Senior Machine Learning Engineering
Senior Machine Learning Engineering
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1 Vacancy
Job Description
- Team Leadership: Build mentor and grow a team of ML engineers. Define team processes establish best practices and foster a culture of innovation and continuous learning.
- Technical Strategy: Define and execute the technical roadmap for recommendation systems including infrastructure decisions model architecture choices and MLOps practices.
- MLOps Infrastructure: Design and implement robust ML pipelines for model training deployment and monitoring. Establish practices for continuous integration/deployment of ML models A/B testing frameworks and automated performance monitoring.
- Algorithm Development & Innovation: Guide the team in designing and implementing stateoftheart deep learning models for content understanding retrieval and ranking. Drive architectural decisions for scaling recommendation systems.
- System Design: Architect scalable solutions for realtime inference ensuring high availability and low latency of recommendation services. Design systems for efficient model serving and feature computation.
- Crossfunctional Leadership: Collaborate with engineering product and business leaders to align ML initiatives with company objectives. Drive technical decisions that balance innovation with business impact.
- Production Excellence: Establish metrics and monitoring systems for model performance system health and business KPIs. Lead incident response and optimization efforts for production ML systems.
- Research Direction: Set the research agenda for the team prioritizing highimpact areas for innovation. Foster collaboration with academic institutions and research communities.
- Experience: 6 years of ML engineering experience with 3 years in team leadership roles
- Education: Masters or Ph.D. in Computer Science Machine Learning or related field
- Technical Leadership:
- Proven track record of building and leading ML teams
- Experience setting technical direction for largescale ML systems
- Strong background in MLOps practices and production ML system design
- Technical Expertise:
- Deep expertise in recommendation systems ranking algorithms and deep learning
- Experience with largescale distributed systems and realtime serving architectures
- Strong knowledge of ML infrastructure including feature stores experiment platforms and model serving systems
- Infrastructure & Tools:
- Expertise with cloud platforms (AWS GCP) and containerization (Kubernetes Docker)
- Experience with ML platforms (Kubeflow MLflow or similar) and monitoring tools
- Proficiency in Python SQL and ML frameworks (PyTorch TensorFlow)
- Business Impact:
- Track record of delivering measurable business impact through ML initiatives
- Experience translating business requirements into technical solutions
- Communication & Leadership:
- Outstanding communication and stakeholder management skills
- Experience mentoring engineers and developing technical talent
- Ability to influence crossfunctional stakeholders and drive consensus
- Publication/Patents: Publications in toptier conferences (NIPS ICML RecSys) or patents in recommendation systems are a plus
recommendation systems,real-time serving architectures,gcp,python,sql,aws,kubernetes,monitoring tools,mlflow,docker,ml,kubeflow,ranking algorithms,mlops,design,machine learning,deep learning
Employment Type
Full Time
Company Industry
Key Skills
- Industrial Maintenance
- Machining
- Mechanical Knowledge
- CNC
- Precision Measuring Instruments
- Schematics
- Maintenance
- Hydraulics
- Plastics Injection Molding
- Programmable Logic Controllers
- Manufacturing
- Troubleshooting
