Senior Software Engineer, Machine Learning

New York, New York, United StatesFull TimeSenior$175,000–$250,000 /yrPosted 2 months agoVisa sponsorship available

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Role summary

Point72's Surveillance Team is seeking a Senior Software Engineer, Machine Learning to join their Knowledge Graph Intelligence team. This role focuses on building next-generation intelligent systems using graph technology, open-source solutions, modern cloud architectures, and AI. You will design and implement critical infrastructure for the ML lifecycle, including data processing, feature engineering, model training, deployment, and monitoring. Responsibilities include architecting ML models, engineering data pipelines, owning the MLOps framework, implementing deployment strategies, optimizing inference performance, and managing cloud infrastructure with Terraform. The role requires 5+ years of experience in software, data, or ML engineering, strong SQL proficiency, and experience with various data pipeline tools, cloud platforms (AWS), containerization, and CI/CD. Experience with specific ML models like GNNs is also required.

Software Engineer, Machine Learning (MLOps & Data)

A Career with Point72’s Surveillance Team

On the Knowledge Graph Intelligence team, you’ll work alongside product managers, engineers, and data scientists to build the next generation of intelligent systems through graph technology. We’re a team of experts who experiment and work to discover new ways to harness open-source solutions, modern cloud architectures, and sophisticated Artificial Intelligence (AI) solutions, while embracing enterprise agile methodologies. Our commitment to building and innovating in the AI space provides the framework intended to drive smarter decision-making and enhance how we build and operate our platforms and applications.

What you’ll do

In this data-heavy role, you will design and build mission-critical infrastructure that powers our machine learning lifecycle, from large-scale data processing and feature engineering to model training, real-time deployment, and monitoring. Specifically, you will:

Architect and implement the full lifecycle of ML models, from data ingestion to production inference, contributing to the design of our next-generation, event-driven architecture, using technologies like gRPC, Kafka, and high-performance API frameworks, like FastAPI, Spring WebFlux, and Axum.

Engineer and automate robust, large-scale data processing pipelines (ETL/ELT) using tools like Spark, dbt, and workflow orchestrators, and lead the design and implementation of our Feature Store strategy.

Own the MLOps framework for model training, versioning, and deployment, including CI/CD pipelines, automated workflows, and experiment tracking and evaluation tooling.

Implement sophisticated deployment strategies, including canary, blue-green, shadow, and A/B testing, to ensure safe, zero-downtime releases, and optimize inference performance for LLMs and other large models.

Leverage cutting-edge tools and techniques like quantization and compilation to maximize throughput and minimize latency.

Collaborate with data scientists to develop models and optimize model performance for low-latency serving using techniques like Python performance tuning.

Define, provision, and manage our cloud infrastructure using Terraform, working hands-on with a wide array of cloud services across compute, storage, and machine learning platforms.

What’s REQUIRED

5+ years of experience in a software, data, or ML engineering role.

Strong proficiency in SQL.

Experience building and orchestrating data pipelines using tools such as Spark, dbt, and Dagster/Airflow, as well as data warehouses like Snowflake, Redshift, BigQuery.

Understanding of infrastructure as code, including experience with Terraform.

Proficiency with containerization and orchestration including Docker and Kubernetes.

Hands-on experience with CI/CD tools and ML lifecycle tools, including Jenkins, MLflow, Kubeflow, and W&B.

Experience with AWS and its core services including S3, EC2, Lambda, RDS, and EMR, and practical experience with Boto3 and AWS ML services SageMaker and Bedrock.

Understanding of modern ML models and ability to discuss the performance characteristics and engineering trade-offs that influence deployment decisions.

Experience with systems incorporating Graph Neural Networks (GNNs), recommendation systems, anomaly detection, and complex time-series models.

Commitment to the highest ethical standards.

We take care of our people

We invest in our people, their careers, their health, and their well-being. When you work here, we provide:

Fully-paid health care benefits

Generous parental and family leave policies

Volunteer opportunities

Support for employee-led affinity groups representing women, people of color and the LGBT+ community

Mental and physical wellness programs

Tuition assistance

A 401(k) savings program with an employer match and more

About point72

Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry’s brightest talent by cultivating an investor-led culture and committing to our people’s long-term growth. For more information, visit https://point72.com/.

The annual base salary range for this role is $175,000-$250,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.

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