Machine Learning Engineer — Job Description
Position summary
Design, build, and productionize ML models and data pipelines to deliver scalable, reliable, and monitored machine‑learning features and products.
Key responsibilities
- Model development: Research, prototype, and implement ML models (supervised, unsupervised, deep learning) tailored to product needs.
- Data engineering: Ingest, clean, and feature‑engineer large datasets; design repeatable ETL/feature pipelines.
- Productionization: Deploy models to production (model serving, APIs, batch jobs), optimize inference latency and throughput, and manage versioning.
- Scalability &* reliability:* Design systems for horizontal scaling, fault tolerance, and cost efficiency (GPU/TPU usage, autoscaling).
- Evaluation &* monitoring:* Define evaluation metrics, perform cross‑validation, A/B testing, and implement model monitoring (drift, performance, data quality).
- MLOps &* CI/CD:* Build CI/CD pipelines for model training, testing, deployment, and rollback; automate retraining and deployment workflows.
- Infrastructure &* tooling:* Work with cloud platforms and ML infrastructure (Kubernetes, Docker, MLflow, Sagemaker, Vertex AI) and optimize resource use.
- Collaboration: Partner with data scientists, product managers, software engineers, and SRE to integrate models into products and services.
- Interpretability &* fairness:* Implement model explainability, bias detection/mitigation, and privacy‑preserving techniques where applicable.
- Research &* continuous learning:* Stay current with ML research and translate innovations into practical improvements.
- Documentation &* governance:* Document model designs, assumptions, data lineage, and support compliance/audit requirements.
Required qualifications
- Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or related field (or equivalent experience).
- 2+ years experience deploying ML models to production (varies by seniority).
- Strong programming skills in Python and experience with ML libraries (TensorFlow, PyTorch, scikit‑learn).
- Experience with data processing tools (Pandas, Spark) and SQL.
- Familiarity with model serving frameworks, containerization (Docker), and orchestration (Kubernetes).
- Solid understanding of ML fundamentals (training, generalization, regularization) and evaluation metrics.
Preferred qualifications
- Advanced degree (MS/PhD) in ML/AI, applied statistics, or related field.
- Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML) and MLOps tools (MLflow, Kubeflow).
- Experience with deep learning at scale, transformers, or specialized domains (NLP, CV, recommender systems).
- Background in software engineering best practices, distributed systems, and performance optimization.
- Familiarity with privacy techniques (differential privacy, federated learning) and regulatory considerations.
Pay: $7,943.54 – $10,743.56 per month
Work Location: In person
