AI/ML Job: Lead Machine Learning Engineer

Lead Machine Learning Engineer at Spectrum Labs

Remote › 🇺🇸🇨🇦 100% remote position (in US or Canada)   (Posted Jul 3 2022)

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Job description
Are you well-versed in and excited by the unique challenges of applying NLP and NLU to content moderation? Do you regularly leverage deep learning frameworks such as Tensorflow, PyTorch, and ONNX to build and maintain reliable, fast, secure, and scalable software systems in object-oriented programming languages such as Python and Scala? If so, Lead MLE at Spectrum Labs might be the role for you.

At Spectrum Labs, we’re working hard to make the Internet a safer place for communities to grow. While technology has brought us closer than ever, it has also opened up new ways for people to harass, bully, threaten, scam, and overall hurt each other. With our products, we empower our clients to recognize these behaviors on their own platforms according to their own community guidelines. Using the signal from our solutions, platforms can more effectively enforce those guidelines and promote a healthier environment for their users to become a community.

The Lead MLE plays a central role in experimenting and productionizing models, working between the engineering and data science teams to integrate solutions across data and model management tasks. As a member of our team, you'll develop large portions of our stream processing and model training technologies while improving response times and saving bandwidth at inference. You’ll expand your capabilities in software design and code development, and work in an agile and collaborative environment while making the internet a safer and better understood environment.

Key Responsibilities

Design and implement highly scalable machine learning applications processing large volumes of data.

Implement distributed cloud GPU training for deep learning models.

Collaborate with researchers to productionize real-time, scalable deep learning models, optimizing trade offs between performance and scale.

Benchmark performance, power, latency, bandwidth on CPU/GPU.

Containerize production code using Docker and Kubernetes.

Develop robust and extensible libraries for preprocessing text and audio data for downstream applications.

Efficiently process streams of text and audio data in real-time.

Implement API integrations with external systems.

Write tests to ensure robustness and reliability of pipelines and processes.

Work collaboratively alongside world-class ML/AI professionals and researchers.

Job Benefits

Competitive salary and equity.

Remote-first anywhere within North America.

Comprehensive medical, dental, vision, and other benefits.

Personal hardware choices.

Challenging and rewarding experiences.

Team Culture

As a remote-first start up, we work hard to balance the benefits of remote work with the cultivation of a cohesive team culture. We maintain daily communications with Slack, stand ups, and team meetings, as well as quarterly (optionally in-person) gatherings for departments, teams, or the whole organization.

Job Qualifications

We expect all candidates to demonstrate:

Advanced degree in computer science, physics, statistics, or a related discipline.

Minimum 3-years experience building scalable machine learning systems.

Minimum 3-years experience in Deep Learning framework's Tensorflow and PyTorch

Experience training distributed deep learning models on GPUs.

Experience in deploying production-level inference systems.

Proficiency in Python and PySpark.

Solid understanding of data structures and algorithms.

Preferred Qualifications

We are most interested in candidates who demonstrate:

Experience with efficient batch and streaming workflows using Argo, Spark, Kafka Streams, etc.

Experience with distributed computing frameworks (Celery, PySpark, Hadoop,…).

Experience with AWS and GCP pipelines.

Interview process

After a 30 minute initial screen, we will organize a 1 hour group screen with 4 team members, covering a range of questions related to key job responsibilities and qualifications. We will work with you to determine the most effective evaluation of your coding abilities, whether it is walking through a repository you’ve created or an ML package you work with regularly. Following a chat with the hiring VP, we may schedule some additional informal chats if there are any remaining questions on either side.

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