Pay
In line with similar jobs
$170K–$210K against $154K–$225K, the middle half of what similar US postings state.
From 2,958 postings in the last year. Stated ranges, not salaries paid.
Manager, Machine Learning Engineering
Reads like a Data Leadership role: running a data team.
- Pay
USD 170,000–210,000/year — pay source
Salary 170,000 – 210,000 USD per year Our vision is to build a new financial ecosystem where everyone can participate on equal footing and access the tools they need to be financially healthy. We strongly believe that inclusion fosters innovation and we’re proud to have a diverse global team that represents a multitude of backgrounds, cultures, and experience. We hire talented people regardless of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.
Read the full posting- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingManage and develop a team of 4–6 Machine Learning Engineers
Hire and close ML engineering talent
Set quarterly goals and ensure the team delivers against them
Provide technical leadership on real-time inference and streaming ML systems
Own SLOs, on-call health, reliability, and incident response
From the employer’s posting
Manage and develop a team of 4–6 Machine Learning Engineers across mid-to-senior levels.
Hire, source, interview, and close strong MLE talent.
Set quarterly goals and ensure the team consistently delivers against them.
Provide technical leadership across model infrastructure, real-time inference, streaming feature extraction, batch processing, and production ML systems.
Own and improve SLOs, on-call health, capacity planning, reliability, and incident response.
What you’ll bring
All qualificationsCore experience
- 6+ years of backend software engineering in consumer-scale applications
- 2+ years directly managing engineers
- At least 3 years of hands-on Python
- Experience building and operating ML or causal inference systems in production
- Experience with production on-call, incident response, and capacity planning
Qualification wording
6+ years of backend software engineering experience in consumer-scale applications.
2+ years of directly managing engineers, including hiring, performance management, coaching, and career development.
At least 3 years of hands-on Python experience.
Experience building and operating machine learning or causal inference systems in production.
Experience with production on-call, incident response, and capacity planning.
About Tala
Most Talazens join us because they connect with our mission.
In the employer’s words · Read in context
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 19, 202647 days ago
- Recorded sightings
- 29
- Last seen by us
- Nov 5, 2026
- Employer says posted
- Sep 17, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 47
Similar jobs usually come down between day 7 and day 54.
At Tala, jobs usually come down by about day 28.
From 7,965 similar postings that came down, and 6 of Tala’s. Coming down is not always a hire.
Tala is hiring
3 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Takes jobs down quickly
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- Azure
- Docker
- GCP
- Kafka
- Kubernetes
- MySQL
- PostgreSQL
- Snowflake
- Spark
- Hugging Face
- pandas
- PyTorch
- scikit-learn
- TensorFlow
- XGBoost
Where the posting names them
- 6+ years of backend software engineering experience in consumer-scale applications. - At least 3 years of hands-on Python experience. - Experience building and operating machine learning or causal inference systems in production.
We’re particularly interested in candidates with experience across: - Languages: Python, SQL - Machine Learning: Jupyter, Pandas, Scikit-Learn, XGBoost, TensorFlow, PyTorch, Hugging Face
- Machine Learning: Jupyter, Pandas, Scikit-Learn, XGBoost, TensorFlow, PyTorch, Hugging Face - Cloud & Infrastructure: AWS, GCP, Azure, Kubernetes, Docker - Streaming: Kafka, Kinesis, Beam, Flink, Spark Streaming
- Cloud & Infrastructure: AWS, GCP, Azure, Kubernetes, Docker - Streaming: Kafka, Kinesis, Beam, Flink, Spark Streaming - Batch Processing: Airflow, Metaflow
- Batch Processing: Airflow, Metaflow - Databases: MySQL, PostgreSQL, Cassandra, Snowflake, Druid, and/or similar technologies - APIs: REST, GraphQL, gRPC, Protocol Buffers
- Languages: Python, SQL - Machine Learning: Jupyter, Pandas, Scikit-Learn, XGBoost, TensorFlow, PyTorch, Hugging Face - Cloud & Infrastructure: AWS, GCP, Azure, Kubernetes, Docker
Your next step
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Demand for Data Leadership roles
New Data Leadership postings at the employers we follow are up 7% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
US government figures for computer and information systems managers
- Median yearly pay, May 2025
- $175,140
- Expected change, 2025 to 2035
- 15.8% more jobsagainst 3.5% for all jobs
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
Job description
About Tala
Tala is the AI-native credit infrastructure that connects global capital to the global majority. We combine proprietary risk intelligence with an expanding network of capital and distribution partners to power credit access at scale. To date, Tala has distributed more than $9 billion in capital to more than 14 million customers across Africa, Latin America, and Asia, building the definitive contextual dataset on thin-file borrowers in emerging markets. Tala is now converting that foundation into shared infrastructure that partners, capital providers, and ecosystems can build on.
The company has been named to the Fortune Impact 20 list, CNBC’s World’s Top Fintech Companies twice, CNBC Disruptor 50 for five years, and Forbes’ Fintech 50 list for ten years running.
The company has been named to the Fortune Impact 20 list, CNBC’s World’s Top Fintech Companies twice, CNBC Disruptor 50 for five years, and Forbes’ Fintech 50 list for ten years running.
Given the global nature of our team, we operate on a remote-first approach with office hubs in Santa Monica, CA (HQ); Nairobi, Kenya; Mexico City, Mexico; Manila, the Philippines; and Bangalore, India.
Most Talazens join us because they connect with our mission. If you are energized by the impact you can make at Tala, we’d love to hear from you!
The Role
We’re looking for a Manager, Machine Learning Engineering to lead Tala’s ML Platform team. This person will manage a team of Machine Learning Engineers responsible for building the platforms, frameworks, and infrastructure that enable our Data Science teams to securely train, deploy, monitor, and operate machine learning models at scale.
This is a player-coach management role. You’ll be responsible for developing and growing the team while also providing enough technical leadership to guide architecture, engineering practices, reliability, and production systems. The role has a particular focus on real-time machine learning inference and streaming data systems, as well as the platforms that support batch model development and deployment.
What You'll Do
- Manage and develop a team of 4–6 Machine Learning Engineers across mid-to-senior levels.
- Hire, source, interview, and close strong MLE talent.
- Establish clear expectations, provide regular feedback, and create development plans for direct reports.
- Coach engineers toward growth and promotion while addressing performance gaps directly and thoughtfully.
- Create opportunities for engineers to take on challenging projects and grow their technical leadership.
- Set quarterly goals and ensure the team consistently delivers against them.
- Own prioritization across product roadmap work, run-the-business activities, and operational excellence.
- Balance team capacity across new development, maintenance, technical debt, and production support.
- Improve team productivity by reducing context switching and delegating effectively.
- Partner with engineers and technical leads to estimate and scope complex work.
- Guide the development of platforms and frameworks that allow Data Scientists and Analysts to explore data, develop features, and train, test, deploy, and monitor ML models.
- Provide technical leadership across model infrastructure, real-time inference, streaming feature extraction, batch processing, and production ML systems.
- Drive strong engineering practices around testing, automation, observability, fault tolerance, infrastructure-as-code, and deployment.
- Own and improve SLOs, on-call health, capacity planning, reliability, and incident response.
- Review technical designs and help drive architectural standards and technical debt reduction.
- Work closely with Data Science, Data Engineering, Data Platform, Product, Credit, and Business Development teams.
- Translate business and technical needs into scalable ML platform solutions.
- Coordinate dependencies and delivery across multiple engineering and data teams.
- Help create structure and clarity in an environment where priorities and requirements can evolve.
Lead & Grow the Team
Own Engineering Delivery
Provide Technical Leadership
Partner Across the Organization
What You'll Need
- 2+ years of directly managing engineers, including hiring, performance management, coaching, and career development.
- Experience managing a team through at least one full performance cycle.
- Demonstrated ability to coach engineers toward promotion and address underperformance effectively.
- Experience owning team goals, prioritization, estimation, and delivery.
- Experience with production on-call, incident response, and capacity planning.
- Willingness to be actively involved in sourcing, interviewing, and closing engineering talent.
- 6+ years of backend software engineering experience in consumer-scale applications.
- At least 3 years of hands-on Python experience.
- Experience building and operating machine learning or causal inference systems in production.
- Earlier-career experience personally building and deploying ML models or ML infrastructure.
- Ability to participate in technical architecture and system-design discussions and provide technical direction without needing to be the primary coder.
- Strong understanding of software quality, security, reliability, testing, and production operations.
- Languages: Python, SQL
- Machine Learning: Jupyter, Pandas, Scikit-Learn, XGBoost, TensorFlow, PyTorch, Hugging Face
- Cloud & Infrastructure: AWS, GCP, Azure, Kubernetes, Docker
- Streaming: Kafka, Kinesis, Beam, Flink, Spark Streaming
- Batch Processing: Airflow, Metaflow
- Databases: MySQL, PostgreSQL, Cassandra, Snowflake, Druid, and/or similar technologies
- APIs: REST, GraphQL, gRPC, Protocol Buffers
- Production Engineering: DevOps, SLOs, monitoring/observability, on-call, capacity planning, root-cause analysis
- ML/Analytics: Machine learning, causal inference, scalable algorithms
Management Experience
Technical Experience
Technical Skills
We’re particularly interested in candidates with experience across:
Salary
170,000 – 210,000 USD per year
Our vision is to build a new financial ecosystem where everyone can participate on equal footing and access the tools they need to be financially healthy. We strongly believe that inclusion fosters innovation and we’re proud to have a diverse global team that represents a multitude of backgrounds, cultures, and experience. We hire talented people regardless of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.
Employment type
Full-time
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Salary 170,000 – 210,000 USD per year Our vision is to build a new financial ecosystem where everyone can participate on equal footing and access the tools they need to be financially healthy. We strongly believe that inclusion fosters innovation and we’re proud to have a diverse global team that represents a multitude of backgrounds, cultures, and experience. We hire talented people regardless of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.
- Location & working pattern
US
The company has been named to the Fortune Impact 20 list, CNBC’s World’s Top Fintech Companies twice, CNBC Disruptor 50 for five years, and Forbes’ Fintech 50 list for ten years running. Given the global nature of our team, we operate on a remote-first approach with office hubs in Santa Monica, CA (HQ); Nairobi, Kenya; Mexico City, Mexico; Manila, the Philippines; and Bangalore, India. Most Talazens join us because they connect with our mission. If you are energized by the impact you can make at Tala, we’d love to hear from you!
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.