Senior AI​/ML Engineer; Freelance - Remote EU

Who we are

Besedo is a tech company delivering Trust & Safety solutions to online marketplaces worldwide. Every year, we review and moderate over 500 million pieces of user-generated content and block more than 40 million fraud attempts – helping online platforms stay safe and trusted for millions of users.

Besedo has headquarters in Stockholm (Sweden) and employs over 350 employees in 5 offices in different countries. Implio is our all-in-one SaaS moderation platform that combines state-of-the-art AI (deep learning, NLP, computer vision) with a powerful user interface for human moderation and powerful analytics. The result is an easy-to-use content moderation solution that enables companies to reduce risks, protect their users and build safer online environments.

Learn more about Implio in this video, and discover more about Besedo on our website.

The role

We are looking for a senior-level AI/ML professional to lead the productionization, deployment, and continuous optimization of ML systems in mission-critical environments. This role requires strong technical leadership, deep hands-on experience shipping models to production, and the ability to drive reliability, scalability, and cost/performance trade-offs end-to-end. The ideal candidate combines strong ML depth with MLOps and production engineering excellence
, and can operate autonomously while influencing architecture and best practices across teams.

You’ll join a small, senior ML team building the AI behind Implio, which moderates user-generated content for online platforms ’ll work across the full lifecycle, from model research to production, and help shape how we build and ship ML.

What we’re looking for

• Master’s, PhD, or equivalent experience
  in Computer Science, Engineering, Data Science (or related), with strong foundations in mathematics, statistics, and ML.

• 6+ years
  (or equivalent) experience across ML engineering and MLOps/production ML, with a proven track record delivering high-impact systems in production
  .

• Demonstrated expertise deploying models to production
  : scalable serving patterns, model packaging, inference optimization, observability, and safe rollout strategies.

• Strong experience with
  model performance and efficiency optimization
  : latency/throughput tuning, batching, quantization, ONNX/TensorRT, GPU/CPU optimization, memory and cost tuning.

• Strong proficiency in
  Python
  .

• Hands‑on experience with ML frameworks such as
  PyTorch, scikit-learn
  , and optimization/runtime stacks like
  ONNX, TensorRT
  .

• Strong grasp of
  CI/CD for ML
  (e.g., Git Lab CI), containerization (Docker), and reproducible workflows.

• Experience with
  monitoring and reliability
  : metrics/logging/tracing, model/data drift, alerting, SLOs, incident response practices.

• Plus (nice to have):Kubernetes, AWS, FastAPI (and/or similar Python serving frameworks), Sage Maker, service meshes, feature stores, model registries.

What you’ll own

• Handle the production lifecycle
  of ML systems: training, packaging, deployment, monitoring and controlled rollouts.

• Design, build, and continuously improve
  training and retraining pipelines
  (data ingestion, labeling/QA loops, feature generation, experiment tracking, reproducibility).

• Build and improve
  high-performance inference services
  and deployment patterns.

• Drive
  inference optimization
  (ONNX/TensorRT, quantization, batching, GPU/CPU tuning) with measurable latency/cost improvements.

• Partner with Dev Ops/SRE to ensure
  operational excellence
  : scaling, reliability, incident readiness, observability, and alerting.

• Own
  model versioning, CI/CD, and release workflows ensuring reproducibility and safe delivery to production.

• Strengthen
  data & model monitoring
  (performance, drift, quality) and implement feedback loops for continuous improvement.

• Communicate results and…