A
ASAPP
Lead Machine Learning Engineer
Skills
DockerPythonKafkaMachine LearningAIAWSKubernetesNLP / Natural Language Processing
1 month ago
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Important information
Contract type:
Permanent
Salary:
$170,000 – $190,000 / year
Location:
York, UK
Work mode:
Onsite
Published on:
17 September 2026
What they need
- At ASAPP, our mission is simple: deliver the best AI-powered customer experience—faster than anyone else. To achieve that, we’re guided by principles that shape how we think, build, and execute. We value customer obsession, purposeful speed, ownership, and a relentless focus on outcomes. ASAPP’s AI Engineering team is seeking an enterprising, talented and curious machine learning engineer
- The AI Engineering team is responsible for working closely with the research and modeling teams to create state-of-the-art NLP models for specific tasks, and deploy them in a production setting designed to serve our customers at scale
- We are looking for a Machine Learning Engineer to help build and evaluate the core intelligence behind our agentic AI systems. This role will play a key part in designing and owning evaluation frameworks that ensure quality, safety, and performance across complex agentic systems
- We’re looking for a Lead Machine Learning Engineer to own and grow the evaluation platform that measures quality, safety, and performance across ASAPP’s agentic AI systems- the infrastructure that tells us, with confidence, whether a model or agent change is actually an improvement before it reaches customers
- Help develop the technical roadmap and architecture for the evaluation platform, from offline benchmarking to online/production monitoring of agentic and LLM-based systems
- Design eval methodologies appropriate to different stages of the pipeline: golden/regression test sets, human-in-the-loop review workflows, LLM-as-judge approaches, and automated metrics for task success, safety, and hallucinations
- Build the data infrastructure evaluation depends on: annotation and labeling pipelines, dataset versioning, data quality checks, and tooling that lets researchers and product teams run and interpret experiments without needing platform team help
- Partner closely with Research, Product, and Platform teams to productize experiments into robust AI solutions
- Represent the eval platform to stakeholders outside the immediate team- set expectations on what “good” looks like for a model/agent release, and report on platform health and coverage
- Stay current with advancements in ML, NLP, voice, and LLM systems, and contribute actively to technical discussions across teams
- Mentor and support other engineers through design reviews, feedback, and knowledge sharing- Experience designing data pipelines for ML evaluation- labeling/annotation workflows, dataset versioning and quality control, and reproducible benchmarking
- A Bachelor’s Degree in CS or other related fields
- Demonstrated technical mentorship of junior and mid-level engineers, driving adoption of best practices and architectural alignment for scalability and extensibility
- Deep, hands-on experience building and operating evaluation systems for modern ML/LLM/agentic systems- not just consuming existing eval tools
- Strong architectural skills, with proven experience designing complex, data-intensive software systems and production experience with Python, AWS, Kubernetes, and/or Docker
- Demonstrated experience leading the technical direction of a project or small team: setting architecture, driving design reviews, and being accountable for a system’s long-term health (not just shipping features)
- Desire to learn, teach, and collaborate closely with cross-functional peers
- Experience with voice/audio quality evaluations
- Experience building and evaluating agentic systems at scale
- Production experience with LLM-centric services (e.g., inference, orchestration, evaluation, monitoring)
- Experience with conversational/customer-support AI domains (e.g., containment rate, conversation quality, goal completion)
- Familiarity with large-scale ML experimentation, benchmarking, or simulation frameworks
- Knowledge of techniques for optimizing model architectures for faster inference
- Experience with AWS, CI/CD, Kafka, Athena
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