Staff Machine Learning Engineer, Content Mining
Staff Machine Learning Engineer, Content Mining
Job Description
Responsibilities
- Own the long-term architecture, roadmap, and execution for source discovery, acquisition optimization, and content understanding.
- Lead design reviews, set engineering standards, and drive cross-team alignment with Product, Data, and Infra.
- Mentor and uplevel MLEs through technical direction, pairing, and reviews.
- Train/fine-tune LLMs and NLP models for classification, extraction, and instruction-following; design eval loops and guardrails.
- Design features and frameworks for sharing features across models.
- Productionize models for large-scale inference; drive latency, reliability, and cost efficiency (quantization, distillation, caching).
- Establish offline/online evaluation, gold sets, and automated regressions; run A/B and canary/shadow launches.
- Work with human and automated labeling sources to define data labeling standards.
- Partner on data strategy, labeling/weak supervision, and feedback loops to expand coverage and improve precision/recall.
- Define and meet SLOs for data quality, model performance, and serving reliability; lead incident playbooks and postmortems.
- Measure and drive downstream impact on revenue and engagement.
Qualifications
- 5+ years building ML products end-to-end, including 2+ years as a tech lead driving multi-quarter roadmaps and cross-functional execution.
- Deep hands-on experience with NLP/LLM training and inference (PyTorch, Python); strong grounding in evaluation, prompt/data design, and fine-tuning.
- Proven track record shipping models at scale: feature/data pipelines, online serving, monitoring/observability, and cost/perf trade-offs.
- Strong software engineering in Python with an eye for software engineering best practices.
- Experience mentoring senior engineers and influencing partner teams.
- Masters or PhD in ML related studies.
Preferred Qualifications
- LLM efficiency techniques (LoRA/adapters, distillation, quantization, prompt caching) and cost control strategies.
- MLOps at scale with tools like Airflow, Spark/Presto, Triton, vLLM.
Locations
- Toronto, ON, CA; Remote, CA (Remote)
Salary
Estimated Salary Rangemedium confidence
220,000 - 350,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- NLP/LLM training and inferenceintermediate
- PyTorchintermediate
- Pythonintermediate
- Evaluation, prompt/data design, and fine-tuningintermediate
- Feature/data pipelinesintermediate
- Online servingintermediate
- Monitoring/observabilityintermediate
- Cost/perf trade-offsintermediate
- Software engineering best practicesintermediate
- Mentoring senior engineersintermediate
- LLM efficiency techniques (LoRA/adapters, distillation, quantization, prompt caching)intermediate
- Cost control strategiesintermediate
- MLOps with Airflow, Spark/Presto, Triton, vLLMintermediate
Required Qualifications
- 5+ years building ML products end-to-end, including 2+ years as a tech lead driving multi-quarter roadmaps and cross-functional execution. (experience)
- Deep hands-on experience with NLP/LLM training and inference (PyTorch, Python); strong grounding in evaluation, prompt/data design, and fine-tuning. (experience)
- Proven track record shipping models at scale: feature/data pipelines, online serving, monitoring/observability, and cost/perf trade-offs. (experience)
- Strong software engineering in Python with an eye for software engineering best practices. (experience)
- Experience mentoring senior engineers and influencing partner teams. (experience)
- Masters or PhD in ML related studies. (experience)
Preferred Qualifications
- LLM efficiency techniques (LoRA/adapters, distillation, quantization, prompt caching) and cost control strategies. (experience)
- MLOps at scale with tools like Airflow, Spark/Presto, Triton, vLLM. (experience)
Responsibilities
- Own the long-term architecture, roadmap, and execution for source discovery, acquisition optimization, and content understanding.
- Lead design reviews, set engineering standards, and drive cross-team alignment with Product, Data, and Infra.
- Mentor and uplevel MLEs through technical direction, pairing, and reviews.
- Train/fine-tune LLMs and NLP models for classification, extraction, and instruction-following; design eval loops and guardrails.
- Design features and frameworks for sharing features across models.
- Productionize models for large-scale inference; drive latency, reliability, and cost efficiency (quantization, distillation, caching).
- Establish offline/online evaluation, gold sets, and automated regressions; run A/B and canary/shadow launches.
- Work with human and automated labeling sources to define data labeling standards.
- Partner on data strategy, labeling/weak supervision, and feedback loops to expand coverage and improve precision/recall.
- Define and meet SLOs for data quality, model performance, and serving reliability; lead incident playbooks and postmortems.
- Measure and drive downstream impact on revenue and engagement.
Tags & Categories
Staff Machine Learning Engineer, Content Mining
Staff Machine Learning Engineer, Content Mining
Job Description
Responsibilities
- Own the long-term architecture, roadmap, and execution for source discovery, acquisition optimization, and content understanding.
- Lead design reviews, set engineering standards, and drive cross-team alignment with Product, Data, and Infra.
- Mentor and uplevel MLEs through technical direction, pairing, and reviews.
- Train/fine-tune LLMs and NLP models for classification, extraction, and instruction-following; design eval loops and guardrails.
- Design features and frameworks for sharing features across models.
- Productionize models for large-scale inference; drive latency, reliability, and cost efficiency (quantization, distillation, caching).
- Establish offline/online evaluation, gold sets, and automated regressions; run A/B and canary/shadow launches.
- Work with human and automated labeling sources to define data labeling standards.
- Partner on data strategy, labeling/weak supervision, and feedback loops to expand coverage and improve precision/recall.
- Define and meet SLOs for data quality, model performance, and serving reliability; lead incident playbooks and postmortems.
- Measure and drive downstream impact on revenue and engagement.
Qualifications
- 5+ years building ML products end-to-end, including 2+ years as a tech lead driving multi-quarter roadmaps and cross-functional execution.
- Deep hands-on experience with NLP/LLM training and inference (PyTorch, Python); strong grounding in evaluation, prompt/data design, and fine-tuning.
- Proven track record shipping models at scale: feature/data pipelines, online serving, monitoring/observability, and cost/perf trade-offs.
- Strong software engineering in Python with an eye for software engineering best practices.
- Experience mentoring senior engineers and influencing partner teams.
- Masters or PhD in ML related studies.
Preferred Qualifications
- LLM efficiency techniques (LoRA/adapters, distillation, quantization, prompt caching) and cost control strategies.
- MLOps at scale with tools like Airflow, Spark/Presto, Triton, vLLM.
Locations
- Toronto, ON, CA; Remote, CA (Remote)
Salary
Estimated Salary Rangemedium confidence
220,000 - 350,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- NLP/LLM training and inferenceintermediate
- PyTorchintermediate
- Pythonintermediate
- Evaluation, prompt/data design, and fine-tuningintermediate
- Feature/data pipelinesintermediate
- Online servingintermediate
- Monitoring/observabilityintermediate
- Cost/perf trade-offsintermediate
- Software engineering best practicesintermediate
- Mentoring senior engineersintermediate
- LLM efficiency techniques (LoRA/adapters, distillation, quantization, prompt caching)intermediate
- Cost control strategiesintermediate
- MLOps with Airflow, Spark/Presto, Triton, vLLMintermediate
Required Qualifications
- 5+ years building ML products end-to-end, including 2+ years as a tech lead driving multi-quarter roadmaps and cross-functional execution. (experience)
- Deep hands-on experience with NLP/LLM training and inference (PyTorch, Python); strong grounding in evaluation, prompt/data design, and fine-tuning. (experience)
- Proven track record shipping models at scale: feature/data pipelines, online serving, monitoring/observability, and cost/perf trade-offs. (experience)
- Strong software engineering in Python with an eye for software engineering best practices. (experience)
- Experience mentoring senior engineers and influencing partner teams. (experience)
- Masters or PhD in ML related studies. (experience)
Preferred Qualifications
- LLM efficiency techniques (LoRA/adapters, distillation, quantization, prompt caching) and cost control strategies. (experience)
- MLOps at scale with tools like Airflow, Spark/Presto, Triton, vLLM. (experience)
Responsibilities
- Own the long-term architecture, roadmap, and execution for source discovery, acquisition optimization, and content understanding.
- Lead design reviews, set engineering standards, and drive cross-team alignment with Product, Data, and Infra.
- Mentor and uplevel MLEs through technical direction, pairing, and reviews.
- Train/fine-tune LLMs and NLP models for classification, extraction, and instruction-following; design eval loops and guardrails.
- Design features and frameworks for sharing features across models.
- Productionize models for large-scale inference; drive latency, reliability, and cost efficiency (quantization, distillation, caching).
- Establish offline/online evaluation, gold sets, and automated regressions; run A/B and canary/shadow launches.
- Work with human and automated labeling sources to define data labeling standards.
- Partner on data strategy, labeling/weak supervision, and feedback loops to expand coverage and improve precision/recall.
- Define and meet SLOs for data quality, model performance, and serving reliability; lead incident playbooks and postmortems.
- Measure and drive downstream impact on revenue and engagement.
Tags & Categories