Lead Machine Learning Engineer

Who We Are

We are a small deep-tech startup with specialized capacities in behavior change solutions, offering a proven combination of behavioral psychology, data analytics, and digital communications. Our company focuses effectively on creating science-based solutions that tend to embrace social purpose and enhance the quality of people’s lives by creating a variety of products that use cutting-edge machine learning and data science methods to model, segment, and create the products.

We are seeking a Lead Machine Learning Engineer interested in solving the most challenging problems leveraging Machine Learning, especially Natural Language Processing (NLP) and Natural Language Understanding (NLU), and information retrieval including topical classification, sentiment analysis, and behaviors (user intent detection). Looking for people curious and love problem-solving. You have to like building end-to-end products that have a focus on ethics and reliability.

We are looking for Senior Machine Learning Engineers who assist the Data Science team in analyzing large amounts of data to find patterns and solutions that will provide insights used for solving complex problems. Work will require working in teams to solve large, complex problems to achieve interrelated and interdependent objectives. The Senior Machine Learning Engineer must be self-directed and comfortable supporting the requirements of the machine learning platform.

Responsibilities

Implementing and operating machine learning algorithmsRunning AI systems experiments and testsDesigning and developing machine learning systemsPerforming statistical analyses

Data Collection And Preprocessing

Gather, clean, and preprocess large datasets to make them suitable for machine learning tasks.Collaborate with data engineers and data scientists to ensure data quality and availability.Model Development:Design, build, and train machine learning models using state-of-the-art techniques and frameworks.Experiment with different algorithms and architectures to achieve optimal results.

Feature Engineering

Create and select relevant features from data to improve model performance.Utilize domain knowledge to engineer features that capture important patterns.Model Deployment:Deploy machine learning models into production environments.Work closely with DevOps teams to ensure scalability, reliability, and security.

Monitoring And Optimization

Implement monitoring solutions to track model performance in real-time.Continuously fine-tune and optimize models to maintain or improve accuracy.Collaboration and Communication:Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts.Clearly communicate technical findings and insights to both technical and non-technical stakeholders.

Research And Innovation

Stay up-to-date with the latest developments in machine learning and artificial intelligence.Experiment with emerging technologies and techniques to drive innovation within the organization.

Qualifications

The ideal candidate will have a background in Python, have experience working with large data sets, annotating and formatting data for ML, and have experience in building Machine Learning Platforms, applying Machine Learning, and deploying data-driven solutionsMaster's or PhD degree in Computer Science, Machine Learning, Data Science, or a related field (Ph.D. preferred).Proven experience (7+ years) as a Machine Learning Engineer or a similar role.Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch) and programming languages like Python, Java, Golang, and Scala. Strong knowledge of machine learning algorithms, deep learning, and statistical modeling.Experience with data pre-processing, feature engineering, and model deployment.Demonstrated strength in data modeling, ETL development, and data warehousingExperience using big data technologies (PostgresDB, Airflow, Kubernetes, Docker, Spark, Data Lakes, TensorFlow)Experience delivering end-to-end projects independently.Experience using business intelligence reporting tools (SuperSet, Power BI, Tableau, etc.).Knowledge of data management fundamentals and data storage principles.Experience with data pipelines and stream-processing systems Knowledge of distributed systems as it pertains to data storage and computing.Proven success in communicating with end-users, technical teams, and senior management to collect requirements, and describe data modeling decisions and data engineering strategy.Knowledge of software engineering best practices across the development life-cycle, including agile methodologies, coding standards, code reviews, version control, build processes, testing, and observability.

Salary: $200k - $250/yr plus bonus and equity.