Senior Machine Learning Engineer

We are a fast-growing HR tech startup backed by leading international VCs, having raised €9M+ from 360 Capital, IFF, Kfund, and 14Peaks. We are a team of 30+ professionals passionate about shaping the future of talent assessment. Skillvue is a Skill AI Assessment platform (SaaS) to hire top-skilled candidates and measure employee skill, culture, and leadership at scale to upskill and grow the workforce by leveraging AI. In today's evolving labour market, skills have become the top priority for HR departments and their importance will only continue to grow. Our platform enables companies to conduct Skill AI Assessments for both external and internal hiring, as well as targeted evaluations across their entire workforce.Role overview

You will own end-to-end ML systems: model training, fine‑tuning, deployment, monitoring, and cost/performance optimization. Partner closely with organizational psychologists, people scientists, and software engineering to productionize LLMs, real‑time conversational agents, and ML pipelines. You will report to the Head of AI & Science and drive engineering best practices, reliability, and reproducibility across the stack.

• Design, build, and maintain end-to-end ML platforms and pipelines: data ingestion, feature engineering, training, validation, deployment, and monitoring.
• Develop, fine‑tune, and deploy LLMs and GenAI services for assessment tasks (prompt engineering, instruction tuning, RLHF/IL, retrieval‑augmented generation).
• Implement scalable, low‑latency inference systems (serverless and/or containerized), real‑time voice/text conversational agents, and batching strategies for cost‑effective throughput.
• Build infrastructure‑as‑code (Terraform/CloudFormation) for reproducible environments and secure, compliant deployments.
• Create automated CI/CD for data, models, and infra (model/data versioning, reproducible training runs, canary/blue‑green deployments).
• Optimize model size and inference cost using quantization, pruning, distillation, sharding, and hardware‐aware optimizations.
• Implement monitoring, observability, drift detection, and alerting for model performance and data pipeline health; run A/B and multivariate experiments to validate model changes.
• Integrate vector databases, retrieval pipelines, and caching strategies for RAG systems; manage embeddings lifecycle and similarity search performance.
• Ensure data and model governance: lineage, access controls, privacy safeguards, and auditability.

• Bachelor or Master’s degree in Computer Science or related field.
• 7+ years experience in ML engineering/ML‑Ops delivering production ML products.
• 3+ years practical experience training and deploying GenAI/LLMs in production.
• Strong production experience on AWS (SageMaker, Lambda, ECS/EKS, Bedrock experience is a plus).
• Proven track record building highly scalable services and real‑time systems.
• Experience with infrastructure‑as‑code (Terraform, CloudFormation) and container orchestration (Docker, Kubernetes).
• Hands‑on experience with ML pipeline and experiment platforms (MLflow, Weights & Biases, Kubeflow, Airflow/Prefect).
• Proficiency in Python and TypeScript; solid software engineering practices and Git workflows.
• Experience implementing model monitoring, drift detection, and A/B testing for ML models.
• Familiarity with vector DBs (e.g., Qdrant), retrieval pipelines, and prompt/agent design.
• Fluency in English (C1) and strong communication for cross‑functional collaboration.

• Experience with Bedrock, SageMaker, or other managed LLM infrastructures.
• Experience of deploying in multimodal models, speech‑to‑text, text‑to‑speech, and building voice‑based conversational agents.
• Experience with distributed training frameworks (Horovod, DeepSpeed, ZeRO) and model parallelism.
• Knowledge of model compression, quantization toolchains (ONNX, TensorRT, Optimum), and cost‑optimization strategies.
• Familiarity with feature stores and online/offline serving (Feast, Tecton).
• Prior experience in HR tech, assessment, or conversational assessment/coaching systems.
• Contributions to open‑source ML infra or published ML blog posts or conference papers.

• Opportunity to shape and scale AI systems at an early‑stage company with real product impact.
• Close collaboration with researchers and product teams to deploy scientifically grounded ML features.
• Remote work (within the EU timezone)
• Competitive compensation, flexible work, and budget for conferences, training, and research resources.
• A collaborative, flat environment where engineering leadership influences product and research direction.