About Turing:
Turing enables U.S. companies to hire the world’s best remote software engineers. 100+ companies including those backed by Sequoia, Andreessen, Google Ventures, Benchmark, Founders Fund, Kleiner, Lightspeed, and Bessemer have hired Turing engineers. For more than 180,000 engineers across 140 countries, we are the preferred platform for finding remote U.S. software engineering roles. We offer a wide range of full-time remote opportunities for full-stack, backend, frontend, DevOps, mobile, and AI/ML engineers.
We are growing fast (our revenue 15x’d in the past 12 months and is accelerating), and we have raised $14M in seed funding (one of the largest in Silicon Valley) from:
- Facebook’s 1st CTO and Quora’s Co-Founder (Adam D’Angelo)
- Executives from Google, Facebook, Square, Amazon, and Twitter
- Foundation Capital (investors in Uber, Netflix, Chegg, Lending Club, etc.)
- Cyan Banister
- Founder of Upwork (Beerud Sheth)
We also raised a much larger round of funding in October 2020 that we will be publicly announcing over the coming month.
Some articles about Turing:
- TechCrunch: Turing raises $14M seed to help source, vet, place, and manage remote developers
- The Information: Six Startups Prospering During Coronavirus
- Cyan Banister: Turing Helps the World Level Up
- Jonathan Siddharth (Turing CEO): The Future of Work is Remote.
Turing is led by successful repeat founders Jonathan Siddharth and Vijay Krishnan, whose last A.I. company leveraged elite remote talent and had a successful acquisition. (Techcrunch story). Turing’s leadership team is composed of ex-Engineering and Sales leadership from Facebook, Google, Uber, and Capgemini.
About the role:
Software developers from all over the world have taken 200,000+ tests and interviews on Turing. Turing has also recommended thousands of developers to its customers and got customer feedback in terms of customer interview pass/fail data and data from the success of the collaboration with a U.S customer. This generates a massive proprietary dataset with a rich feature set comprising resume and test/interview features and labels in the form of actual customer feedback. Continuing rapid growth in our business creates an ever-increasing data advantage for us.
We are looking for a Machine Learning Scientist who can help solve a whole range of exciting and valuable machine learning problems at Turing. Turing collects a lot of valuable heterogeneous signals about software developers including their resume, GitHub profile and associated code and a lot of fine-grained signals from Turing’s own screening tests and interviews (that span various areas including Computer Science fundamentals, project ownership and collaboration, communication skills, proactivity and tech stack skills), their history of successful collaboration with different companies on Turing, etc.
A machine learning scientist at Turing will help create deep developer profiles that are a good representation of a developer’s strengths and weaknesses as it relates to their probability of getting successfully matched to one of Turing’s partner companies and having a fruitful long-term collaboration. The ML scientist will build models that are able to rank developers for different jobs based on their probability of success at the job.
You will also help make Turing’s tests more efficient by assessing their ability to predict the probability of a successful match of a developer with at least one company. The prior probability of a registered developer getting matched with a customer is about 1%. We want our tests to adaptively reduce perplexity as steeply as possible and move this probability estimate rapidly toward either 0% or 100%; maximize expected information-gain per unit time in other words.
As an ML Scientist on the team, you will have a unique opportunity to make an impact by advancing ML models and systems, as well as uncovering new opportunities to apply machine learning concepts to Turing product(s).
This role will directly report to Turing’s founder and CTO, Vijay Krishnan. This is his Google Scholar profile.
Responsibilities:
- Enhance our existing machine learning systems using your core coding skills and ML knowledge.
- Take end to end ownership of machine learning systems - from data pipelines, feature engineering, candidate extraction, model training, as well as integration into our production systems.
- Utilize state-of-the-art ML modeling techniques to predict user interactions and the direct impact on the company’s top-line metrics.
- Design features and builds large scale recommendation systems to improve targeting and engagement.
- Identify new opportunities to apply machine learning to different parts of our product(s) to drive value for our customers.
Minimum Requirements:
- BS, MS, or Ph.D. in Computer Science or a relevant technical field (AI/ML preferred).
- Extensive experience building scalable machine learning systems and data-driven products working with cross-functional teams
- Expertise in machine learning fundamentals, applicable to search - Learning to Rank, Deep Learning, Tree-Based Models, Recommendation Systems, Relevance and Data mining, understanding of NLP approaches like W2V or Bert.
- 2+ years of experience applying machine learning methods in settings like recommender systems, search, user modeling, graph representation learning, natural language processing.
- Strong understanding of neural network/deep learning, feature engineering, feature selection, optimization algorithms. Proven ability to dig deep into practical problems and choose the right ML method to solve them.
- Strong programming skills in Python and fluency in data manipulation (SQL, Spark, Pandas) and machine learning (scikit-learn, XGBoost, Keras/Tensorflow) tools.
- Good understanding of mathematical foundations of machine learning algorithms.
- Ability to be available for meetings and communication during Turing's "coordination hours" (Mon - Fri: 8 am to 12 pm PST).
Other Nice-to-have Requirements:
- First author publications in ICML, ICLR, NeurIPS, KDD, SIGIR, and related conferences/journals.
- Strong performance in Kaggle competitions.
- 5+ years of industry experience or a Ph.D. with 3+ years of industry experience in applied machine learning in similar problems e.g. ranking, recommendation, ads, etc.
- Strong communication skills.
- Experienced in leading large-scale multi-engineering projects.
- Flexible, and a positive team player with outstanding interpersonal skills.
About Turing
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