Applied AI/Machine Learning Engineer

Role overview

Responsibilities

• check_circle Design, develop, and deploy machine learning and AI-powered features into production systems
• check_circle Apply supervised, unsupervised, and deep learning techniques to structured and unstructured data
• check_circle Build and evaluate models for tasks such as classification, ranking, prediction, NLP, or anomaly detection
• check_circle Develop and integrate GenAI solutions (e.g., LLM-based workflows, retrieval-augmented generation, agents)
• check_circle Translate business and user needs into ML problem statements, metrics, and experiments
• check_circle Implement data pipelines and feature engineering workflows to support model training and inference
• check_circle Evaluate model performance, bias, drift, and reliability; iterate based on results
• check_circle Collaborate with software engineers to integrate models into APIs, services, and user-facing applications
• check_circle Contribute to architecture decisions around model serving, scalability, and cost optimization
• check_circle Document approaches, assumptions, and tradeoffs to support maintainability and knowledge sharing

Basic qualifications

• Strong foundation in machine learning concepts, including model selection, training, validation, and evaluation
• Experience building and deploying ML models in real-world applications
• Proficiency in Python and common ML libraries (e.g., PyTorch, TensorFlow, scikit-learn)
• Experience working with large language models, embeddings, and prompt-driven systems
• Familiarity with data processing tools and workflows (e.g., Pandas, SQL, Spark, or similar)
• Understanding of software engineering best practices (version control, testing, code reviews)
• Ability to reason about tradeoffs between accuracy, latency, cost, and maintainability
• Strong communication skills and comfort working in cross-functional teams
• Performs other related duties as assigned

Preferred qualifications

• Experience working in innovation, R&D, labs, or exploratory engineering teams
• Experience deploying models to cloud platforms and managing inference at scale
• Familiarity with MLOps practices such as model monitoring, CI/CD for ML, and experiment tracking
• Experience contributing to architectural discussions or technical strategy

Benefits

• check_circle Fully remote
• check_circle Annual stipend
• check_circle Comprehensive Benefits Package
• check_circle Company Match 401(k) plan
• check_circle Flexible PTO, Paid Holidays
• check_circle 401(k)
• check_circle 401(k) matching
• check_circle Dental insurance
• check_circle Flexible schedule
• check_circle Flexible spending account
• check_circle Health insurance
• check_circle Health savings account
• check_circle Life insurance
• check_circle Paid time off
• check_circle Parental leave
• check_circle Professional development assistance
• check_circle Referral program
• check_circle Retirement plan
• check_circle Vision insurance