U*** Y****
Machine Learning
This candidate is a deep Machine Learning expert but lacks evidence of technical sourcing or recruiting experience. Ex-Walleye Capital → Reddit (ML Engineer roles) → Amelia (Research Engineer), PhD in CS. Excellent.
This sample report shows what our sourcing engine surfaces for this search. Preview the top 30 matches below.
Machine Learning
This candidate is a deep Machine Learning expert but lacks evidence of technical sourcing or recruiting experience. Ex-Walleye Capital → Reddit (ML Engineer roles) → Amelia (Research Engineer), PhD in CS. Excellent.
Student at University of Michigan
Recent ML Engineer roles focusing heavily on implementing and optimizing models in production environments. Student → ML Engineer at QiQiEducation → Programmer at UMich, heavy neural network focus. Strong.
swe + mle | ex amazon
The candidate is a strong practitioner with ML engineering experience at Amazon and elsewhere, but shows no evidence of technical sourcing or recruiting skills. Software Engineer/Applied Scientist at Amazon Health Search and Amazon Core → ML Engineer at Glance, MS in CS from NYU Courant. Strong.
Senior Research Manager @Microsoft AI, (ex- Facebook AI& Amazon), PhD @Machine Learning
Strong technical background in Applied ML/Research with experience leading teams, but currently an Applied ML leader, not a sourcer or recruiter. Ex-Facebook AI → Microsoft (Research Manager) → Scale AI (Applied ML Lead), PhD in ML. Strong.
Principal Machine Learning Engineer at GetUpside
Extensive background as Principal ML Engineer building platforms and systems, demonstrating strong technical competency, but no experience listed in sourcing or recruiting. Ex-upside → Realtor.com → GetUpside (Principal ML Engineer). Strong.
Graduate Research Assistant @ The University of Texas at Austin | PhD in Computer Science
Strong profile involving ML research and engineering roles at major tech companies, including heavy transformer/self-supervised learning work. Data Scientist Intern at Microsoft → Applied Scientist 2 at Microsoft → AI Engineer at LinkedIn, PhD in CS. Strong.
Senior Data Scientist at Grid Dynamics
Solid background as a Data Scientist/ML Engineer specializing heavily in NLP and Conversational AI, including deep learning research. Data Scientist at Beeline → NLP Researcher at DeepPavlov → Sr DS/MLE roles. Strong.
Senior Machine Learning Researcher at Sensyne Health | Oxford Computer Science | IIT Indore
Experienced Machine Learning Researcher and Engineer with roles at Meta and strong academic credentials. Oxford MSc → SDE at Amazon → Applied Researcher → Sr ML Researcher at Sensyne/ML Engineer at Meta. Strong.
PhD, Data Mining and Machine Learning
Long tenure as a Senior ML Engineer and Applied Scientist with a strong research PhD background. Research Assistant → Applied Scientist at Amazon → Sr ML Engineer at Tubi, PhD focused on data mining/ML. Strong.
Machine Learning Engineer
Very strong background as an experienced Machine Learning Engineer, but lacks any sourcing or recruiting aspect. Staff MLE at Apple/Twitter → Co-founder, MS in ML from Georgia Tech. Strong.
Senior Machine Learning Engineer at Quantiphi
Clearly an experienced Machine Learning Engineer focused heavily on applications and deployment across various domains. Intern → Sr ML Engineer at Quantiphi, exposure to NLP/CV/Transformers. Strong.
NLP Postdoc at NYU
Strong background in ML/NLP research and consulting, suggesting high technical fluency relevant to sourcing, though direct sourcing experience is absent. Postdoc at NYU → BU Faculty starting 2026, NLP Consultant, Deep research history. Strong.
Engineering Manager at Pinterest
This candidate is a proven ML Engineering Manager who has led teams and built pipelines, but is currently in management, not sourcing, though their ML foundation is excellent. Engineering Manager at Pinterest, 9 years experience. Strong.
Machine Learning Engineer at Meta
This candidate is an experienced Machine Learning Engineer building high-impact systems at Meta. Ex-Microsoft → Meta (ML Engineer) → current ML Engineer at Meta, strong AI research background. Strong.
Machine learning Engineer @Mimecast | Building NLP Cybersecurity Solutions for Threat Detection | Prompt Engineering, LLMs, MLOps
This engineer performs high-level Machine Learning work focused on NLP/LLMs, but the profile lacks any technical recruiting or sourcing responsibilities. Mimecast MLE (NLP/Cybersecurity) → Materialise AI Dev → Stevens Research Assistant. Strong.
Data Scientist II at ZipRecruiter
Deep specialization in NLP and Machine Learning roles, yet current profile shows no inclination toward technical sourcing or recruiting activities. ZipRecruiter DS II (NLP/Learning to Rank) → GumGum NLP DS, published ML research. Strong.
MLE @ Roblox | CS @ Stanford (BS & MS)
Excellent technical alignment in Machine Learning, especially deep learning/NLP/CV, but no evidence of a sourcing or recruiting career path. Anthropic MTS → Roblox MLE (NLP) → Meta MLE (CV) → Stanford MS. Strong.
Software Development Engineer II @ AMZN
This individual has clear experience in Machine Learning/ML research roles within AWS/Amazon, but none in technical recruiting or sourcing. Amazon SDE II (Prime ML focus) → AWS → Tsinghua Intern, strong analysis skills. Strong.
Machine Learning Engineer at XtalPi Inc.
This candidate is strong in Machine Learning research and development, but has no technical sourcing background. Research Intern/RA at York/XtalPi → NLP Intern, focusing on RL/Transformers. Strong.
Machine Learning @ Atlassian
Strong recent Machine Learning Engineer/Scientist experience, but no evidence of technical sourcing or recruiting skills. Atlassian ML Scientist/DS → ElectrifAi DS → Research, MS in CS. Strong.
Machine Learning @ Pinterest
This candidate is an experienced Machine Learning professional, but lacks explicit technical sourcer/recruiting background. Pinterest ML Engineer → UCSB MS ML → Microsoft SWE → UCSB Masters ML. Strong.
Research Scientist & Tech Lead at Meta AI
Exceptional Machine Learning Scientist/Tech Lead focused on research and product impact, but lacks direct experience in technical sourcing or recruiting roles. Ex-Google Brain → Meta AI (Research Scientist & Tech Lead). Strong.
Experienced Data Scientist/ML | Actively Looking For New Roles | Python | Data Bricks | Specializing in ML/AI | RAG | Data Engineering | Deep Learning | Gen AI | Prompt Engineering | Cloud Solutions|
This candidate is a strong Machine Learning expert with extensive experience, but the profile does not indicate any technical sourcing or recruiting background. Cigna → Microsoft, 10+ years in Data Science/ML/AI. Strong.
AI & Machine Learning Engineer | Generative AI | LLMs, LangChain, RAG | GCP, AWS, Azure | Python | MLOps
Excellent fit for the Machine Learning portion, specializing in Generative AI, LLMs, and MLOps, but lacks any indicated sourcing or recruiting background. AI & ML Engineer (6.2 yrs) building GenAI solutions (GPT-4, LangChain, RAG) for various domains → spearheading personalized learning assistant projects. Machine Learning: yes. Technical Sourcer: no. Technical Recruiting: no. Sourcing experience: no. Strong.
Machine Learning Engineer @ Simeio Solutions | Applied AI, ML Ops and Data Science
This candidate has extensive applied ML/AI engineering experience, including NLP/chatbot design, but zero matching experience in sourcing or recruiting. ML Engineer (8.8 yrs) architecting chatbot systems (NLP/NLU) and deploying ML models → GenAI Architect orchestrating agentic workflows (Langgraph/OpenAI). Machine Learning: yes. Technical Sourcer: no. Technical Recruiting: no. Sourcing experience: no. Strong.
Large Language Models, NLP, Prompt Engineering, LangChain, Data Science
This candidate is an accomplished individual contributor in Data Science with deep ML expertise, but the profile does not indicate any sourcing or recruiting background. Highly experienced Data Scientist with Ph.D. in Atmospheric Sciences → various research roles → Lead/Staff Data Scientist at LevaData/Arkestro/SupportLogic. Excellent.
Senior ML Scientist at Wayfair
Senior ML Scientist background with extensive NLP/AI work, providing high technical acumen for sourcing, but no recruiting job titles. Senior ML Scientist at Wayfair → Research Scientist at Philips, very long tenure in R&D. Strong.
GenAI Engineer | RAG Pipeline Architect | LLM Fine-Tuning & Vector Database Specialist | LangChain • Hugging Face • AWS Bedrock • Azure ML
Very strong Machine Learning focus centered on GenAI, RAG pipelines, and LLM deployment, though not a sourcer role. GenAI Engineer (4.7 yrs) fine-tuning LLMs (LoRA, Hugging Face), building RAG pipelines (Weaviate) on AWS Bedrock/SageMaker → AI/ML Engineer (3 yrs) fine-tuning transformers (PyTorch). Machine Learning: yes. Technical Sourcer: no. Technical Recruiting: no. Sourcing experience: no. Strong.
Gen- AI Engineer | AI, ML, GenAI, NLP | Python, PyTorch, TensorFlow | GCP, AWS, Azure | Predictive & Scalable AI Solutions
This candidate is an experienced Machine Learning Engineer focused on GenAI and NLP, but their background isn't in technical sourcing. AI Engineer (12 yrs) designing, building deployments using ML/Python/LLMs (Azure OpenAI, Gemini) → globalization/localization focus. Machine Learning: yes. Technical Sourcer: no. Technical Recruiting: no. Sourcing experience: no. Strong.
Staff Machine Learning Scientist
This is a highly experienced Staff Machine Learning Scientist focused on research and development, not sourcing or recruiting roles. Former Postdoc at UC Berkeley → Applied Scientist at Amazon Search → Staff ML Scientist at insitro, Ph.D. in Computer Science. Excellent.
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