Techstars

Senior Machine Learning Engineer

Techstars$120K — $160K *
Healthcare
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science, Statistics, Mathematics, or related field; advanced degrees preferred.
  • 5+ years in creating ML pipelines with MLFlow, Kubeflow on GCP.
  • Strong grasp of machine learning frameworks, data structures, and software architecture.
  • Proficient in Python, Java, and Scala programming languages.
  • Hands-on experience with TensorFlow, PyTorch, and other ML tools and platforms.
  • Familiarity with cloud services such as GCP, AWS, Azure.
  • Strong problem-solving and teamwork orientation.

Responsibilities

  • Engineer and deploy machine learning models into production environments.
  • Build and manage efficient ML training and inference pipelines using MLFlow and Kubeflow.
  • Collaborate with data scientists to effectively scale algorithms.
  • Ensure high performance and quality of deployed models.
  • Work in cross-functional teams to ideate and roll out new features.
  • Research and integrate emerging technologies to enhance development efficiency.

Benefits

  • Opportunity to work on cutting-edge AI tools and frameworks.
  • Collaborative environment with strong emphasis on cross-functional teamwork.
  • Focus on continuous learning and technology integration.
  • Exposure to diverse data projects and solutions.
Full Job Description
Senior Machine Learning Engineer

Role Overview:

In the role of Senior Machine Learning Engineer, you will play a pivotal role in our data team, tasked with the design, construction, and maintenance of ML pipelines and services. Your main responsibilities will include the development and refinement of ML training and inference pipelines, ensuring their efficiency, reliability, and accessibility to boost AI practitioner productivity and reduce cycle times. Collaborating closely with product engineers, data scientists, analysts, and other key stakeholders, you will support data-driven decision-making and enhance business intelligence.

Key Responsibilities:

- Engineer, implement, and deploy machine learning models into live environments.

- Construct and maintain robust ML training and inference pipelines utilizing MLFlow and Kubeflow.

- Collaborate with the data science team to scale their algorithms effectively.

- Guarantee the performance, quality, and responsiveness of deployed models.

- Work alongside cross-functional teams to conceptualize, design, and launch new functionalities.

- Proactively explore, assess, and integrate new technologies to enhance development efficiency.

Qualifications:

- A Bachelors degree in Computer Science, Statistics, Mathematics, or a related field, with a preference for advanced degrees.

- At least 5 years of experience in crafting ML training and inference pipelines using MLFlow, Kubeflow on GCP.

- Profound knowledge of machine learning frameworks, libraries, data structures, data modeling, and software architecture.

- Proficiency in programming languages such as Python, Java, Scala.

- Practical experience with machine learning algorithms, processes, tools, and platforms, including Python, TensorFlow, PyTorch, etc.

- Familiarity with cloud services (GCP, AWS, Azure).

- Exceptional problem-solving abilities and teamwork skills.

Additional Skills:

- Implementation of data quality checks and governance processes to ensure data integrity and compliance with privacy and security regulations.

- Optimization of pipelines and queries for enhanced performance and scalability.

- Effective collaboration with cross-functional teams to meet data requirements.

- Continuous learning and evaluation of new data engineering technologies and tools.

Desirable Skills:

- Experience with data streaming technologies like Apache Kafka.

- Knowledge in ELT and ETL processes for feature extraction.

- Proficiency in data visualization tools such as Tableau, Power BI.

- Certifications in relevant data engineering technologies.

This role is crucial for developing our data infrastructure and ensuring the availability and reliability of data for analysis. Your expertise in data engineering will be instrumental in driving our organization's data-driven strategies and overall success.

About Techstars

Techstars is a global startup accelerator that provides mentorship, funding, and resources to early-stage companies. Founded in 2006, Techstars has helped launch over 2,000 companies across various industries. The company has a strong focus on technology and innovation and has partnerships with major corporations such as Amazon, Barclays, and Comcast. Techstars operates in over 150 countries and has offices in various locations around the world. The company is committed to diversity and inclusion and has implemented various initiatives to support underrepresented founders.
Learn more about Techstars
Size
200 employees
Industry
Founded
2006

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