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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
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 teams. Proficient in optimizing model performance and implementing scalable solutions in production environments.
Highest-signal resume keywords
MLOps Lifecycle ManagementMachine Learning Model OptimizationHands-On Experience with LLMsProficiency in Python and SQLDeep Learning Evaluation Metrics
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep LearningMLOpsModel DeploymentModel MonitoringFine-TuningQuantizationExperiment TrackingCI/CD for MLData Pipelines
Soft Skills
Excellent Communication SkillsMentoringCollaboration
Tools & Technologies
TensorFlowPyTorchVLLMSGLang
Certifications & Qualifications
Master’s or Ph.D. in Quantitative Field
Industry Keywords
Search RankingRecommendationsMatching SolutionsA/B TestingGuardrail MetricsDistributed Application Architecture
Tech Stack
Tools & technologiesPythonPyTorchSQLTensorflow
About the role
Key responsibilities & impact- Lead development of advanced machine learning and AI models for marketplace algorithms, including search ranking, recommendations, and matching solutions
- Design and architect robust MLOps practices for seamless model deployment and scalability, including self-hosted LLMs
- 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, including data pipelines, experiment tracking, CI/CD, model registry, monitoring, and rollback
- Define and own evaluation frameworks for deep learning and ML systems
- Establish offline metrics, online experimentation, A/B testing, guardrail metrics, and LLM-specific evaluations
- Collaborate with engineers, ML infrastructure teams, data scientists, and product managers on scalable ML systems
- Develop the long-term technical vision and roadmap for the team
- Design and implement new products and features and enhance existing products with ML capabilities
- Mentor junior team members and encourage knowledge sharing
Requirements
What you’ll need- Master’s or Ph.D. in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline
- 6+ years of experience in data science and machine learning
- Hands-on experience post-training and self-hosting open-weight LLMs, including fine-tuning, quantization, and serving infrastructure such as vLLM or 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
- 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
- Strong coding skills
- Excellent communication skills and 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