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Senior Machine Learning Engineer
4 weeks ago
Position Overview :
We are seeking a highly skilled Senior Machine Learning Engineer to join our team and develop impactful, data-driven solutions that drive real-world value. In this role, you will design, build, and optimize machine learning models and scalable data pipelines, turning complex data into actionable insights. Collaborating closely with cross-functional teams, you will play a critical role in empowering research scientists and stakeholders during key milestones in the study design process.
This is a unique opportunity to work on cutting-edge ML solutions in a fast-paced, collaborative environment that values innovation, growth, and technical excellence.
Your expertise will help shape the future of our platform and make a tangible impact on the industry.
Key Responsibilities :
Platform and Tool Development :
- Design and build scalable platforms and tools using advanced machine learning techniques, including data parsing, chunking, and information extraction.
- Apply Bayesian programming methods to solve complex problems and enhance system capabilities.
LLM Integration & Optimization :
- Implement and productionalize large language models (LLMs) using modern frameworks such as LangChain, optimizing for performance and minimizing issues like hallucinations.
Problem Identification & Solution Architecture :
- Partner with stakeholders to identify impactful challenges and architect innovative ML solutions that align with both business priorities and technical feasibility.
Data Analysis & Feature Engineering :
- Explore and analyze datasets to identify actionable insights, enabling the development of ML systems and new product features.
- Engineer effective features to maximize model accuracy and applicability in production.
Model Development & Evaluation :
- Build, train, and fine-tune machine learning models to meet key performance objectives.
- Evaluate models rigorously using relevant metrics to ensure they are ready for real-world deployment.
Scalable Code & API Development :
- Write clean, production-ready code for ML pipelines, ensuring modularity, scalability, and maintainability.
- Collaborate with engineering teams to integrate ML models seamlessly with existing services through robust APIs.
Deployment & Monitoring :
- Deploy models in production environments, leveraging best practices for scalability and reliability.
- Implement proactive monitoring systems to detect data drift, model degradation, and other performance issues, ensuring long-term stability.
Stakeholder Communication & Alignment :
- Clearly communicate ML insights, limitations, and implications to non-technical stakeholders, fostering alignment with organizational goals.
- Act as a trusted advisor, bridging the gap between technical capabilities and business needs.
Qualifications :
Education :
- Bachelor's or Master's degree in Computer Science, Statistics, Data Science, or a related technical field. Advanced degrees are a plus.
Experience :
- 6+ years of hands-on experience as a Machine Learning Engineer or Data Scientist, with a proven track record of delivering impactful ML solutions in production environments.
Technical Expertise :
- Machine Learning Fundamentals : Strong grasp of ML techniques and frameworks, including training/validation workflows, supervised/unsupervised learning, feature engineering, and optimization strategies.
- Programming & Libraries : Advanced proficiency in Python and hands-on experience with ML and data science libraries (e.g., Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers, spaCy).
- Data Management & EDA : Expertise in wrangling, cleaning, and transforming raw data into structured formats optimized for machine learning applications.
- Model Evaluation : Deep understanding of performance metrics (e.g., F1-Score, Precision, Recall) and the ability to contextualize and communicate model results effectively to both technical and non-technical stakeholders.
- Production ML : Demonstrated experience in deploying and productionizing ML models, designing pipelines, and implementing monitoring systems to maintain long-term model performance and stability.
Cloud & Infrastructure :
- Familiarity with cloud platforms such as AWS, GCP, or Azure, and experience leveraging these environments for ML workflows.
- Exposure to containerization tools like Docker and orchestration platforms like Kubernetes is a strong plus.
Mindset & Problem-Solving :
- A creative and analytical thinker, capable of tackling complex technical challenges with innovative, scalable solutions.
- Passionate about learning and integrating new tools, technologies, and methodologies to stay at the forefront of ML advancements.
Communication & Collaboration :
- Strong interpersonal skills with the ability to distill technical complexity into accessible insights for diverse audiences.
- A collaborative mindset, with experience working cross-functionally with engineering, product, and business teams.
What We Offer :
- Competitive salary and benefits package
- A collaborative and dynamic work environment
- Professional growth and development opportunities
