Machine Learning Engineer

We’re not like others, and neither are you. Unify Consulting is a collective of genuine, curious, seasoned consultants who unlock potential and deliver with purpose and excellence. We unify daring leaders to better the world and are always seeking co-creators, community-builders and truth-tellers who strive to multiply our positive impact.  

 

We are currently seeking experienced Machine Learning Engineers to join our growing Data Science & Machine Learning Engineering practice in Seattle, San Francisco, Silicon Valley, Dallas, Chicago, Sioux Falls, Fargo, and Salt Lake City. In this role, you will collaborate on the entire lifecycle of infrastructure and custom machine learning solutions delivery. As a senior member of the practice, your experience and expertise will provide mentorship to other consultants and advisement across projects.

 

Ideal Candidate: You like to stay on the forefront of machine learning, data science and software engineering through novel project execution and development of machine learning pipelines for the deployment of algorithms that improve performance and effectiveness across business domains. This role requires a broad knowledge of Software Development Life Cycle, Machine Learning Life Cycle and DevOps processes. 

Responsibilities

• Use your experience and communication skills to work across business & technology teams to productionize and deploy innovative data science models and algorithms
• Collaborate with data science and engineering teams to deliver end-to-end machine learning solutions for our clients
• Collaborate with platform teams and solution architects to evolve big data platforms and evaluate various data science technologies and services
• Be a champion for an MLOps Center of Excellence and continuously refine Standard Operating Procedures
• Clearly articulate highly technical methods and results to diverse audiences and partners to drive decision-making

You will be creating and delivering production-grade, machine learning solutions, built with automated, repeatable processes. This includes:

• Designing end-to-end machine learning pipelines for building, training, tuning, deploying, and performing model inference
• Designing terraform processes to create infrastructure and containers for training, serving, monitoring, and observing production ML
• Standardizing repeatable processes for code, data, and pipeline versioning, experiment tracking and model registration
• Designing strategies for continuous, adaptive and reinforcement learning
• Integrating ML into business applications and build adaptive systems
• Building automated orchestrations for the entire MLOps lifecycle
• Designing, implementing and maintaining MLOps tools, processes, and platforms

Required Education

• Bachelor’s degree in Computer Science or related STEM program with relevant professional work experience
• Additional higher education degree such as a Master’s degree in Data Science is preferred

Required Experience

• You have a proven record of building end-to-end machine learning pipelines and big data solutions using CI/CD, Continuous Training (CT) and MLOps
• You have prior experience as a software engineer writing production code and working in the software development life cycle
• Experience collaborating with cross-functional teams including Data Engineers, DevOps Engineers, and Data Scientists to deliver end-to-end solutions
• Experience building operational pipelines and orchestrations
• Experience building model management and monitoring solutions
• Experience implementing model retraining and replacement strategies including A/B Testing, Challenger/Champion or Multi-Arm Bandits
• Experience implementing feedback loop solutions that include continuous learning, adaptive learning, and reinforcement learning
• Deep technical proficiency in Python and SQL working in cloud technology stacks (Azure, AWS, GCP)
• Proficiency in PySpark, SparkSQL, Delta Lake and MLFlow
• Experience with common data science toolkits and model development
• Extensive knowledge of ML frameworks, libraries, data structures, data modeling, feature engineering, and software architecture patterns
• Strong working knowledge of machine learning techniques, including linear and logarithmic regression, decision trees, probability networks, association rules, clustering, neural networks, and Bayesian models.
• Experience with big data platforms (Hadoop, Spark, Hive), orchestration frameworks (Airflow), Infrastructure technologies (Terraform, Ansible, Kubeflow, Kubernetes, Docker), data science environments and libraries (Jupyter, Anaconda, Databricks, Sagemaker, Azure ML, Vertex AI, Sklearn, MLLib, NumPy) and CI/CD build and version management tools (Jenkins, Git, DVC)
• Working knowledge of high velocity high volume streaming data frameworks (Kafka, Event Hubs, Stream Analytics), data transformation technologies (dbt), and deep learning libraries, such as Tensorflow and PyTorch, applications with NLP and computer vision, and geospatial processing

$150,000 - $195,000 a year