Senior Machine Learning Engineer

Angi

. Lead development of advanced machine learning and AI models for marketplace algorithms, including search ranking, recommendation, and matching solutions .

Posted 9/16/2026full-timeRemote • United StatesSenior💰 $200,000 - $250,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading the development and deployment of advanced machine learning models, with a strong focus on MLOps practices, evaluation frameworks, and collaboration across technical teams. Proficient in optimizing model performance and guiding junior team members in a fast-paced, innovative environment.

Highest-signal resume keywords

MLOps Lifecycle ManagementMachine Learning Model OptimizationHands-On Experience with LLMsProficiency in Python and SQLAdvanced Machine Learning Algorithms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningMLOpsModel Evaluation MetricsModel MonitoringData PipelinesCI/CD for MLFine-TuningQuantizationPerformance Tuning

Soft Skills

Excellent CommunicationTeam LeadershipKnowledge SharingCollaborationContinuous Learning

Tools & Technologies

TensorFlowPyTorchSQLVLLMSGLang

Certifications & Qualifications

Master’s or Ph.D. in Quantitative Field

Industry Keywords

Marketplace AlgorithmsSearch RankingRecommendation SystemsInformation RetrievalDistributed Application Architecture

Tech Stack

Tools & technologies

PythonPyTorchSQLTensorflow

About the role

Key responsibilities & impact

Lead development of advanced machine learning and AI models for marketplace algorithms, including search ranking, recommendation, and matching solutions
Design and architect robust MLOps practices for seamless deployment and scalability of machine learning models, including self-hosted large language models
Automate model training and post-training, including fine-tuning, RLHF/preference alignment, and distillation
Optimize model runtime performance and inference cost
Own the full MLOps lifecycle from data pipelines and experiment tracking through CI/CD, model registry, monitoring, and rollback
Define and own evaluation frameworks for deep learning and ML systems, including offline metrics, online experimentation, guardrail metrics, and LLM-specific evaluation
Collaborate with engineers, ML infrastructure team members, data scientists, and product managers on scalable machine learning systems
Contribute to the end-to-end development process from ideation to deployment
Assist in developing long-term technical vision and propose team roadmaps
Design and implement new products and features and enhance existing products with machine learning capabilities
Guide junior team members and foster continuous learning and technical excellence
Lead knowledge sharing on advanced machine learning techniques

Requirements

What you’ll need

Master’s or Ph.D. in a quantitative field, such as Computer Science, Statistics, Mathematics, or a related discipline
4+ years of experience in data science and machine learning, ideally within the tech industry and marketplace environments
Hands-on experience post-training and self-hosting open-weight LLMs, including fine-tuning, quantization, and serving infrastructure such as vLLM and SGLang
Deep understanding of evaluation metrics across deep learning and classical ML
End-to-end fluency in the MLOps lifecycle, including data versioning, feature stores, CI/CD for ML, model monitoring/observability, and retraining pipelines
Knowledge of large-scale distributed application architecture, design, implementation, and performance tuning
Ability to drive the roadmap and direction of scalable, production-quality systems and communicate cross-functionally with Product and Engineering teams
Practical knowledge of advanced machine learning algorithms and deep learning for search systems, information retrieval, and ranking algorithms
Proficiency in SQL, Python, and ML frameworks such as TensorFlow and PyTorch, with strong coding skills
Excellent communication skills, with the ability to convey complex technical concepts to non-technical stakeholders

Benefits

Comp & perks

Competitive year end performance bonus
Equity package
Full medical, dental, vision package
Flexible vacation policy
Pet discount plans
Retirement plan with company match (401K)
Opportunity to work with sharp, motivated teammates on unique challenges