About this opportunity
Weekday (YC W21) lists this Machine Learning Engineer opportunity in new york, New York. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Location: New York City, NY
Experience: 5+ years
Compensation: $150,000–$250,000 per year
Employment Type: Full-Time
Role Overview
We are looking for an experienced Machine Learning Engineer to design, build, deploy, and scale production machine learning systems. The ideal candidate combines strong machine learning expertise with excellent software engineering fundamentals and has experience taking models from experimentation through reliable production deployment.
You will work closely with software engineers, data scientists, product teams, and other technical stakeholders to develop ML-powered products and systems. This role requires hands‑on experience with model development, data pipelines, model serving, evaluation, monitoring, and production ML infrastructure.
Requirements
Key Responsibilities
Design, develop, train, evaluate, and deploy machine learning models for production applications.
Own the complete ML lifecycle, from data preparation and feature engineering through model training, deployment, monitoring, and retraining.
Build scalable ML pipelines for batch and real‑time inference.
Develop production‑quality Python code and integrate ML models with backend services and APIs.
Work with large datasets to identify patterns, build predictive models, and improve model performance.
Develop and optimize deep learning models using frameworks such as PyTorch or TensorFlow.
Build model‑serving infrastructure and optimize models for latency, throughput, reliability, and cost.
Implement ML monitoring, evaluation, experimentation, and model‑quality tracking.
Collaborate with data scientists and research teams to productionize experimental models.
Design and maintain ML infrastructure using cloud platforms and containerised environments.
Develop automated training and deployment workflows using MLOps best practices.
Investigate model performance, data‑quality issues, model drift, and production failures.
Participate in architecture and system‑design discussions for ML platforms and applications.
Mentor junior engineers and contribute to engineering standards and best practices.
Must‑Have Skills
5+ years of professional experience in Machine Learning Engineering, ML Infrastructure, or a closely related field.
Strong proficiency in Python and production software engineering.
Strong understanding of machine learning algorithms, statistics, model evaluation, and experimentation.
Hands‑on experience with PyTorch, TensorFlow, or equivalent ML frameworks .
Experience building and deploying production machine learning models.
Strong experience with data processing, feature engineering, and ML pipelines.
Experience with REST APIs, distributed systems, and scalable software architecture.
Experience with cloud platforms such as AWS, GCP, or Azure .
Experience with Docker and production deployment environments.
Strong understanding of SQL and experience working with large‑scale datasets.
Experience with model monitoring, experiment tracking, and MLOps practices.
Good‑to‑Have Skills
Experience with Kubernetes and cloud‑native ML infrastructure.
Experience with MLflow, Kubeflow, Ray, Weights & Biases, or similar ML tooling.
Experience with Spark, Kafka, Airflow, or other distributed data technologies.
Experience with LLMs, generative AI, RAG, fine‑tuning, or AI agents.
Experience with recommendation systems, ranking, personalization, NLP, computer vision, or time‑series modeling.
Experience with distributed model training and GPU infrastructure.
Experience with model optimization, quantization, inference acceleration, or specialised ML hardware.
Experience with Terraform and CI/CD pipelines.
Experience working in a high‑growth startup or product‑focused engineering organization.
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Worksite address
new york, NY, 10261, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.