Machine Learning Jobs (US) - May-2024

About The Role:

As a Staff ML Engineer at Jasper your scope of responsibility may include hands-on contribution, architecture and designing highly-robust systems. This is a highly-visible role in Jasper with interaction across all levels of the leadership team (Head of Data & AI and Principal & Staff ML leaders). You will help build the state of the art ML infrastructure that performs automated data retrieval, feature extraction, model training and deployment at scale. You will evaluate the latest ML technologies and come up with proposals to evolve our ML infrastructure. You will work the other ML engineers to produce high quality code through rigorous unit tests, code reviews, and documentation. You will execute on projects that span across multiple areas of the product.

Our target market is composed of content creators and marketing leaders at growing companies. We help companies leverage artificial intelligence to accelerate content production and unlock their best ideas. We see AI as a partner to content marketers that can help them break through writer's block, create fine-tuned imagery, and repackage content for different formats, languages and tones.

The role is open to candidates located in the US and can be remote within the United States. We have offices in Austin, TX and Salt Lake City, UT with employees based across the US and Australia. We've built a strong foundation to support remote employee engagement with other teams. We have a mix of experience levels and backgrounds, and we're building an environment that celebrates knowledge sharing, mentoring and career growth.

What you will do at Jasper:

• Design and build the state of the art ML pipeline to retrieve/preprocess data, extract features, train and deploy models at scale.
• Design and develop CI/CD pipeline to train and deploy models to staging and production environments.
• Design and build the monitoring platform for our models to monitor, evaluate, and retrain if a deviation from stated accuracy goal is observed.
• Drive the technical roadmap and evolve our ML infrastructure.
• Own and lead the development of various modules in the ML pipeline.

What you will bring to Jasper:

• 7+ years of experience developing mission critical applications using Java or Python or similar object-oriented languages.
• 2+ years of experience building ML and Data pipeline architectures.
• 2+ years of background in AI, NLP, and Machine Learning.
• Strong knowledge of ML frameworks such as TensorFlow, PyTorch, Keras.
• Strong knowledge of SQL and NoSQL databases.
• Experience delivering successful software projects from concept to production.
• Strong experience in data structures and algorithms.

Who Should Apply:

• Recent Computer science/Engineering /Mathematics/Statistics or Science Graduates or anyone looking to make their career in IT Industry
• We also assist in filing for STEM extension and H1b and Green card filing.
• Candidates who are serious about their future in the IT Industry and have set big goals for themselves.
• Candidates having difficulty in finding jobs or cracking interviews or who wants to improve their skill portfolio. We also offer Skill Enhancement Programs if the candidates are missing skills or experience which our clients need with great outcomes
• Candidates can benefit from skill enhancement if they fall into the below categories. If they are qualified with enough skills then no need for skill enhancement
• Candidates Who Lack Experience
• Have had a break in careers
• Lack Technical Competency
• Candidates who want to get employed and make a career in the Tech Industry

If the skills are not a match candidates can opt for Skill enhancement. Or their resume can be sent out to clients to see if responses are achievable

REQUIRED SKILLS For Java/Software Programmers:

• Bachelors degree or Masters degree in Computer Science, Computer Engineering, Electrical Engineering, Information Systems, IT
• Highly motivated, self-learner, and technically inquisitive
• Experience in programming language Java and understanding of the software development life cycle
• Knowledge of Core Java , javascript , C++ or software programming
• Spring boot, Microservices and REST API's experience
• Excellent written and verbal communication skills
• For data Science/Machine learning

Required Skills:

• Bachelors degree or Masters degree in Computer Science, Computer Engineering, Electrical Engineering, Information Systems, IT
• Highly motivated, self-learner, and technically inquisitive
• Experience in programming language Java and understanding of the software development life cycle
• Knowledge of Statistics, Python, data visualization tools
• Excellent written and verbal communication skills

Preferred skills: NLP, Text mining, Tableau, Time series analysis

Responsibilities & Duties:

• Develop, implement, and deploy machine learning models in production environments
• Work with cross-functional teams to define and prioritize ML use cases based on business requirements
• Collaborate with data engineers, data scientists, and software engineers to build scalable and efficient ML pipelines
• Implement ML workflows and monitoring solutions using MLflow or other similar life-cycle model management tools
• Continuously improve and optimize the performance of ML models in production
• Document and communicate ML processes, models, and results to stakeholders

Minimum Qualifications & Skills:

• 3+ years of experience in machine learning engineering with a focus on production deployment
• Strong programming skills and experience in Python, Rust, or R
• Solid understanding of machine learning algorithms, such as supervised and unsupervised learning, reinforcement learning, and deep learning, as well as experience with popular machine learning libraries such as TensorFlow, PyTorch, or scikit-learn
• Hands-on experience with ML workflow management tools such as MLflow
• Experience with building, testing, and deploying ML models in production
• Solid understanding of data structures, algorithms, and statistics
• Strong knowledge of software engineering best practices and version control systems such as Git

Preferred Qualifications & Skills:

• Experience with distributed computing technologies such as Apache Spark or Dask
• Familiarity with containerization technologies such as Docker and Kubernetes
• Knowledge of cloud platforms such as AWS, GCP, or Azure
• Experience with data engineering, data modeling, and data architecture
• Strong communication and collaboration skills
• Utilize infrastructure tooling, such as Terraform or Helm, to manage and automate the deployment, scaling, and monitoring of machine learning models in production environments

Job Description:

Reporting to the Senior Engineering Manager, Relationship Intelligence and Machine Learning, you'll help create the magic that underlies Affinity's industry-leading relationship intelligence. You'll define how Affinity uses our massive datasets (including email messages, calendar events, and data pulled from around the public internet) to extract actionable information and insights for our users.

This is an applied machine learning position and not a research role. The expectation is that the candidate will be a strong contributor to the development of our ML Ops and ML Engineering capabilities, as well as doing modeling work. We value short iterations that deliver value to our customers.

What you'll be doing:

• Take projects from concept to production, including feature engineering, model construction, deployment, and model observability and evaluation.
• Help define our data roadmap. Which problems are amenable to AI/ML techniques, and which are better solved via simpler non-statistical methods? How might we define and evaluate data quality? Which models and techniques are best suited to our domain? You'll collaborate with our product and engineering teams to help to answer these questions and more.
• Build Machine Learning data solutions for production, such as Q&A using Deep Learning and NLP techniques, leveraging open source models and tools.
• Collaborate with the rest of our data pipeline and ML engineering teams to build the robust, high-scale systems that underlie all of our data processing and ML Operations.

Qualifications:

Don't meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every qualification. At Affinity, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you're excited about this role, but your past experience doesn't perfectly align with the qualifications above, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

Required:

• You have 3+ years of industry experience in software development.
• You have a minimum of 2 years of experience with applied machine learning engineering in production.
• You are a builder and care deeply about building systems and products that deliver meaningful value to users quickly and iteratively.
• You have experience working with large, multi-terabyte datasets, and are comfortable with tools for high-scale data ingestion, transformation, analysis, and prediction.
• You're excited to work collaboratively within engineering and across functional teams.
• You're eager to contribute your ideas and experiences to help Affinity continuously improve as a product and as a company.

Nice to have:

• You have experience with NLP techniques and domains, including Named Entity Recognition, and Deep Learning.
• Experience with Databricks and Delta Lake.
• Experience working with AWS, Kubernetes.
• Experience with entity resolution and enrichment-focused data pipelines.

About The Role:

We’re looking for seasoned ML Infrastructure engineers with experience designing, building and maintaining training and serving infrastructure for ML research.

Responsibilities:

• Provide infrastructure support to our ML research environment
• Building tooling to diagnose cluster issues and hardware failures
• Monitor deployments, manage experiments and generally support our research

Requirements:

• 4+ years' experience supporting the infrastructure within a ML environment
• Experience in support of Large Language Models is highly desired
• Experience developing tools used to diagnose ML infrastructure problems and failures is also required
• Experience with large GPU clusters and high-performance computing/networking
• Experience with cloud platforms (e.g., Compute Engine, Kubernetes) is also required