Senior Machine Learning Engineer

Fractal; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life insurance

Disability insurance

11 paid holidays

12 weeks parental leave

Paid time off

Job summary

Fractal is seeking a Senior Machine Learning Engineer in the San Francisco Bay Area to lead AI-driven solutions across industries. You will own problem framing, model development, production deployment, and client adoption for enterprise applications such as forecasting, maintenance, and supply chain optimization.

You will guide junior team members, collaborate with engineering and product teams, and stay current with Generative AI, time-series, and enterprise AI platforms to deliver measurable

Qualifications

• 5+ years of applied machine learning, data science, or AI experience.
• Experience owning projects or major workstreams from problem definition to delivery.
• Excellent Python programming with strong OOP skills.
• Proficiency with ML frameworks like Scikit-learn, PyTorch, TensorFlow.
• Experience with production ML tools and lifecycle (experimentation, deployment, monitoring).
• Exposure to cloud ML services (AWS/Azure/GCP) and enterprise platforms.
• Ability to lead independently and communicate with technical and non‑technical audiences.

Responsibilities

• Own end‑to‑end ML delivery for proprietary platform‑based solutions.
• Collaborate with internal and client stakeholders to translate needs into models.
• Lead development of ML, forecasting, optimization, and AI models at scale.
• Evaluate data readiness, feature strategies, and data quality risks.
• Design modeling approaches balancing accuracy, explainability, and usability.
• Integrate models into client applications and operational workflows.
• Establish reproducibility, governance, monitoring, and documentation practices.
• Provide senior guidance to junior data scientists and engineers.

Skills

Python

OOP

Scikit-learn

PyTorch

TensorFlow

pandas

NumPy

XGBoost

MLflow

Airflow

Kubeflow

Databricks

Spark

Cloud services

Education

MS/PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, OR related field

Tools

MLflow

Airflow

Kubeflow

Databricks

Spark

Job description

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.

Please visit Fractal | Intelligence for Imagination for more information about Fractal

***Please note that this role is specifically located in the San Francisco Bay Area and requires 100% onsite availability. For candidates who are not local, we provide weekly travel or relocation.***

Note: This position is not eligible for Immigration Sponsorship at this time.

Position Overview

As a Senior Machine Learning Engineer at Fractal Analytics, you will be at the forefront of building and deploying AI-driven solutions that address critical business challenges across industries. You will work with cutting-edge AI platforms to develop both custom and pre-built enterprise applications. These applications include use cases such as demand forecasting, asset reliability, predictive maintenance, inventory optimization, supply chain planning, anomaly detection, operational intelligence, and other applied AI solutions.

This is a senior delivery and ownership role where you will be responsible for leading machine learning and data science workstreams from business problem definition through model development, production deployment, and client adoption. The right candidate can operate independently, guide technical decisions, work directly with stakeholders, and ensure AI solutions are practical, scalable, and valuable in real business environments.

Responsibilities Include

• Own end-to-end machine learning delivery for proprietary AI platform-based solutions, from problem framing through production implementation.
• Partner with internal and vendor AI teams along with client stakeholders to translate business needs into analytical approaches, model requirements, and measurable success criteria.
• Lead the development of machine learning, statistical, forecasting, optimization, and AI models for enterprise-scale applications.
• Evaluate data readiness, define feature strategies, identify data quality risks, and recommend practical paths to production.
• Design modeling approaches that balance accuracy, explainability, reliability, scalability, and business usability.
• Collaborate with engineering, product, and platform teams to integrate models into various client AI applications and operational workflows.
• Establish and follow best practices for experimentation, validation, documentation, reproducibility, monitoring, and model governance.
• Support deployed solutions by monitoring performance, diagnosing issues, and driving continuous improvement.
• Communicate technical findings, assumptions, tradeoffs, and recommendations to both technical and non-technical audiences.
• Provide senior-level guidance to junior data scientists, engineers, and analysts contributing to project delivery.
• Stay current with developments in machine learning, Generative AI, time series modeling, optimization, and enterprise AI platforms, applying new methods where they improve outcomes.

Qualifications

• MS or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, Operations Research, or a related quantitative field.
• 5+ years of applied machine learning, data science, or AI experience in a professional setting.
• Demonstrated experience owning projects or major workstreams from initial problem definition through delivery.
• Excellent programming skills in Python to include strong overall OOP experience.
• Strong proficiency with machine learning and data science tools such as Scikit-learn, PyTorch, TensorFlow, pandas, NumPy, XGBoost, or similar frameworks.
• Broad knowledge of machine learning and AI methods, including when to apply different approaches and what tradeoffs to consider.
• Project experience in several of the following areas: supervised learning, unsupervised learning, time series forecasting, deep learning, operations research, optimization, anomaly detection, or Generative AI.
• Familiarity with the machine learning lifecycle, including experimentation, versioning, deployment, monitoring, governance, and retraining.
• Experience with production-oriented ML tools or workflow platforms such as MLflow, Airflow, Kubeflow, Databricks, or similar.
• Familiarity with scalable data and AI technologies such as Spark, distributed processing, streaming systems, or cloud-based ML services.
• Prior exposure to AWS, Azure, or Google Cloud, especially AI/ML services, is advantageous.
• Ability to lead independently while collaborating effectively with client, product, engineering, and delivery teams.
• Strong communication skills, including the ability to explain complex analytical work clearly to business stakeholders.
• High ownership mindset, strong judgment, practical problem-solving ability, and a desire to create measurable business impact.

Preferred Qualifications

• Hands-on experience with the C3 AI platform.
• Experience working in an embedded client environment, partner delivery model, enterprise SaaS implementation, or AI platform delivery role.
• Experience delivering AI solutions for large enterprises or Fortune 500 clients.
• Domain experience in manufacturing, energy, utilities, supply chain, aerospace, telecommunications, financial services, or other operationally complex industries.
• Experience with demand forecasting, asset reliability planning, predictive maintenance, inventory optimization, or supply chain optimization.
• Experience mentoring junior team members or leading small technical teams.

Pay

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $120,000 - $180,000. In addition, you may be eligible for a discretionary bonus for the current performance period.

As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides for 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation.

Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.