Machine Learning Engineer 4 - Machine Learning Foundations (MLX)

Description

The company builds and ships AI and technology products, and the team solves complex business problems in a collaborative, iterative setting.

You will join a large group of builders and problem solvers focused on real problems and real customer needs.

The company wants Machine Learning Engineers who are keen to apply current open source frameworks, advanced algorithms, and new technologies.

The role puts you at the front of major AI transformation work and in scaling production models across the company.

Responsibilities

• Your ML engineering work overlaps with Ops, Modeling, and Data Engineering, and you will take on one or more of the following.
• Design, build, and or deliver ML models and components for real business problems, working with the Product and Data Science teams.
• Make infrastructure decisions informed by ML modeling issues, such as model and feature choice, data, training, hyperparameter tuning, dimensionality, bias and variance, and validation.
• Write and test application code, develop and validate ML models, and automate testing and deployment to solve complex problems.
• Work in a cross-functional Agile team to create and improve software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Build or use cloud architectures, technologies, or platforms to deliver optimized ML models at scale.
• Build optimized data pipelines that feed ML models.
• Apply continuous integration and deployment practices, including test automation and monitoring, to deploy models and application code reliably.
• Keep code well managed to reduce vulnerabilities, keep models governed from a risk standpoint, and follow Responsible and Explainable AI practices.
• Write code in Python, Scala, or Java.

Requirements

• A Bachelor's degree or higher in Computer Science, Machine Learning, or a related quantitative field such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering.
• At least 4 years of experience programming in Python, Java, Golang, or C++.
• At least 4 years of ML experience with PyTorch or TensorFlow and libraries such as Pandas, NumPy, and Scikit-learn.
• At least 4 years of experience running large scale distributed systems, such as Spark or Ray, to prepare AI or ML data.
• At least 2 years of experience deploying and running ML solutions in production, running production services on AWS, GCP, or Azure, and managing containerized ML systems at scale with Kubernetes.
• Preferred: A Master's or Doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
• Preferred: 3+ years optimizing ML algorithms, configurations, and infrastructure.
• Preferred: 3+ years applying software development best practices, including source control, testing, code reviews, and CI/CD.
• Preferred: 3+ years building resilient software with pre-production testing, advanced deployment methods such as one-box, blue/green, and gradual dial-up, monitoring, alarms, and incident response plans.
• Preferred: 3+ years working with supervised, semi-supervised, unsupervised, and reinforcement learning, model types such as regression, classification, and clustering, architectures such as RNNs, CNNs, LSTMs, and Transformers, training concepts, and diagnosing underfitting and overfitting.
• Preferred: 3+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
• Preferred: 1+ years as a technical lead developing ML solutions with industry best practices and automation.
• Preferred: Authored or co-authored a paper on an ML technique, model, or proof of concept.

Conditions

The company will not sponsor a new applicant for employment authorization or offer immigration related support for this position, including H-1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other work authorization that requires employer immigration support.

Base salary ranges, full-time annual, by location: Sales Territory $179,400 - $204,700; McLean, VA $197,300 - $225,100; New York, NY $215,200 - $245,600; Richmond, VA $179,400 - $204,700; San Jose, CA $215,200 - $245,600. Candidates hired elsewhere get the range for their location, and the exact offered salary appears in the offer letter.

Part-time pay is prorated based on the agreed number of regular hours.

The role applies to a minimum of 5 business days of applications.

No agencies.

The company is an equal opportunity employer and does not discriminate, and it considers applicants with criminal histories in line with applicable law.

Benefits

• Eligible for performance based incentive compensation, which may include cash bonuses and or long term incentives, discretionary or non discretionary depending on the plan.
• Comprehensive health, financial, and other benefits, with eligibility varying by full or part-time status, exempt or non-exempt status, and management level.