Design, optimize, and operate large-scale data pipelines focused on performance and scalability. Support ML workflows including feature extraction, model training, and real-time inference. Build solutions using Python, SQL, AWS data services and Apache Spark, and train/deploy models with SageMaker, TensorFlow, or PyTorch in a fast-paced remote environment.
This is a remote position.
Datasys builds high-scale, AI-powered data platforms that helps enterprises leverage deep consumer and business intelligence. If you love large-scale data engineering and innovation, we encourage you to apply.
Who we are looking for:
- You have a strong record of delivering results which matter to the company.
- You are goal oriented, driven, and must be comfortable doing whatever is necessary to achieve the goal.
- You have a deep understanding of data pipelines, focusing on performance, scalability, and solving bottlenecks, including those in machine learning workflows such as feature extraction, model training, and real-time inference.
- You are highly proficient in Python and SQL, with experience in AWS data services like Redshift, Glue, Athena, S3, and EMR, particularly for running large-scale data processing with Apache Spark.
- You are familiar with ML infra and frameworks, such as SageMaker, TensorFlow, or PyTorch and have hands-on experience training and deploying models.
Our environment best suits those who:
- Excel in hard work and a high-velocity atmosphere.
- Value exceptional performance and continuous improvement. We believe that if you aren’t constantly learning, you aren’t growing.
- Take responsibility for making impactful decisions that drive Datasys’ future.
Datasys operates in a hybrid work environment, but this position is fully remote and the ideal candidate thrives with the freedom and flexibility this structure provides.
Our interview process:
Fast! From application to offer in a week.
If you’ve done anything notable, such as created an open source project with 500+ stars/downloads, shipped a product with 5K+ downloads, made $50k+ from a single product/project/trade, or similarly impactful, let us know and we’ll prioritize your app.
Zero Expectations
We look for people with a track record of building.
1. Initial meet -Someone on our team will chat with you.
2. Personality assessment- We’ll send you a personality assessment to understand you better.
3. Take home challenge- We’ll send you a small take home challenge.
4. Follow-up interview- We’ll review the take home challenge together.
5. Offer- We’ll give you an offer you’ll be happy with.
Similar Jobs
Blockchain • eCommerce • Fintech • Payments • Software • Financial Services • Cryptocurrency
Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.
Top Skills:
Agent-Assisted Operations ToolingBatch PipelinesCloud InfrastructureCoding AgentsData WarehousesEmbeddingsEvaluation HarnessesEvent StreamsExperimentation SystemsFeature StoresJavaKotlinKubernetesLakehousesLightgbmModel-Serving InfrastructureObservability ToolingPythonPyTorchRanking/Retrieval SystemsRecommendation FrameworksSemantic SearchSQLTensorFlowWorkflow OrchestrationXgboost
Blockchain • Fintech • Mobile • Payments • Software • Financial Services
Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.
Top Skills:
Batch PipelinesCloud InfrastructureCoding AgentsData WarehousesEmbeddingsEvent StreamsExperimentation SystemsFeature StoresJavaKotlinKubernetesLakehousesLightgbmModel-Serving InfrastructureObservability ToolingPythonPyTorchRanking/Retrieval SystemsRecommendation FrameworksSemantic SearchSQLTensorFlowWorkflow OrchestrationXgboost
Healthtech • Software
Build and own a versioned, documented gold data layer from silver Lakehouse sources for AI research. Transform, validate, and curate multi-modal datasets, build reusable Databricks/PySpark pipelines, automate quality and labeling, support researcher workflows, and maintain lineage and dataset snapshots for model development and evaluation.
Top Skills:
AirflowAsrAWSAzureCcdaCi/CdDagsterDatabricksDatabricks WorkflowsDbtDelta LakeFeature StoreFhirGitHl7V2Hugging Face DatasetsIcd-10Llm ApiLoincMinhash/LshMlflowOcrParquetPrefectPysparkPythonPyTorchSnomed CtSnorkelSparkSQLUnity Catalog
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
