Machine Learning Engineer

Job Description

• Build state of the art langugae models to understand vernacular languages.
• Build and push machine learning models to optimise the results.
• Consume real-time data and build layers around that to leverage customer understanding.
• Our state-of-the-art models are ingesting and generating relevant search results for our customers weekly. Work directly with our current models to tune them to the abundant inflow of data as well as architecting new ones to further infuse AI into search workflows.
• Bee an integral part of Zevi, working on the core tech that makes our product what it is today. It doesn’t stop there though: As we collect more and more insights, get ready to shape the future of search.

Skills and Experience expected:

• Have at least 2 years of experience working with language models, building, fine-tuning, training them.
• Have closely read NLP publications and implemented some of them.
• Have designed and implemented a scalable ML infrastructure that is both secure and modular.
• Have pushed deep learning models in production.
• Have been responsible for breaking down and solving complex problems.
• Have developed engineering principles and designed processes/workflows.
• Have experience working in Python, sklearn, Pytorch, Tensorflow, and are an expert in at least one of those technologies.

What can you expect from Zevi ?

• Closely work with leading enterprise engineering teams.
• Be a part of highly motivated core team.
• Get access and contribute to all strategies being built by Zevi.
• Full ownership of your product line.

Responsibilities

1. Create data funnels to feed into models via web, structured and unstructured data
2. Maintain coding standards using  SDLC, Git, AWS deployments etc
3. Keep abreast of developments in the field
4. Deploy models in production and monitor them
5. Documentations of processes and logic
6. Take ownership of the solution from code to deployment and performance

Job roles and responsibilities:

• Design, develop, test, deploy, maintain and improve ML models/infrastructure and software that uses these models
• Experience writing software in one or more languages such as Python, Scala, R, or similar with strong competencies in data structures, algorithms, and software design
• Experience working with recommendation engines, data pipelines, or distributed machine learning
• Experience working with deep learning frameworks (such as TensorFlow, Keras, Torch, Caffe, Theano)
• Knowledge of data analytics concepts, including bigdata, data warehouse technical architectures, ETL and reporting/analytic tools and environments
• Participate in cutting edge research in artificial intelligence and machine learning applications
• Contribute to engineering efforts from planning and organization to execution and delivery to solve complex, real world engineering problems
• Working knowledge on different Algorithms and Machine Learning techniques like, Linear & Logistic Regression analysis, Segmentation, Decisions trees, Cluster analysis and factor analysis, Time Series Analysis, K-Nearest Neighbour, K-Means algorithm, Random Forests Algorithm, NLP (Natural language processing), Sentimental analysis, various Artificial Neural Networks, Convolution Neural Nets (CNN), Bidirectional Recurrent Neural Networks (BRNN)
• Demonstrated excellent communication, presentation, and problem-solving skills

Technical Skills Required:

• GCP Native AI/ML services like Vision, NLP, Document AI, Dialogflow, CCAI, BQ etc.,
• Proficiency with a deep learning framework such as TensorFlow or Keras, etc.,
• Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas, jupyter notebook
• Expertise in visualizing and manipulating big datasets
• Ability to select hardware to run an ML model with the required latency
• Good to have MLOps and Kubeflow knowledge
• GCP ML Engineer Certification

• Passionate about search & AI technologies. Open to collaborating with colleagues & external contributors.
• Good understanding of the mainstream deep learning models from multiple domains: computer vision, NLP, reinforcement learning, model optimization, etc.
• Hands-on experience on deep learning frameworks, e.g. Tensorflow, Pytorch, MXNet, BERT. Able to implement the latest DL model using existing API, open-source libraries in a short time.
• Hands-on experience with the Cloud-Native techniques. Good understanding of web services and modern software technologies.
• Maintained/contributed machine learning projects, familiar with the agile software development process, CICD workflow, ticket management, code-review, version control, etc.
• Skilled in the following programming languages: Python 3.
• Good English skills especially for writing and reading documentation

Data Scientist - Product Development

Employment Type: Full Time, Permanent

Experience: 3-5 Years as a Full Time Data Scientist

Job Description:

We are looking for an exceptional Data Scientist who is passionate about data and motivated to build large scale machine learning solutions to shine our data products. This person will be contributing to the analytics of data for insight discovery and development of machine learning pipeline to support modeling of terabytes (TB) of daily data for various use cases.

Location: Pune (Currently remote up till pandemic, later you need to relocate)

About the Organization: A funded product development company, headquarter in Singapore and offices in Australia, United States, Germany, United Kingdom and India. You will gain work experience in a global environment. Qualifications:

Candidate Profile:

• 3+ years relevant working experience
• Master / Bachelor’s in computer science or engineering
• Working knowledge of Python, Spark / Pyspark, SQL
• Experience working with large-scale data
• Experience in data manipulation, analytics, visualization, model building, model deployment
• Proficiency of various ML algorithms for supervised and unsupervised learning
• Experience working in Agile/Lean model
• Exposure to building large-scale ML models using one or more of modern tools and libraries such as AWS Sagemaker, Spark ML-Lib, Tensorflow, PyTorch, Keras, GCP ML Stack
• Exposure to MLOps tools such as MLflow, Airflow
• Exposure to modern Big Data tech such as Cassandra/Scylla, Snowflake, Kafka, Ceph, Hadoop
• Exposure to IAAS platforms such as AWS, GCP, Azure
• Experience with Java and Golang is a plus
• Experience with BI toolkit such as Superset, Tableau, Quicksight, etc is a plus

****** Looking for someone who can join immediately / within a month and carries experience with product development companies and dealt with streaming data. Experience working in a product development team is desirable. AWS experience is a must. Strong experience in Python and its related library is required.

You will be responsible for translating the vision for the product to the development team and play a pivotal role throughout the entire development lifecycle. 

What You’ll Be Doing

• A huge part of the day-to-day job is developing end-to-end Machine Learning and Deep Learning solutions for end customers. You will solve customer problems by creating solutions using the newest technology in Data Analytics & Machine Learning.
• Document and teach others what you know and have learned through customer engagements. This can vary from building hands-on training, to writing papers, developer blogs, and teaching.
• Above all, you will be the person that helps bring SmartCow technology to life in the Enterprise. You’ll get to be the face and brains of SmartCow that our customers will rely on.
  
  

What We Need To See

• 4+ years of experience.
• Strong foundational expertise from a BS or MS degree in Engineering, Mathematics, Physics, Computer Science, Data Science, or equivalent work experience.
• Ideal candidates will have an established track record in Deep Learning and Machine Learning; experience with GPUs as well as expertise in using deep learning frameworks such as TensorFlow or PyTorch.
• Strong analytical and problem-solving skills.
• Strong coding development and debugging skills. Including experience with Python, C/C++, Bash, as well as Cloud services, Spark and Linux.
• Experience working with DevOps including but not limited to Docker/Containers, Kubernetes and Data Centre deployments.
• Understanding of dense data center design including computing, storage, and networking.
• Ability to multitask effectively in a dynamic environment.
• Clear written and oral communications skills with the ability to effectively collaborate with executives and engineering teams.
• Successful candidates will be able to demonstrate a strong desire to share knowledge with clients, partners and co-workers.
  
  

Ways To Stand Out From The Crowd

• Demonstrate expertise in one or more of these areas: Data Analytics, Machine Learning, Computer Vision.
• Show a willingness and ability to dig into unfamiliar territories to tackle complex problems through examples in previous work.

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.