Machine Learning Software Engineer

Bostondynamicsaiinstitute in Cambridge, MA

Our Mission
Our mission is to solve the most important and fundamental challenges in AI and Robotics to enable future generations of intelligent machines that will help us all live better lives.

Software Engineers will work cross-functionally, creating new technology to improve software development for robots. If you have a passion for developing technology for robots and use it to advance their capabilities and usefulness, you will want to join us! We are onsite in our new Cambridge, MA office where we are building a collaborative and exciting new organization.
We provide equal employment opportunities to all employees and applicants for employment and prohibit discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
    • Train, deploy and maintain various ML algorithms on cloud and on-premise infrastructure
    • Develop process, pipeline and tools for all components of ML lifecycle (training, evaluation, deployment)
    • Build and maintain data, model and experimentation pipelinesHelp with model tuning, algorithm selection, hyperparameter search using our MLops platform
    • Partner closely with research and applied science teams to put models into production
    • Promote quality and reliability through regular code reviews
    • 5+ years experience delivering robust code
    • BS or MS in computer science, engineering, data science, or related technical, math, or scientific field
    • Experience with C++ or Python
    • Experience with deep-learning techniques in NLP and Computer Vision
    • Experience using data science tools, libraries, and frameworks (e.g. Scikit-learn, caret, mlr, mllib, SparkML, NumPy, SciPy, Pandas, TensorFlow, PyTorch, MXNet)
    • Experience with git, issue tracking, CI/CD, and modern software engineering practices
    • Understanding of machine learning algorithms such as Linear and Logistic regressions, Decision tree, Naive Bayes, KNN, K-means, Random forest
    • Experience with Docker, Kubernetes, cloud computing, or similar applications
    • Experience with data processing, logging, and visualization tools
    • Experience with MLOps (model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning)Experience creating orchestration workflows with tools such as Airflow, Kubeflow, or AWS Step Functions
    • DevOps experience (e.g. CI/CD Pipelines, Infrastructure as Code, containers, Agile software development)
    • Experience implementing IoT solutions such as edge computing
    • Big Data batch and real time data processing experience (e.g. Hadoop, Spark , Presto, Kafka, Kinesis, Flink)
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