Senior ML Engineer Jobs
Senior ML Engineer jobs are open across tech, finance, healthcare, and defense, from mid-level to staff and principal, with specializations in NLP, computer vision, and recommendation systems. Find a role that fits from the openings below and apply directly.
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About THIA
THIA is transforming how small and medium enterprises build internal applications and automate business processes. Our AI-powered platform enables business experts to create custom applications using natural language, eliminating the need for expensive development teams. We’re well-funded, generating revenue, and solving real problems for companies that need more than off-the-shelf software.
About the Role
This is a hands-on ML role for someone who wants depth in modeling and pragmatism about everything else it takes to ship. You’ll own ML work end to end: framing the problem, picking the right approach (fine-tune, prompt, retrieve, or something cheaper), fine-tuning the language models that power our platform when that’s the answer, building the evals that tell us whether things are getting better, and getting the result into production.
We’re open to a range of profiles. Some people will go deepest on fine-tuning and evaluation; others will be strongest at shipping ML features across the full stack. We’ll shape the scope around where you’re strongest. You’ll work closely with a small, senior team and have direct influence over what we train, how we measure quality, and how we build.
We move fast, keep our codebase clean, and take tech debt seriously.
What You’ll Do
Modeling & Evaluation
- Fine-tune transformer-based models (instruction tuning, LoRA/PEFT, RLHF/DPO, distillation) and ship the result through eval into production
- Make pragmatic tradeoffs across fine-tuning, prompting, and retrieval based on what the problem actually needs
- Design and curate evaluation datasets that meaningfully reflect real customer behavior
- Build and extend evaluation pipelines: offline metrics, regression detection, and LLM-as-judge aligned against human judgment
- Run experiments end to end: hypothesis → controlled comparison → calibrated metrics → decision
Shipping ML Features
- Ship ML features end to end: problem framing, modeling, evaluation, deployment, iteration
- Work with cloud ML infrastructure across training, serving, and monitoring
- Own model-quality monitoring in production and close the loop back into training data
- Drive concrete quality gains through eval results, customer feedback, and prompt iteration
Collaboration
- Work autonomously while staying tightly coordinated with a small, async-first team
- Partner with the platform team on model serving, observability, and the multi-tenant rollout, and with product on what to ship next
- Contribute to architectural decisions and internal documentation
What We’re Looking For
Must-Haves
- Strong Python; working expertise with PyTorch and the HuggingFace transformers ecosystem
- Hands-on experience training or fine-tuning transformer-based models, with at least one result shipped or rigorously evaluated
- Has built or substantially contributed to a model-evaluation pipeline, with experimental rigor: hypothesis design, controlled comparisons, calibrated metrics
- Has carried at least one ML feature through the full lifecycle (data → train → eval → deploy → monitor)
- Comfortable with cloud ML infrastructure (GCP, AWS, or Azure) across training, serving, and monitoring
- Experience collaborating with product or domain experts to ship real features
Nice to Have
- LLM eval and observability work (LLM-as-judge, human-judgment alignment, trace enrichment)
- RAG or retrieval system design experience
- MLOps tooling (model registries, feature stores, experiment tracking)
You Don’t Need
- Mastery of every part of the ML stack. Depth in one area plus working knowledge across the rest is the target.
Our Stack
Python · PyTorch · HuggingFace · Modal · GCP · PostgreSQL / SQLite · Qdrant · Redis · Docker · GitLab CI/CD · Datadog
Compensation & Benefits
The salary range for this position is $110,000 – $160,000 per year. Placement within the range is based on role-relevant experience and skills. This range reflects what we genuinely expect to pay for the position; it is determined by the role and is not negotiable. Compensation also includes early-stage equity, health/dental/vision benefits, paid time off, and a fully remote work arrangement.
Equal Opportunity
THIA is an equal opportunity employer. We evaluate all applicants based on skill and ability to do the work, and we do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.
Accommodations
If you need a reasonable accommodation at any point in the application or interview process, let us know and we’ll work with you.
How We Review Applications
We use AI tools to assist in reviewing applications and identifying qualified candidates. All final hiring decisions, including who advances through the interview process and who receives an offer, are made by our team.
A Note on AI
We actively encourage using AI tools to move faster: for coding, experiment setup, analysis, and documentation. Real engineering judgment is still required: to direct AI effectively, catch what it misses, and make the final call before work ships.
What You Gain
- Ownership: end-to-end accountability for model quality and ML features at a growing AI company
- Impact: direct collaboration with leadership and real influence on technical direction
- Growth: a clear path to a lead role as the team expands
- Equity: early-stage equity at an AI startup
- Quality: a clean codebase and a team that takes evaluation rigor and tech debt seriously
Pay: $110,000.00 - $160,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
Work Location: Remote
Senior ML Engineer Jobs by Experience Level
Top Cities Hiring Senior ML Engineers
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Find Senior ML Engineer JobsSenior ML Engineer Job Market
Who's Hiring
- TikTok73

- Meta60

- Uber50

- Amazon Web Services48

- Lockheed Martin44

Top Industries Hiring
- Technology & Software83
- Electronics & Hardware14
- Biotechnology & Pharmaceuticals12
- Education5
- Banking & Financial Services5
What Employers Look For
The qualifications that appear most often in senior ML engineer jobs.
- 5 or more years of hands-on machine learning engineering experience in production environments
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX
- Experience designing and deploying end-to-end ML pipelines at scale
- Familiarity with MLOps tooling including experiment tracking, model registries, and CI/CD for models
- Strong understanding of distributed computing and cloud platforms such as AWS, GCP, or Azure
- Graduate degree in computer science, statistics, or a related quantitative field preferred
Tips for Your Senior ML Engineer Job Search
Quantify model impact on your resume
Recruiters screening senior ml engineer resumes look past model architectures and want outcomes. Replace 'built a classification model' with the latency reduction, accuracy lift, or revenue impact it delivered. Numbers make your contributions defensible in technical screens.
Apply early to roles that fit
Migrate Mate lists senior ml engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target job descriptions by deployment environment
Senior ML roles split hard between research-heavy and production-focused. Scan each posting for keywords like 'MLOps', 'model serving', or 'real-time inference' versus 'publications' or 'R&D'. Applying to the wrong side wastes your strongest talking points and theirs.
Prepare a project walkthrough before interviewing
Most senior ml engineer system design rounds ask you to walk through a real project end-to-end. Pick one where you made a consequential architecture decision, owned the tradeoffs, and can explain what you would do differently. Vague project summaries are the most common screen failure.
Negotiate scope before you negotiate compensation
At the senior level, the team size, GPU budget, and model ownership structure affect your actual work more than base pay does. Clarify those in the final round before discussing offer details so you can evaluate whether the role matches your growth goals.
Follow up with a technical observation after interviews
A follow-up email that references a specific technical problem from the interview, adds a detail you did not get to, or proposes an alternative approach signals engagement that generic thank-you notes do not. It also gives the hiring manager something concrete to share with the panel.
Senior ML Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most senior ml engineers?
The companies hiring the most senior ml engineers right now include TikTok, Meta, and Uber, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of November 2026. Demand is concentrated in companies with active model deployment needs rather than early-stage research programs.
How many senior ml engineer jobs are remote?
About 74% of senior ml engineer openings are fully remote or hybrid as of November 2026, making it one of the more remote-accessible senior engineering roles. Sub-areas with the highest remote share include NLP, ranking systems, and applied research, where most collaboration happens asynchronously through code and documentation.
How do you become a senior ml engineer?
Reaching the senior ml engineer level typically requires building and shipping production ML systems, not just research notebooks. Start by owning a model end-to-end at a current employer, from data pipeline to serving infrastructure. Then deepen your MLOps knowledge, contribute to system design decisions, and mentor junior teammates. Documented production impact in your portfolio moves you past resume screens faster than additional credentials.
Can you get a senior ml engineer job with limited experience?
Breaking into senior ml engineer roles with limited direct experience is difficult but possible through adjacent paths. Engineers with strong software engineering backgrounds who have shipped ML-adjacent systems, such as real-time feature stores or data pipelines, are often considered. Demonstrating ownership of a deployed model project, even from a side project or open-source contribution, matters more than years on a resume at many companies.
What does the senior ml engineer interview process look like?
Most senior ml engineer interview processes include a recruiter screen, a take-home or live coding round focused on ML fundamentals, a system design session covering model serving or training infrastructure at scale, and a behavioral round assessing cross-functional collaboration. Some companies also include a research or paper discussion round. The system design round carries the most weight at the senior level and is where most candidates differentiate themselves.
Where can I find and apply to senior ml engineer jobs?
You can find and apply to senior ml engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Find roles that match your background and apply directly to each listing without being redirected to separate employer portals.
See All 1,769+ Senior ML Engineer Jobs
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