The Role
Develop and productionize deep learning models across the full machine learning lifecycle, including neural network design, data preparation, training, tuning, deployment, monitoring, and continuous improvement. Build scalable data pipelines for large datasets, collaborate with Backend, DevOps, and data-labeling teams, and apply security and information-protection practices. The role requires strong Python and machine learning framework expertise, production deployment experience, and familiarity with distributed data technologies.
Summary Generated by Built In
About Deploy
Deploy is an intelligence infrastructure platform that gives developers and enterprises access to open source and proprietary AI models. For leading open source LLMs, Deploy operates an optimized model inference engine that keeps costs and latency low while maintaining high throughout. Deploy also offers a set of proprietary AI models that address challenges in content safety, AI & deepfake detection, sponsorship intelligence, and IP & publicity protection. Trusted by the world's largest and most innovative organizations, Deploy's solutions unlock high-performance AI model inference at production scale.
Deploy has raised more than $200M from leading investors, including Tomales Bay Capital, General Catalyst, 8VC, Glynn Capital, Bain & Company, Visa Ventures, and others. The company has more than 250 employees across offices in San Francisco, Seattle, New York, and Delhi.
Deploy has raised more than $200M from leading investors, including Tomales Bay Capital, General Catalyst, 8VC, Glynn Capital, Bain & Company, Visa Ventures, and others. The company has more than 250 employees across offices in San Francisco, Seattle, New York, and Delhi.
Machine Learning Role
In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at the forefront of deep learning technology, prototyping state-of-the-art neural net models and launching these models into production. We value hard workers who have no qualms working with terabyte-scale datasets, who are interested in learning new technologies at all levels of the machine learning stack, and who move fast and take ownership of their projects. Our ideal candidate has experience creating a working machine learning-powered project from the ground up, contributes innovative ideas and ingenious implementations to the team, and is capable of planning out scalable, maintainable data pipelines.
Responsibilities
- Everything involved in applying a ML model to a production use case, including, designing and coding up the neural network, gathering and refining data, training and tuning the model, deploying it at scale with high throughput and uptime, and analyzing the results in the wild in order to continuously update and improve accuracy and speed
- Interface closely with the Backend and DevOps teams as well as with our internal data labeling services
- Utilize OWASP top 10 techniques to secure code from vulnerabilities
- Maintain awareness of industry best practices for data maintenance handling as it relates to your role
- Adhere to policies, guidelines and procedures pertaining to the protection of information assets
- Report actual or suspected security and/or policy violations/breaches to an appropriate authority
Requirements
- You have an undergraduate or graduate degree in computer science or similar technical field, with significant coursework in mathematics or statistics
- You have 1-2 years industry machine learning experience
- You have successfully trained and deployed a deep learning machine model (image, NLP, video, or audio) into production, with measurably improved performance over baseline, either in industry or as a personal project
- You have strong experience with a high-level machine learning frameworks such as Tensorflow, Caffe, or Torch, and familiarity with the others
- You know the ins and outs of Python, especially as it applies to the above ML frameworks
- You are capable of quickly coding and prototyping data pipelines involving any combination of Python, Node, bash, and linux command-line tools, especially when applied to large datasets consisting of millions of files
- You have a working knowledge of the following technologies, or are not afraid of picking it up on the fly: C++, Scala/Spark, SQL, Cassandra, Docker
- You are up-to-date on the latest deep neural net research and architectures, both in understanding the theory and motivations behind the techniques, as well as how to implement them in the ML framework of your choice
- You have great communication skills and ability to work with others
- You are a strong team player, with a do-whatever-it-takes attitude
Who We Are
We are a group of ambitious individuals who are passionate about creating a revolutionary AI company. At Deploy, you will have a steep learning curve and an opportunity to contribute to one of the fastest growing AI start-ups in San Francisco. The work you do here will have a noticeable and direct impact on the development of the company.
Thank you for your interest in Deploy and we hope to meet you soon!
The current expected base salary for this position ranges from $120,000 - $180,000. Actual compensation may vary depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the total compensation package that is provided to compensate and recognize employees for their work; stock options may be offered in addition to the range provided here.
Skills Required
- Undergraduate or graduate degree in computer science or a similar technical field, with significant mathematics or statistics coursework
- 1-2 years of industry machine learning experience
- Successfully trained and deployed a deep learning model into production with measurable performance improvement over baseline
- Strong experience with a high-level machine learning framework such as TensorFlow, Caffe, or Torch
- Strong Python programming experience, particularly for machine learning frameworks
- Ability to code and prototype data pipelines using Python, Node.js, Bash, and Linux command-line tools for large datasets
- Working knowledge of C++, Scala/Spark, SQL, Cassandra, and Docker
- Current knowledge of deep neural network research, architectures, theory, and implementation
- Strong communication and collaboration skills
- Ability to work effectively as a team player
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The Company
What We Do
Hive is the leading provider of cloud-based enterprise AI solutions to power the next wave of intelligent automation. The company offers a portfolio of best-in-class deep learning models, built with consensus-validated training data sourced and annotated by a distributed workforce of more than 2 million registered contributors. Hive's APIs enable use cases including automated content moderation, contextual advertising, advertising and sponsorship measurement, document parsing, and more. Hive processes billions of API requests per month for many of the world’s largest and most innovative companies. Hive has raised over $120M in capital from leading investors, including General Catalyst, 8VC, Glynn Capital, Jericho Capital, Bain & Company, Visa Ventures, and others. In April 2021, Hive announced a $50M Series D at a $2B valuation. The San Francisco-based company has 175+ full-time employees globally, in addition to a distributed workforce of more than 2 million global contributors that supports data labeling operations. For more information, visit thehive.ai.
