Machine Learning Engineer Two Jobs in New York (NOW HIRING)

Staff Machine Learning Engineer

Overview:As a Staff Machine Learning Engineer, you will be the overall tech lead of asingleAI/Machine Learning team, responsible for the tech design and tech health of the team. You will build and architect scalable and reliable AIML solutions that align with the company's tech paved path and stakeholder requirements. This role requires a minimum of 6 years of relevant experience.

Key Responsibilities:

System development: Architect scalable and reliable AIML solutions that align with the company's tech paved path and stakeholder requirements.

Establish ML Best Practice:Develop and implement Software Development Lifecycle (SDLC) best practices for machine learning projects, ensuring scalable, secure, and reliable systems from model development to production deployment. Expected to stay hands-oncodingabout 70% of the time.

Product Leadership & Feature Backlogs: Define the product roadmap for machine learning solutions andestablishfeature backlogs. Prioritize key ML features in collaboration with product managers, aligning them with businessobjectivesand technical feasibility.

OptimizeModel Performance and ReliabilityDebug and troubleshoot model performance issues, track key metrics, and continuously enhance model reliability, speed, and efficiency in production environments.

End-to-End Model Lifecycle Management:Own the complete lifecycle of ML models, including monitoring, retraining, finetuning and managing versions of models to ensure they continue to meet business needs over time.

Leadership and Mentorship:Guide and mentor machine learning engineers, promote best practices in software engineering, model development, and deployment. Lead technical decision-making processes and foster collaboration within the team.

Minimal Qualifications:

• Bachelor's degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related field; an advanced degree (master's or Ph.D.) is highly desirable

• At least 6 years of hands-on experience in machine learning and software engineering.

• Technical Skills:

• Deepproficiencyin programming languages such as Python, Java, or similar, with a strong emphasis on coding excellence.

• Proficiencyin AIML frameworks such as TensorFlow,PyTorch, Scikit-learn,Langchain,langraph, etc.

• Experience with SQL, Spark, and scripting languages such as Python for data processing and model development.

• Expertisein cloud platforms (AWS, Azure, GCP) and containerization technologies such as Docker, as well as orchestration tools like Kubernetes.

• Proven experience in deploying machine learning systems in a production environment, ensuring scalability, reliability, and high availability.

• Core Engineering Skills:

• Extensive experience with object-oriented design (OOD), design patterns, and writing clean, maintainable code.

• Solid understanding of distributed systems and the challenges associated with scaling machine learning models in production.

• Expertisein implementingMLOpspractices, including setting up continuous integration (CI), continuous delivery (CD), automated testing, and deployment pipelines for machine learning models.

• Strong understanding of system architecture, performance optimization, and the ability to design fault-tolerant systems that handle large-scale data and high-volume requests.

• Experience designing, building, and maintaining ETL pipelines, streamlining data collection, transformation, and storage for model development.

• Proficient in containerizing applications using Docker and managing deployment and scaling using Kubernetes or similar orchestrators.

• Experience setting up monitoring and logging systems for tracking model performance in production environments and ensuring efficient resourceutilization.

PreferredQualifications:

• 3 yearsinterfacing directly withinternal business stakeholders and/or external stakeholders on AIML initiatives

• Workingexperiencewithcloud provider solutions such as Azure and AWS

• Experienceutilizingboth open source (e.g.llama, Qwen, Mistral) and proprietary (e.g.GPT, Claude) LLMs forappropriate tasks

• Experiencewith tools that power LLM-based AI agents: eval frameworks, agent tooling, RAG pipelines, prompt engineering, etc.

• Experiencebuilding LLM-based AI agent workflows via both no code/low code and traditional high-code development environments

• Experience inideating, integrating, and designing applications and frontends using React or similar.

If you are passionate about pushing the boundaries of machine learning technology, thrive in a hands-on technical leadership role, and enjoy solving complex, large-scale problems, we encourage you to apply.