Machine Learning Engineer
Machine learning engineer who trains, evaluates, and productionizes models and LLM-powered applications for financial products. Requires 2+ years of ML systems experience, strong Python and PyTorch skills, production data pipelines, model evaluation, and API development.
About the job
Responsibilities
- Develop and train sequence, embedding, and classification machine-learning models on large-scale financial and behavioral data.
- Build feature and data pipelines that transform raw event data into training-ready datasets and maintain consistency between training and serving features.
- Design offline and online evaluation for models and agentic workflows, including success metrics, backtests, A/B tests, error tracing, and regression suites.
- Deploy models to production and own serving infrastructure, latency and cost optimization, retraining loops, and monitoring for drift and performance degradation.
- Fine-tune and adapt large language models and build orchestration involving prompting, memory and context pipelines, retrieval, and tool integrations.
- Build Python backend services and RESTful APIs for models and agentic applications.
- Instrument pipelines with logging, tracing, and distributed monitoring.
- Collaborate with ML engineers, data scientists, and product teams to develop intelligent and safe AI features.
Requirements
- Bachelor's or master's degree in Computer Science, Engineering, Statistics, or a related field, or equivalent experience.
- 2+ years of industry experience building and shipping machine-learning systems.
- Strong Python and hands-on experience with PyTorch, NumPy, pandas, and scikit-learn.
- Experience using AI-assisted development tools such as GitHub Copilot, Cursor, or ChatGPT.
- Strong understanding of model architecture, training dynamics, regularization, and model diagnostics.
- Experience with large-scale data processing and production feature engineering.
- Experience designing ML and LLM evaluation metrics, automated checks, offline test harnesses, and behavioral regression suites.
- Working knowledge of LLM APIs, prompt engineering, and an agentic framework or equivalent.
- Experience with API design, asynchronous workflows, and SQL or NoSQL databases.
- Clear communication and collaborative working style.
Nice-to-haves
- LLM fine-tuning with Unsloth, Axolotl, LLaMA-Factory, or HuggingFace PEFT/TRL, including LoRA or QLoRA.
- Distributed training or representation learning.
- MLOps tools for experiment tracking, feature stores, or model registries, such as MLflow, Weights & Biases, or Feast.
- Vector stores such as Weaviate, Pinecone, or Qdrant.
- OpenTelemetry or similar observability frameworks.
- Container-based deployment or serverless environments, including Docker or AWS Lambda.
- Experience in fintech, fraud, risk, or credit modeling.
Compensation
- Base salary: $187,000–$229,000, plus equity and benefits.
Skills
Python, PyTorch, NumPy, pandas, scikit-learn, Spark, Databricks, LLM APIs, Prompt Engineering, REST APIs, SQL, NoSQL, Docker, MLflow, OpenTelemetry
Similar jobs
ML Engineering jobs
SentiLink
United States
Build and deploy production machine-learning models for fraud detection, identity verification, and financial risk products. The role suits new PhD graduates or early-career researchers with strong quantitative foundations, Python experience, and interest in owning the full ML lifecycle.
Decagon
San Francisco, CAResearch Engineer focused on building and deploying real-time audio and speech models for conversational voice agents. The role requires experience with speech or multimodal machine learning, production inference, Python, and PyTorch, with emphasis on taking research from prototype to measurable production impact.
Decagon
San Francisco, CAResearch Engineer focused on making conversational AI agents safe, reliable, and controllable in production. The role develops evaluations, safeguards, post-training methods, and monitoring systems, requiring 2+ years of AI/ML or safety experience and strong Python and production engineering skills.
Ambral
New York, NYBuild replayable enterprise environments, evaluation systems, graders, and post-training workflows for AI agents. The role spans machine-learning research and production engineering and requires 1–7 years of software or ML systems experience.
AfterQuery
San Francisco, CA
Research Engineer designing post-training infrastructure and running controlled experiments to measure how datasets affect foundation-model behavior. Requires at least 2 years of ML or research engineering experience, strong Python, and hands-on experience with PyTorch, JAX, Ray, Slurm, and LLM post-training.