Machine Learning Engineer – II

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. Research, design, develop, and maintain state-of-the-art machine learning models .

Posted 9/21/2026full-timeUnited StatesMid-LevelSenior💰 $124,000 - $186,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and deploying machine learning models, with a strong foundation in Python and experience with advanced ML techniques. Capable of leading projects, mentoring team members, and collaborating across departments to drive innovative data solutions.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingAWS Services ProficiencyPyTorch ExpertiseData Pipeline Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning TechniquesFeature EngineeringStatistical InferenceLinear AlgebraStochastic OptimizationTime Series AnalysisAnomaly DetectionClassificationRegressionInfrastructure-as-Code

Soft Skills

Analytical SkillsProblem-SolvingCommunicationTeamworkAdaptability

Tools & Technologies

PyTorchNLTKSpacyOpenCVTesseractHuggingfaceLangchainLlamaindexN8nSpringAI

Industry Keywords

Generative AI ServicesLarge-Scale DatasetsMaster Service AgreementsCompliance RequirementsReal-Time Data Products

Tech Stack

Tools & technologies

Amazon RedshiftAWSJavaKotlinPythonPyTorchC++

About the role

Key responsibilities & impact

Research, design, develop, and maintain state-of-the-art machine learning models
Train, evaluate, and fine-tune models for model selection, validation, accuracy, latency, and throughput
Recommend strategies for scalability, tuning, and data infrastructure configuration
Design and implement end-to-end data and ML pipelines for real-time data products
Source, clean, and perform feature engineering on raw data
Productionize and deploy ML models into existing systems
Monitor deployed models for efficacy, throughput, and latency
Influence software architecture decisions for high-volume datasets
Lead ML projects from conception through production deployment
Mentor junior team members and champion best practices
Collaborate with Product, Engineering, Marketing, Customer Success, Sales, and other product-development teams
Conceptualize, research, and develop customer-facing features and predictive models
Communicate complex technical concepts through knowledge-sharing sessions
Own, shape, and prioritize work with minimal oversight
Establish stakeholder relationships and foster collaborative, inclusive, and continuously improving team culture
Use AI development tools to explore ideas, prototype, interpret data, and experiment with LLM and agent-based systems

Requirements

What you’ll need

Ph.D. or Master’s degree in a quantitative discipline with at least four years’ experience applying advanced machine learning techniques to real-world industry challenges, or Bachelor’s degree with at least six years of directly relevant experience
Advanced programming proficiency in Python, C++, Java, or Kotlin
Hands-on expertise with PyTorch, NLTK, Spacy, OpenCV, Tesseract, and Huggingface
Understanding of linear algebra, stochastic optimization, and probability theory
Knowledge of statistical inference and machine learning, including forecasting, time series analysis, hypothesis testing, anomaly detection, classification, and regression
Experience with large-scale datasets exceeding two million training examples and highly imbalanced data
Proficiency with AWS services including ECS, Kinesis, Lambda, S3, Glue, Sagemaker, Bedrock, Athena, RDS, and Redshift
Experience architecting and deploying scalable generative AI services using Langchain, Llamaindex, n8n, and springAI
Capability in developing and maintaining infrastructure-as-code
Experience using version control systems
Understanding of sensitive-data handling under Master Service Agreements and compliance requirements
Strong analytical, problem-solving, communication, teamwork, adaptability, ownership, and attention-to-detail skills
Ability to communicate technical concepts and business implications to non-technical audiences
Successful completion of applicable background checks

Benefits

Comp & perks

Comprehensive benefits package
Formal and on-the-job learning opportunities
Hybrid working model with individual flexibility
Inclusive and diverse workplace
Interview adjustments or accommodations for disabilities or other reasons
Incentive plans may be available
Additional benefits in accordance with company policy and local regulations