Senior Machine Learning Engineer
$180,000 to $210,000 base + 10% bonus
Remote, United States
Applied ML for vertical SaaS
We are hiring a Senior Machine Learning Engineer for a high growth software company building modern products for a large, operationally complex industry. This is an opportunity to join early in the buildout of an AI and machine learning function with meaningful production scale, strong proprietary data, and direct product impact.
This role is designed for someone who wants to work across the full lifecycle of applied machine learning. You’ll help take ideas from discovery through experimentation, model development, deployment, and monitoring, while partnering closely with product, engineering, and data stakeholders. The scope is broad, practical, and tied to real customer workflows rather than isolated research.
What you’ll be doing
You’ll work across a wide range of production ML problems, including:
• designing, building, and shipping ML models across regression, classification, clustering, ranking, and recommendation use cases
• owning end to end ML workflows, including data gathering, feature engineering, experimentation, training, evaluation, deployment, and monitoring
• defining model performance metrics, running A/B tests, and improving systems based on real world feedback
• contributing to shared feature stores and reusable ML infrastructure across batch and real time contexts
• working within a modern MLOps environment to support scalable, reliable deployment
• contributing to model versioning, orchestration, and CI/CD workflows for machine learning
• collaborating closely with data scientists and data engineers on high impact product initiatives
• helping build NLP, LLM, and agentic AI components where they make sense for the product
What they’re looking for
• proven experience shipping machine learning systems into production, not just prototypes or research environments
• strong Python skills and hands on experience with frameworks such as PyTorch or TensorFlow
• strong grounding in classical machine learning and some depth in areas such as feature engineering, experimentation, model evaluation, ranking, clustering, and recommendation systems
• around 5+ years of industry experience in applied machine learning, with advanced degrees considered where relevant
• strong collaboration and communication skills, with the ability to work effectively across engineering, product, and design
• someone who enjoys broad ownership and can operate well in a fast moving environment with ambiguity
Nice to have
• familiarity with MLOps tooling and concepts such as model versioning, orchestration, monitoring, evaluation, and model serving
• experience with tools such as MLflow, DVC, Airflow, or similar systems
• exposure to LLMs, NLP frameworks, and agentic workflows
• cloud infrastructure experience in AWS or GCP
• strong SQL skills and experience working with large scale data
• familiarity with vector databases, embeddings, retrieval systems, or feature stores
• previous startup experience or experience building customer facing ML systems in vertical software environments
Tech Stack & Environment
This team works across Python, SQL, cloud infrastructure, production data systems, modern MLOps workflows, feature pipelines, and ML deployment environments. The role sits in a collaborative setup alongside engineering, product, data science, and data engineering, with scope spanning model development, infrastructure, and applied AI systems.
Why this role stands out
This is not a narrow research role and it is not a basic API integration job with an AI label attached. It is a chance to join a foundational team building practical machine learning systems that solve real operational problems at scale. You’ll have room to shape how the function evolves while working on products that are grounded in real workflows, meaningful data, and clear customer value.
People In AI is a specialist search firm focused on data, machine learning, AI engineering, and adjacent technical leadership hires. We work closely with candidates to give clear context on team structure, technical scope, interview process, and what actually matters for success in the role, so you can make a well informed decision about whether the opportunity is the right fit.
