Experience running and serving open-weight models on GPUs with understanding of latency, throughput, memory, and cost trade-offs
• Experience designing adversarial evaluations or testing against deliberate evasion attempts

How We Work With You

Candidates apply directly through Get A Job.ai. Our recruiting team will screen applications and conduct initial interviews to understand your experience with ML evaluation, production engineering, and cross-stack development. We'll then submit qualified candidates to our client for their review and interview process.

Please do not attempt to contact the client directly. All communication and coordination will flow through our talent team to ensure a smooth, professional process for everyone involved.

Pay

Compensation details will be discussed during the screening process and vary based on experience and the client's total compensation package.

Equal Opportunity: Get A Job.ai is committed to fair and equitable recruiting practices. We evaluate all candidates based on qualifications and role requirements without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other protected characteristic.