Lead Machine Learning Engineer — Data Classification & Security

Lead Machine Learning Engineer — Data Classification & Security

Engagement: Full-time commitment; initial three-month contract-to-hire

Compensation: $135,000–$155,000 annualized, based on experience, paid monthly

About Symosis Security

Symosis Security is a Silicon Valley–based cybersecurity consulting firm helping enterprise and public-sector clients strengthen security through AI security, security engineering and automation, advisory, and managed security services. Our senior practitioners work directly with clients to solve complex problems and deliver measurable results.

We’re expanding our AI and data security practice and looking for a hands-on technical lead who wants ownership, direct client exposure, and opportunities to help grow the team.

About the Role

You will lead a small engineering team delivering an enterprise data classification engagement. Working alongside an applied data scientist and ML engineer, you will build and improve classifiers that identify sensitive data, evaluate performance, and deliver reliable engineering outcomes.

This is a hands-on technical leadership role with significant coding and implementation responsibility.

What You’ll Do

• Own the technical approach, implementation decisions, and engineering quality.
• Build, configure, and improve classifiers within an existing enterprise framework.
• Guide ground-truth development, performance evaluation, and error analysis.
• Improve precision and recall through systematic tuning and testing.
• Review code and oversee holdout validation, edge-case testing, and regression testing.
• Work directly with client technical stakeholders to resolve implementation challenges.
• Deliver maintainable code, configurations, technical documentation, and handoff materials.
• Coordinate delivery priorities and mentor the engineering team.

What You Bring

• 8+ years of relevant software or ML engineering experience, including technical leadership.
• Demonstrated ownership of classification systems from evaluation through deployment.
• Strong Python, SQL, debugging, code review, and automated testing skills.
• Experience with rule-based and/or ML-based classification systems.
• Practical understanding of precision, recall, sampling bias, overfitting, and validation.
• Ability to work within existing enterprise codebases and toolchains.
• Strong client communication and experience leading a small technical team.

Preferred Experience

• Sensitive-data discovery, DLP, privacy engineering, security analytics, or content classification.
• Building and operating classification systems at enterprise scale.
• Prior experience at Google, Microsoft, Meta, Amazon, Apple, NVIDIA, Tesla, or a comparable technology organization.

We prioritize demonstrated technical ownership and delivery over employer names.

Compensation & Employment Structure

Compensation is $135,000–$155,000 annualized, based on relevant experience and qualifications, paid monthly during the initial three-month contract period. Partial months will be prorated.

The initial contract requires a full-time commitment and includes no company-provided employee benefits. Following a review of performance and ongoing business needs, the role may convert to permanent full-time employment with Symosis’s standard employee benefits. Compensation will be reviewed at conversion.

Work must be performed within the United States, with availability for Pacific Time collaboration. SF Bay Area candidates are preferred; remote arrangements are subject to client approval.

Please include:

• Location: City/state and availability to collaborate during Pacific Time business hours.
• Start date: Earliest available start date and any notice period.
• Compensation: Confirmation that the posted compensation range and initial three-month contract-to-hire arrangement work for you.
• Relevant experience: A brief example of a classification system you personally built or improved, including your role, approach, and measurable results. Please do not share confidential client information.