Overview
On Site
$60+
Contract - Independent
Contract - W2
Skills
Text Embeddings
NLP
TF-IDF
Word2Vec
GloVe
FastText
BERT
Sentence-BERT
OpenAl
Azure Open embeddings
PCA
t-SNE
UMAP
Machine Learning
Statistical Modeling
Job Details
Hi,
Hope you are doing well. We do have the below position.
Position: Sr. Machine Learning Engineer
Location: Any where in US
NO C2C
*Text Embeddings & NLP*
- Design and implement pipelines leveraging text embeddings for semantic search, classification, clustering, and document retrieval.
- Work with embedding techniques such as TF-IDF, Word2Vec, GloVe, FastText, and transformer-based models including BERT, Sentence-BERT, OpenAl, and Azure Open embeddings.
- Apply dimensionality reduction methods (PCA, t-SNE, UMAP) to analyze and visualize embedding spaces.
- Use cosine similarity, Euclidean distance, and approximate nearest neighbor algorithms like FAISS and ScaNN for similarity search and clustering.
- Integrate embedding outputs into downstream applications such as intent detection, topic modeling, semantic deduplication, document ranking, and retrieval systems.
*Traditional Machine Learning & Statistical Modeling*
- Build and deploy predictive models with logistic/linear regression, random forests, gradient boosting techniques (X&Beest, LightGRM), SVM, Naive Bayes, k-means, and hierarchical clustering.
- Employ statistical inference techniques including hypothesis testing, confidence intervals, bootstrapping, Bayesian inference, multicollinearity diagnostics, residual ana and time series forecasting (ARIMA, SARIMA).
- Evaluate model performance using ROC/Precision-Recall curves, AUC, confusion matrices, F1-score, lift/gain charts, and KS statistics.
- Conduct feature selection via Lasso/Ridge regression, recursive feature elimination (RFE), and SHAP values for interpretability.
*Experimentation & Causal Inference*
Q Search
M365
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