Senior ML Engineer

Benefits

Parental leave

Not verified

Non-birth-parent leave

Not verified

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

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Verification

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Salary

$180K-$200K From the posting source checked Jun 20, 2026

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

U.S. benchmark only; posted salary is not compared across countries or currencies.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Work mode

Hybrid From the posting source checked Jun 20, 2026

In-office days

2 days From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

Not verified

Application

Cover letter

Not verified

Assessment

Required From the posting source checked Jun 20, 2026

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Senior ML Engineer Hybrid WHY TEXTUS TextUs on a mission to revolutionize business communication by enabling seamless and impactful engagement between workers and consumers. With a focus on innovation, ease of use, and delivering measurable results, our strategy is rooted in creating tools that outperform other messaging solutions while fostering trust and value for our customers and stakeholders. At TextUs, every team member is empowered to make a difference. Our collaborative and data-driven culture, combined with the guidance of a proven leadership team, ensures you have the resources and support to excel. Together, we're building the future of mobile-first, conversational engagement and redefining what's possible for businesses and their stakeholders. RESPONSIBILITIES We're moving from a product where AI is a feature you can turn on to one where it's a layer that runs through everything: response suggestions, abuse detection, summarization, lead scoring, intent classification. That shift only works if there's an engineering layer underneath that treats ML systems with the same rigor as the rest of production. We're AI-pragmatic, not AI-maximalist. Most of what we ship will run on frontier model APIs with retrieval and good prompt engineering. Some will run on small classifiers we train ourselves. A few things will justify fine-tuning against our eleven years of conversation data. Your job is to know which is which, and to build the platform that lets us move between them without rebuilding from scratch every time. You own the ML and AI engineering layer end to end. The ML Ops platform:

Read the full description at boards.greenhouse.io. FewerJobs shows a preview and links to the original posting.