Senior Machine Learning Engineer

About the role

We’re looking for a Senior Machine Learning Engineer to build, deploy, and scale ML models that directly impact product and business outcomes. You’ll work closely with product, backend, and data teams to turn real‑world problems into production‑ready ML solutions. This is a hands‑on role where experimentation, ownership, and shipping matter more than academic perfection.

Key responsibilities

• Design, build, train, and deploy machine learning models for production use.
• Build end‑to‑end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
• Experiment with and rapidly prototype open‑source ML models, fine‑tune them for NLP and vision tasks, and generate synthetic data for validation.
• Review, refactor, containerize, version, and monitor data‑science models for quality and performance.
• Instrument deep observability including traces, logs, metrics, data/feature drift, model performance, safety signals, and cost tracking.
• Develop templates, SDKs, CLIs, sandbox datasets, and documentation to make shipping ML the default path.
• Design and develop privacy APIs and backend infrastructure to support large‑scale data and privacy workflows.
• Optimize models for performance, scalability, and reliability, including GPU performance optimisation of large‑scale transformers.
• Contribute to performance engineering efforts ensuring low‑latency, high‑throughput transactions at scale.
• Participate in building effective test strategies and developing software with high agility and zero downtime.

Required profile

• 8+ years of experience in machine learning.
• Proficient in Go or Python.
• Hands‑on experience building end‑to‑end ML systems and deploying models to production.
• Experience with AI‑driven software, especially NLP and NER.
• Strong knowledge of Python libraries such as NumPy, Pandas, and Scikit‑learn.

Required skills

• Deep learning frameworks: TensorFlow or PyTorch.
• Understanding of state‑of‑the‑art model architectures, especially language models.
• Experience with data pipelines, feature stores, and model lifecycle management.
• Performance engineering for high‑throughput, low‑latency systems.
• Continuous integration, testable code, and test‑driven development.
• Algorithms, data structures, scalability, and distributed systems.
• Privacy, authorization, and authentication engineering (a plus).

What we offer

• Work‑from‑home expense reimbursement.
• Excellent health insurance options.
• Very generous paid time off.
• Flexible working hours.
• Commitment to diverse and inclusive teams.